Ijraset september 2013(issn 2321 9653) by IJRASET

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Fixed Point Of Multivalued Mapping On Polish Space Yogita R. Sharma School of Studies in Mathematics, Vikram University, Ujjain, M.P., India, yogitasharma17@yahoo.com

Abstract: is proved.

A general fixed point theorem, that incorporates several known random fixed point theorems, involving continuous random operators,

Keywords: Fixed point; Multivalued mapping; Polish space. 1.INTRODUCTION Random fixed point theorems are of fundamental importance in probabilistic functional analysis. They are stochastic generalization of classical fixed point theorems and are required for the theory of random equations. In a separable metric space random fixed point theorems for contractive mapping were proved by Spacek [1], Hans [2, 3], Mukherjee [4]. Itoh [5, 6] extended several fixed point theorems, i.e., for contraction, nonexpensive, mappings to the random case. Thereafter, various stochastic aspects of Schauder’s fixed point theorem have been studied by Sehgal and Singh [7] and Lin [8]. Afterwards Beg and Shahzad [9], Badshah and Sayyad [10] studied the structure of common random fixed points and random coincidence points of a pair of compatible random operators and proved the random fixed points theorems for contraction random operators in polish spaces. 2. PRELIMINARIES Let (X, d) be a Polish space; that is a separable, complete metric space and (, a) be a measurable space. Let 2X be a family of all

be a random operator and  n  a sequence of measurable mappings  :   X . The sequence is said to be asymptotically T-regular, if n

d  n (), T (,  n ())  0.

3. THEOREM Let X be a Polish space. Let T , S :   X  CB X  be two continuous random multivalued operators. If there exist measurable mappings , ,  :   0,1 such that (1) minH S , x , T , y , d x, S , x d  y, T , y ,

d  y, T , y   mind x, T , y , d  y, S , x 

 d x, S , x   d x, y d  y, T , y  for each x, y  X , where  , ,  are real numbers such that 0       1 and there exists a

measurable sequence

 n  which is asymptotically

subsets of X and CB(X) denote the family of all non empty closed bounded subsets of X. A mapping T :   2 X , is called measurable, if for any open subset C of X, T 1 (C )  {   : T ()  C  }  a .A mapping  :   X ,

regular with respect to S and T ,then there exists a common random fixed point of S and T. (H is Hausdorff metric on induced by metric d )

is said to be measurable selector of a measurable mapping T :   2 X , if  is measurable and for any   , ()  T (). A

3.1 Proof. Let 0 :   X bne an arbitrary measurable mapping and choose a measurable mapping 1 :   X such that

mapping f :   X  X is called a random operator if for any x  X , f (. , X ) is measurable. A mapping T :   X  CB ( X ), is called random multivalued operator if for every x  X , T (. , X ) is

measurable. A measurable mapping  :   X , is called the random fixed point of a random multivalued operator T :   X  CB( X ) ( f :   X  X ), if for every   ,

()  T (, ()) ( f (, ())  ()). Let T :   X  CB( X )

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1   S ,  0  for each    . Then for each   

 H S ,  0 , T , 1 ,    min d  0 , S ,  0 d 1 , T , 1 ,  d  , T ,    1 1      mind  0 , T , 1 , d 1 , S ,  0 

 d  0 , S ,  0    d  0 , 1  d 1 ,T , 1 .

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) It further implies [lemma 2.3 of Beg (1993a)], that there exists a measurable mapping  :   X such that for any 2

   ,  2   T , 1  and for x   n 1  and y   n  by

condition (1), we have  H S ,  n1 , T ,  n ,    min d  n1 , S ,  n1 d  n , T ,  n ,  d  , T ,    n n  

 d (),  2 n  2 () 

 H (T (,  2 n 1 ()), S (, ())) ,

Or, d (), S (, ())   d (), S (, ())    d ,  2 n1   d   2 n1 , T ,  2 n1  ,

 n   S ,  n1  .

which tends to zero. So,  2 n1 is asymptotically T-regular.

We have mind  n ,  n1 , d  n1 ,  n d  n ,  n1 

 d  n1 ,  n   d  n1 ,  n d  n ,  n1 

Letting n   we have d (), S (, ())   0 . Hence, ()  S (, ()) for    .

and it follows that mind  n ,  n1 , d  n1 ,  n d  n ,  n1 

    d  n1 ,  n d  n ,  n1  Since d  n 1 ,  n d  n ,  n 1        d  n 1  ,  n  d  n  ,  n 1   is not possible (as 0         1 ).

Similarly, for

d (), T (, ())   d ,  2 n1   H S ,  2 n , T ,  d (), T (, ())   0 .

We have d  n ,  n1      d  n1 ,  n d  n ,  n1  Or d  n  ,  n 1    kd  n 1  ,  n  d  n  ,  n 1   where k     , 0  k 1

Hence, ()  T (, ()) for each    . 3.2 Corollary. Let X be a Polish space. Let T :   X  CB X  be a continuous random multivalued operator. If there exist measurable mappings , ,  :   0,1 such that minH T , x , T , y , d x, T , x d  y, T , y , d  y, T , y 

Similarly proceeding in the same way, by induction we produce a sequence of measurable mapping  n :   X such that for n  0 and any     2 n 1 ()  S ,  2 n () ,  2 n  2 ()  T ,  2 n 1 ()  d  n (),  n 1 ()   k d  n 1 (),  n ()   ...  k n d  o (), 1 () 

d  n (),  m ()   d  n (),  n1 ()   ...  d  m1 (),  m () 

 ...  k

d  n (),  m () 

References [1] A. Spacek, G. Zulfallige Czechoslovak Mathematical Journal (5) pp.462-466, 1955. [2] O. Hans, R. Zufallige, Transformationen. Czechoslovak Mathematics Journal (7) 154-158, 1957. [3] O.Hans, Random operator equations. Proceedings of the 4th Berkeley symposium in Mathematics and Statistical Probability, Vol. II: pp. 180-202, 1961.

 d  (),  () o

 d  x, T , x    d  x, y d  y, T , y , for each x, y  X and    where  , ,  are real numbers such

and converges to a random fixed point of T.

Further, for m > n,

 k  k

  mind  x, T , y , d  y, T , x 

that 0       1 then there exists a sequence  n  of measurable mappings  n :   X which asymptotically T-regular

and

m 1

sequence and there exists a measurable mapping :  X such that  n ()  () for each    . It implies that  2 n 1 ()  () and

 2 n 2 ()  () . Thus, we have for any    ,

Since d  , S ,    0 , n n 1

n 1

n   . It follows that  n () is a Cauchy

d (), S (, ())   d (),  2 n 2 ()   d  2 n 2 (), S (, ()) 

  mind  n1 , T ,  n , d  n , S ,  n1   d  n1 , S ,  n1    d  n1 ,  n  d  n , T ,  n 

which tends to zero as

 kn    d  o (), 1 ()  1  k 

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) [4] A. Mukherjee, Random transformations of Banach spaces. Ph. D. Dissertation. Wayne State University, Detroit, Michigan, USA.1968. [5] S. Itoh, Random fixed point theorem with applications to random differential equations in Banach spaces. Journal of Mathematical Analysis and Application (67) 261-273.1979. [6] V.M. Sehgal, & S.P. Singh, On random approximation and a random fixed point theorem for a set valued mappings. Proceedings of the American Mathematical Society (95) 9194, 1985. [7] T.C. Lin, Random approximations and random fixed point theorems for non-self maps. Proceedings of the American Mathematical Society (103) 1129-1135, 1988. [8] I. Beg, & N.Shahzad, 1993. Random fixed points of random multivalued operators on Polish spaces. Nonlinear Analysis 20: 835-847.

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[9] V. H. Badshah, & F. Sayyad, Random fixed points of random multivalued operators on Polish spaces. Kuwait Journal of Science and Engineering, (27) 203-208, 2000. [10] I. Beg, & N. Shahzad, Common random fixed points of random multivalued operators on metric spaces. Bollettino U.M.I. (7) 493-503, 1995. [11] D. Turkoglu, & B. Fisher, Fixed point of multivalued mapping in Uniform spaces. Proc.Indian Acad.Sci. (Math. Sci.) 113 (2): 183-187, 2003. [12] Y.R. Sharma, Fixed Point and Weak Commuting Mapping, International Journal of Research in Engineering and Technology, 2 (1), pp.1-4, 2013.

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A Review Paper On Firewall Dr. Ajit singh1, Madhu Pahal2, Neeraj Goyat3 Bpsmv.ajit@gmail.com1 , madhupahal@gmail.com2 , sbit.cse08426@gmail.com3 School Of Engineering And Sciences, Bhagat Phool Singh Mahila Vishwavidyalaya Sonipat (Haryana) 131001 India

Abstract : A firewall is a software that establishes a security perimeter whose main task is to block or restrict both incoming and outgoing information over a network. These firewalls are basically not effective and appropriate for corporate environments to maintain security of information while it supports the free exchange of views. In this paper, we study network firewall that helps the corporate environment as well as the other networks that want to exchange information over the network. A firewall protects the flow of traffic over internet and is less restrictive of outward and inward information and also provide internal user the illusion of anonymous FTP and www connectivity to internet. Keywords: Firewalls, gateways, packet filter, firewall configuration, working of application gateways



Introduction :

Computer networks are designed to connect two or more computers located at same or different corners in world. They are free to exchange information with any other computer. This kind of sharing is a great advantage for both individuals as well as for corporate world but as we know in today’s era, most important and confidential information is also exchanged on internet so attacker can do easily attack and can find out the important information and can harm the company in any manner. Most common type of attacks are :



To prevent our data from these dangers we must ensure some security mechanisms such that inside information remain inside and outside information remain outside and prevent outside attackers from entering in corporate network. One solution of this problem is the firewall. The main task of firewall is to regulate flow of information between computer network. It protects network by standing between network and the outside world. The data transfer in any direction must pass through the firewall.

3. Characteristics of Good Firewall : (a) Transfer of information either from inside to outside or from outside to inside must pass through the firewall. (b) The authorized traffic should be allowed to pass. (c) The firewall must be strong enough to prevent from attacks.

Truste d Netwo rk

Fir e wa ll

As corporation may have large amount of valuable data, leaking of which to competitors can do a great loss. There is also a danger from outside world such as viruses and worms, they can enter into corporate network.

(a) (b) (c) (d)

Types of Firewalls : There are different kinds of technique which may be implemented by a firewall. Some of them are as follows : Packet filter Application gateway Circuit level gateway Proxy server

3.1 Packet Filter : Untrus ted Networ k



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It looks at one packet at a time and then apply some set of rules to each packet and then decides to either forward the packet or discard the packet. The rules are based on a number of fields in the IP and TCP/UDP headers i.e. Source and

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Protected

Internet

Zone

Internal Networ Packet Filter k Fig. packet Filte

Solution: we can defeat the attacker by discarding all packets which has the same source address equal to internal address. (c) SOURCE ROUTING ATTACKS: Here attacker specify the route that is followed by the packet to move along the internet so that packet filter can be fooled to bypass its normal checks. Solution: the solution of this attack is discard all packets that use this option. Advantages: (a) It is Simple to implement. (b) Low hardware cost, cheap boxes can do packet filtering. (c) Rules set are less complex.

3.2 APPLICATION GATEWAYS :In order to control risks when internal server allow connections from internet we use a technique called application gateway, also known as proxy server because it acts like a substitute and decides about flow o f information.

HTT P

a) It receive each packet. Apply rules. b) If no rules, apply default rules.

TEL NET Inside Connection

SMT OutsideConnection P

Fig. Application Gateway FTP

Working of application gateways:

Fig. Packet Filter Operation

Attackers can break the security with the help of following techniques: (a) IP ADDRESS SPOOFING : In this type of attack, attacker send a packet to internal network, by setting source (b) Ip address equal to IP address of inside user.

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(1) An internal user make connection with application gateways i.e. HTTP, FTP. (2) An application gateway ask the internal user with which it want to communicate. (3) User then provide its id and password which is required to access services. (4) Now on behalf of user application gateway accesses the remote host. (5) After this application gateway acts like a proxy of actual user and delivers packet either from user to remote host or from host to end user.

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Real connection

HTTP

Real connection

SMTP FTP

4.1 SCREENED HOST FIREWALL, SINGLE HOMED

In this type of configuration a firewall consists of following parts :

TELN

BASTION :

user’s illusion

Internal Host

(i) (ii)

External Host

A packet filtering router An application gateway

Fig. Application gateway creates an illusion

Firewall configuration :

The main purpose of this type is as follows:

A firewall is a combination of packet filters and application gateways. Depending on this, following are the configurations of firewalls.



Firewall configurations 

Screene d host firewall , single homed bastion

Screened host firewall, Dual homed gateway Application bastion

Packet filter is used to ensure that incoming data is allowed only if it is destined for application gateway, by verifying the destination address field of incoming IP packet. It also perform the same task on outing data by checking the source address field of outgoing IP packet. Application gateway is used to perform authentication and proxy functions.

Screene d subnet firewall

INTERNE T

FTP SMTP HTTP TELNET

Internal network

Packet filter

Fig. Screened host firewall, single- homed bastion Here Internal users are connected to both application gateway as well as to packet filters therefore if packet filter is

Disadvantage :

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) successfully attacked then the whole Internal Network is opened to the attacker.

In this, direct connections between internal hosts and packet filter are avoided.

4.2 SCREENED HOST FIREWALL, DUAL HOMED BASTION :

As it provide connection between packet filter and application gateway, which has separate connection with the internal hosts.

To overcome the disadvantage of a screened host firewall, single homed bastion configuration , another configuration is available known as screened host firewall, Dual homed bastion.

Now if the packet filter is successfully attacked. Only application gateway is visible to attacker. It will provide security to internal hosts.

Internet

FTP SMTP HTTP TELNET Application gateway

packet filter

Internal network

Fig. screened host firewall, dual homed bastion

4.3 SCREENED SUBNET FIREWALL: It provides the highest security among all firewall configurations. It is improved version over all the available scheme of firewall configuration. It uses two packet filters, one between the internet and application gateway and another between the application gateway and the internal network.

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Internet

FTP SMTP HTTP TELNET Packet Filter

Application gateway

packet filter

Internal network

Fig. Screened subnet firewall



Limitations of firewall :

Till now as we discussed about all the security it provides to us and also a firewall is an extremely usefull security measure for any organization but at the same time it does not solve all the practical security problems. Its main limitations are as follows : (i)

(ii)

(iii)

Virus attacks: A firewall can not completely protect the internal network from virus threats because it can not scan every incoming packet for virus contents. Insider’s intrusion: A firewall is designed to protect insider from outside attacks but if an inside user attacks the internal network, the firewall cannot prevent from such type of attack. Direct internet traffic : a firewall is only effective if it is the only entry exit point of a network but if there exist more than one entry exit point from where attacker can exchange information firewall can not handle such type of situations carefully. Conclusion : As we have discussed so far that firewall is very important part of computer defense against viruses, spyware, Trojans and other malwares and also between direct malicious attacks from outside and outside of network. A good firewall is the one that provide full protection of network without effecting the speed of our computer and our network access. In order to provide security, one should keep following things in mind :

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 

We should never install any software from suspicious sources. Always download from the respected sites available on internet. Use a firewall to monitor all data or information that we want to exchange over the internet. On every computer a firewall software must be installed else it will only take one PC to become infected and very fast it will effect the all computers available on that network.

REFERNCES: http://books.google.co.in/books www.cs.ucdavis.edu/research http://gregorio.stanford.edu http://www.google.co.in/imgres?imgurl=http://computerclip arts.net http://www.milincorporated.com/a3-firewall-internetsecurity.html

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T )

Cyclic Prefix Based Channel Estimation of Single Carrier Frequency Domain Equalization Smrati Singh Sachan1, D r. A nil K umar S harma 2 1 2

IET Alwar,Rajasthan,India,smratisingh123@gmail.com IET Alwar Rajasthan,India,aks_826@yahoo.co.in

Abstract: For the mobile communication, single carrier cyclic prefixed (SC-CP) with frequency domain equalizer (FDE) is favourable and robust channel estimation. Conventional SC-CP wireless system uses training sequences that put in every packet to get the channel information. Channel State Information (CSI) is the crucial parameter that should be available at the receiver. Correct channel estimation is very important to the implementation of any communication system. In wireless communication system, data is often transmitted through unknown channel. This state of information is generally obtained at the receiver by sending training sequence from transmitter to receiver repeatedly and a training algorithm is performed by the receiver on the observed channel output and the known input to the channel. Since, training sequence is sent with the data frame, these solution results in loss of net throughput. A different way of obtaining CSI has been explored in this paper, making use of the transmitted information i.e. cyclic prefix itself and without adding any training symbols. This obviates the need for training sequences and results in overall increase in throughput. Keywords: Channel estimation, cyclic prefix, frequency domain equalization, single carrier, wireless.

1. INTRODUCTION The wireless communication channels are generally multipath fading channels which cause Inter Symbol Interference (ISI) in the received signal. To compensate for this channel induced ISI, various equalizer are in use. However, the information about the channel is highly desirable for proper functioning of equalizer. General Method of obtaining Channel information in Fig 2 shows a generic communication system which exploits channel estimation and signal detection operations in equalization. The digital source is usually protected by channel coding and interleaved against fading phenomenon, after which the binary signal is modulated and transmitted over multipath fading channel. Additive noise is added and the sum signal is received. Due to the multipath channel there is some inter symbol interference (ISI)

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in the received signal. For successful equalization, channel state information need to be known. Traditionally, the channel estimation is based on the known sequence of bits, which is unique for a certain transmitter and which is repeated in every transmission burst. Thus, the channel estimator is able to estimate CSI for each burst separately by exploiting the known transmitted bits at the receiver and the corresponding received samples. These known sequences are called training sequences or training symbols. Every time a frame is transmitted, these training symbols are transmitted and form the preamble part of the frame.

Training Data bits

Data

Figure 1 Training Sequences and data block

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) to -1.The last v symbols (from m-v to m-1) has been prefixed in the frame. The input to the receiver of SC- FDE can be expressed in the form of equation given by

y Hx Vn

Where x is the transmitted vector and y is the received vector and H is the Channel matrix. Now if we consider only the cyclic prefix part of the frame then

Figure.2 Generic wireless system with Channel Estimator

ycp Hxcp  Vn

Fig 2 shows that data bits from signal source are encoded, interleaved and modulated. After modulation, it passes through the Multipath channel. Due to the multipath channel there is some inter- symbol interference (ISI) in the received signal. Here CSI is estimated based on the known training sequence, which is transmitted in every transmission

However, there in one drawback of above system that it consumes bandwidth with every frame transmitted. Every time a frame is transmitted, a large space is occupied by the training symbols and hence the overall throughput reduces.

(2)

(3)

ycp X 0 h  Vn

Which is another way of expanding convolution, where h is of length l and Xo is matrix containing cyclic prefix symbols. Xo has dimensions of [v x l] where v is the cyclic prefix length and represented as

x  v 0



0

x  (v  1) x  (v  2 ) .. .. x  1   x  (v  x  (v  2 ) .. .. x  1  x  (v  2 ) .. .. x  1   : .. .. :  : .. .. :   0 .. x 1 

X 0 

2.1 Proposed Model for CSI in SC-FDE In case of SC-FDE, a frame has the structure as

 :  : 0  ..

0 : : 0

(4)

Now estimation of channel h can be described through equation

shown in fig 3 with every frame transmitted, a cyclic prefix is appended with every frame to maintain the periodicity in the signal which has advantage.

ˆ Where

-V

The above equation can also be expressed as

burst as Fig.1. The receiver can utilize the known training bits CSI typically for each burst separately.

2. PROPOSED WORK

(1)

hˆ

1

h X ycp

(5)

is the estimate of channel h and X-1 can be given as

X 1 

-1

X 0

X

1 T 0

(6)

In fig 3, the Cyclic Prefix (CP) is of length v from –v

Figure 3. cyclic prefix is of length v

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) This is the pseudo inverse matrix of Xo. In equation5, ycp is available to us at the receiver. Now in order to estimate the channel through equation 5, we need to know the entries in the matrix X-1 or basically the cyclic prefix symbols transmitted. Here, we will make use of equation 2 the estimate of x is given by

xˆ x H 1V

is duplicity in the initial and last symbols of the frame which is the only information available to us here.

3. SIMULATION RESULT

3.1

(7)

2.2 Block Diagram of Proposed System Here, we make use of the fact that the last v symbols of the frame are equal to the cyclic prefix part. That is true also since the last part of the frame has been prefixed at the input. In the received vector also, the last v symbols must be equal to first v symbols.

Flow Diagram

Channel estimation take palce when the last symbols of data block and prefixed symbols, both of these should be equal or the difference of them should be equal to zero. This process is repeated again and again until we get those values of h coefficients for which the first and last part of the frame are equal or their difference is minimum. Once we reach that stage, that will be the correct estimate of h and the frame can be equalized with these coefficients. In the next operation, when new frame arrives at the receiver, the recent value of h is updated

Figure 4 Block diagram of channel estimator in SC- FDE Initially, assumption is made that channel coefficients are available to us. Based on this, the received symbols are equalized. Once these symbols are equalized, the first v symbols are extracted and put in equation and channel is estimated. Now based on the new channel estimated, again the received symbols are equalized; the first v symbols are extracted and compared with cyclic prefix part. The last symbols of data block and prefixed symbols, both of these should be equal or the difference of them should be equal to zero. This process is repeated again and again until we get those values of h coefficients for which the first and last part of the frame are equal or their difference is minimum. Once we reach that stage, that will be the correct estimate of h and the frame can be equalized with these coefficients. In the next operation, when new frame arrives at the receiver, the recent value of h is updated.

Figure-5 Simulation Flow Diagram 3.2

Simulation Result

Here are results of the above explained algorithm for estimating the channel matrix. First column have the exact value of channel coefficient and other column have the estimated value of channel for repeating the loop different number of times.

This way, it is possible to obtain channel state information. We have made use of the fact that there

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Table 1

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) 3.3 Advantage of proposed System The proposed system does not require any additional bits in the form of training sequences to be sent with each frame. Therefore, high throughput can be expected here compared to the system when training symbols are sent along with the main frame. 3.4 Drawback of proposed system The drawback of the proposed system can be in the form of delay in decoding a frame. However, today highly computational devices are available that can be used for fast processing.

4. CONCLUSIONS & FUTURE SCOPE In CSI using cyclic prefix in SC-FDE: one system has been proposed for estimating the channel in SC- FDE that does not require any training symbols to obtain channel coefficients. Therefore, high throughput can be expected here compared to the traditional system like in GSM where training symbols are sent along with the main frame Some of the points regarding the future scope of the paper that can be expected, Includes: A basic strategy to obtain Channel state information using cyclic prefix has been proposed here in this paper. However, there is scope for using better algorithm while using the same basic principle proposed in this paper. Simulation for large frame size, different cyclic prefix length and long channel impulse response (v>L) can be explored further.

References [1] Zhiqiang Liu, “Maximum Diversity in Single-Carrier Frequency-Domain Equalization” IEEE Transactions on Information Theory, Vol. 51, no. 8, August 2005. [2] Lei Ye, Alister Burr, “Frequency Diversity Comparison of Coded SC-FDE &OFDM on Different Channels” The 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'07). [3] Fabrizio Pancaldi, Giorgio M. Vitetta, Reza Kalbasi, Naofal Al Dhahir , Murat Uysal and Hakam Mheidat “Single Carrier- Frequency Domain Equalization” IEEE Signal Processing Magazine Vol. 25, No. 5, September 2008. [4] Shankar, P.M “Introduction to wireless Systems”, pp. 299, John Wiley & Sons,2001

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[5] Xiaohui Zhang, Enqing Chen, and Xiaomin Mu “SingleCarrier Frequency- Domain Equalization Based on Frequency-Domain Oversampling” IEEE Communications Letters, Vol. 16, No. 1, January 2012. [6] T. Walzmanand M Schwartz “Automatic equalization using the discrete frequency domain ” IEEE Trans. Inform Theory, vol 19, no.1,pp 59-68, Jan 1973. [7] H. Sari, G. Karam, and I. Jeanclaude, “Transmission techniques for digital terrestrial tv broadcasting,” Communications Magazine, IEEE, vol. 33, no. 2, pp. 100–109, Feb 1995 [8] D. Falconer, S. L. Ariyavisitakul, A. Benyamin-Seeyar, and B. Eidson,“Frequency domain equalization for single-carrier broadband wireless systems,"IEEE communication Magazine, Vol. 40, no. 4, pp. 58-66, Apr 2002. [9] Z. Wang and G. B. Giannakis, “Wireless multicarrier communications: Where Fourier meets Shannon," IEEE Vehicular Technology Conference, Vol. 17, pp. 29-48, May 2000. [10] N. Al-Dhahir, “Single-carrier frequency- domain equalization for space-time block-coded transmissions over frequency-selective fading channels,” IEEE Commun. Letter, Vol. 5, no. 7, pp. 304–306, July 2001. [11] Lei Ye, Alister Burr, ”Frequency Diversity Comparison of Coded SC-FDE & OFDM on Different Channels”, The 18th Annual IEEE International Symposium on personal, Indoor and Mobile Radio Communications (PIMRC'07)”. [12] Ali Tajer, Aria Nosratinia, Naofal Al- Dhahir, “Diversity Analysis of Symbol- by-Symbol Linear Equalizers” IEEE transaction on Communication, Sep 2011. [13] G. Proakis, “Digital Communications”, New-York McGraw-Hill, 1989 [14] T. Rappaport, “Wireless Communications, Principle & Practice”, IEEE Press,Prentice Hall, pp. 31, 1996. [15] V. Erceg et al., “A Model for the Multipath Delay Profile of Fixed Wireless Channels, ”IEEE JSAC, vol. 17, no. 3, Mar. 1999, pp. 399–410. [16] H.Sari, G. Karam and I. Jeanclaude, “Frequency-Domain equalization of Mobile Radio and Terrestrial Broadcast Channels”, Proc. Globecom ’94, San Francisco,Nov.-Dec. 1994, pp. 1-5.

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Cyber Security in Indiaâ&#x20AC;&#x2122;sTourism Peeyush Vyas Vadodara Institute of Engineering, Kotambi, Halol Road, Vadodara, Gujarat. India. peeyushvyas@gmail.com

Abstract:

The growing use of ICT for administration of all the spheres of our daily life cannot be ignored. Also, we also cannot ignore the need to

secure the ICT infrastructures used for meeting social function like Tourism. Tourism is a very large industry in India. India has the fifth rank among countries with the fastest growing tourism industry according to the data given by World Travel and Tourism Council. Every year thousand of foreign tourists arrive in India mainly from Union Territories, Unites States and UK. Also, domestic tourists visit the states like Uttar Pradesh, Andhra Pradesh, Tamil Nadu, Himachal Pradesh, Jammu & Kashmir, Chennai, Delhi, Mumbai and many more places. So, the threat of Cyber Security in Indiaâ&#x20AC;&#x2122;s tourism has put an immense challenge during the last so many years. The threat attacks in resorts, hotels, banks, tourist offices and across all the major tourist places have become an inadequate mechanism to address this challenge. The roll of ICT (Information and Communication Technology) has exposed the users to a huge data bank of information regarding their bank a/c number, credit/debit card number, contact number or any type of personal information etc. and all these information are very much vulnerable if we talk about the use of computers and internet in the field of tourism and hence the probability of cyber attacks cannot be denied.

This paper is in regard with the understanding of the nature and effectiveness of cyber security and making an effort to address this challenge, highlighting what could be done. With the advanced development in the country and with the use of tools like email, online shopping, cell phones, satellite phones, on line ticket booking, online reservations etc. , cyber security has become an important issue related to the cyber tourism. The electronic gazettes like computers, laptops, mobiles etc. are being used as a weapon. It is generally unlawful attack and the related threats against the computers, networks & information stored, and cause enough harm to generate fear amongst the local as well as foreign tourists. Keywords: Tourism, Cyber, ICT, Security

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) 1.INTRODUCTION Cyber security can be described as the protection of systems, networks and data in cyber space. It is concerned with the protection against cyber risks, which broadly fall into three areas: Cyber Crime, Cyber War, and Cyber Terror. The growing use of ICT for administration of all the spheres of our daily life cannot be ignored. Further, we also cannot ignore the need to secure the ICT infrastructures used for meeting these social functions. Cyber security is in fact protecting our personal information or any kind of digital asset stored in computer or in any digital storage device. Here, it is a small try to elaborate cyber security in the area of India’s tourism. Tourism is a very large industry in India. India has the fifth rank among countries with the fastest growing tourism industry according to the data given by World Travel and Tourism Council. Every year thousand of foreign tourists arrive in India mainly from Union Territories, Unites States and UK. Also, domestic tourists visit the states like Uttar Pradesh, Andhra Pradesh, Tamil Nadu, Himachal Pradesh, Jammu & Kashmir, Chennai, Delhi, Mumbai and many more places. The threat of Cyber Security in India’s tourism has put an immense challenge during the last so many years. The threat attacks in major tourist places, resorts, hotels and across all the major tourist places have become an inadequate mechanism to address this challenge. The roll of ICT (Information and Communication Technology) has exposed the users to a huge data bank of information regarding their bank a/c number, credit/debit card number, contact number etc. and all these information are very much vulnerable if we talk about tourism and hence the probability of cyber attacks cannot be denied. Reports suggest that cyber attacks are understandably directed toward tourism department as there is a great involvement of Indian as well as foreign currencies and hence it can collapse the Indian economy. This paper is in regard with the understanding of the nature and effectiveness of cyber security and making an effort to address this challenge, highlighting what could be done. The structure of the paper will be as follows:

system in order to introduce delay or make the system unpredictable. Computer viruses and Trojans are used in this type of attack whereas in Semantic Attack the information keyed in the system during entering and exiting the system is modified without the users knowledge in order to induce errors. Cyber tourism is not only limited to paralyzing computer infrastructures but it has gone far beyond that. It is also the use of computers, Internet and information gateways - to support the traditional forms of tourism like suicide bombings. Internet and email can be used for organizing a terrorist attack also. Most common usage of Internet is by designing and uploading websites on which false propaganda can be pasted. This comes under the category of using technology for psychological warfare. Tools of Cyber Tourism. Cyber criminals use certain tools and methods to allow to run freely this new age of tourism. These are :(a) Hacking:-This is the most popular method used by a terrorist. It is a generic term used for any kind of unauthorized access to a computer or a network of computers. Password cracking, packet sniffing etc are the tools of hacking. (b) Trojans:-These are the programmes which pretend to do one thing while actually the are meant for doing something different. (c) Computer Viruses:- These are computer programmes, which infects other computer, programmes by modifying them. They spread very fast. (d) Computer Worms:-They are a set of programmes that is able to spread functional copies of itself usually via network connections. (e) E-Mail Related Crime:- Certain emails are used as host by viruses and worms. E-mails are also used for spreading disinformation, threats and defamatory stuff. (f) Denial of Service:- These attacks are aimed at denying authorized persons access to a computer or computer network. (g) Cryptology:- Cyber Criminals have started using encryption, high frequency encrypted voice/data links etc.

Cyber Tourism Challenges to National Security: In general, 'Cyber tourism’ is the combination of tourism and cyber space. With the advanced development in the country and with the use of tools like email, online shopping, cell phones, satellite phones, on line ticket booking, online reservations etc. , cyber security has become an important issue related to the cyber tourism. The electronic gazettes like computers, laptops etc. are being used like a weapon. It is generally unlawful attack and threats against the computers, networks and information stored, and cause enough harm to generate fear amongst the local as well as foreign tourists. Methods of Attack.

India has already made its position in the world related to the IT sector and has started in the working environment of e-governance and handling the tasks of ‘Passport’, ‘Visa’ and many more. The sector of tourism is hence highly reliable on the e-governance. Also, the concepts of e-commerce and e-banking have been brought in the new challenging areas. Indian tourism is highly based on these above mentioned factors. To paralyze the tourism segment all the possible mentioned threats are the delicate issues. Existing Cyber Security Initiatives:

The Computer viruses, worms, hacking and fishing are frequently used as a weapon in the tourism industry . The attacks or methods on the computer system can be Syntactic or Semantic. In the Syntactic Attack the computer infrastructure is damaged by modifying the logic of the

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The various government organizations working for cyber security are as under National Informatics Centre (NIC):-

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Security

Assurance

Programme

 Joint efforts by all Government agencies including defence forces to attract qualified skilled  personnel for implementation of counter measures.  Cyber security not to be given more lip service and the organizations dealing with the same should be given all support.  Agreements relating to cyber security of India’s tourism should be given the same importance as other traditional agreements.  There should be more investment in the field of cyber security of tourism in terms of finance and manpower.  Here should be a close eye on the developments in the IT sector by the Indian agencies working together in this direction. Conclusions.

NISAP is mainly for Government as well as typical infrastructure. Under this programme, it is mandatory for organizations to implement security control and inform to Cert-In if there is any security issue. Cert-In creates a panel which audits to the organizations. Indo-US Cyber Security Forum (IUSCSF):Indo-US Cyber Security Forum was established in April 2002 as a step to intensify the on-going cooperation to address national security issues arising from the increasing interdependency of our critical network information systems involved in outsourced business processing, knowledge management, software development and enhanced inter-government interaction. The group is mandated to cooperate on policy, procedural, and technical issues of cyber security interest to both nations. Challenges and Concerns : Tracking of information available on the net has always been a tedious and a challenging task and looking to the present scenario of cyber traffic we can highlight the following concerns and challenges(a) Lack of general awareness of cyber security in tourism department at individual as well as departmental level. (b) Lack of trained and qualified staff. (c) A weak IT ACT with obsolete cyber laws. (d) Non availability of email policies especially for defense, police and agency personnel. (e) Cyber attacks from neighboring countries as well as criminals or rather educated criminals. (f) Too many tourism organizations which have become vulnerable due to 'turf wars'. Recommendations. Certain recommendations are given below: Need to sensitize the common citizens about the dangers of cyber tourism.  Cert-in should engage academic institutions and follow an aggressive strategy.

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Google Nexus is a line of mobile devices using the Android operating system produced by Google in conjunction with several manufacturers. It is a series of smart phones and tablets manufactured by Google and its hardware partners and there is a growing nexus between the hacker and the tourists. The time will come when all the smart people associated with the tourism segment will be the good hackers. That will change the entire landscape of tourism. A common vision is required to ensure cyber security and prevent cyber crimes in the India’s tourism. The time has come to prioritize cyber security in India's counter tourism strategy. References [1] Asian School Of Cyber Laws [2] http://law.jrank.org/pages/11992/CyberCrimeIntellectual-property-theft.html [3] http://zh.scribd.com/doc/45792947/CyberCrime-ppt [4]http://www.virtualpune.com/citizencentre/html/cyber_crime_glos sary.shtml [5]http://zh.scribd.com/doc/60851869/13/Data-Diddling [6]http://www.fotosearch.com [7]http://articles.timesofindia.indiatimes.com/2012-0716/ahmedabad

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Direct torque control of three Phase induction motor using matlab Narender Kumar1, Mr. Joginder Singh2 1

Ganga Institute Of Technology and Management, Maharshi Dayanand University, Kablana, Jhajjar, India narendermlk82@gmail.com

Ganga Institute Of Technology and Management, Maharshi Dayanand University, Kablana, Jhajjar, India jogiparas@gmail.com

Abstract: Induction machines are widely employed in industries due to their rugged structure, high maintainability and economy than DC motors. There has been constant development in the induction motor drive system and their implementation in industrial applications. The improvement of switching speed of power electronic devices has enabled control techniques which possess high switching frequency and feasibility of high efficiency drive systems. In this pretext, Direct Torque Control (DTC) was introduced to obtain quick and better dynamic torque response. The DTC scheme in its basic configuration comprises torque and flux estimator DTC controller, stator voltage vector selector and voltage source inverter. Direct Torque Control of induction motor has increasingly become the best alternative to Field- Oriented Control methods. The performance of an induction motor under the classical Direct Torque Control method and improved scheme have been studied and confirmed by simulation using MATLAB .

Keywords: Digital signal processor, direct field oriented control, direct signal processor, direct torque control, pulse-width modulation, application specific Integrated Circuit (ASIC)

constant speed. Induction machines have simpler and more rugged structure, higher maintainability and economy than dc motors.

1. Introduction Industrial loads require operation at wide range of speeds. Such loads are generally termed as variable speed drives. These drives demand precise adjustment of speed in a steeples manner over the complete speed range required. The loads may be constant torque or a function of speed. These loads are driven by hydraulic, pneumatic or electric motors. An industrial drive has some special features when driven by electric motors. Induction machines have provided the most common form of electromechanical drive for industrial, commercial and domestic applications that can operate at essentially

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They are also robust and immune to heavy loading. The possible forms of drive motors are dc drives, ac drives. DC motors are versatile for the purpose of speed control but they suffer from the disadvantage imposed by the commentator. On the other hand ac drives are viable competitors with the advent of thruster power converter technology. The evolution of ac variable speed drive technology has been partly driven by the desire to emulate the performance of dc drive such as fast torque response and speed accuracy, while utilising the advantages offered by standard ac motor.The Field Oriented Control (FOC) and the Direct Torque

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) Control (DTC) are two types of drives employed for high performance applications. Direct Torque Control was introduced in Japan by Takahashi (1984) and Depenbrock (1985). Vector controlled induction motors are employed in high performance drives having precise speed control and good static as well as dynamic response. Direct Torque Controlled drives have increasingly become the best alternative to Field-Oriented Control methods [10], [2]. Modern control methods use state space techniques. The method of stabilizing the drives and improvement in their transient responses have been realized by modern power electronic devices [3]. The block diagram of Direct Torque Control for an induction motor is as shown in Fig. 1. The DTC scheme comprises torque and flux estimator, hysteresis controllers for flux and torque and a switching table.

Fig. 1. Block diagram of classical DTC scheme

Direct torque control has its roots in field-oriented control and direct self control. Field-oriented control uses spatial vector theory to optimally control magnetic field orientation. It has been successfully applied to the design of flux vector controls and is well documented. Direct self-control theory is less well known. The fundamental premise of direct self control is as follows. Given a specific dc-link voltage (Edc) and a specific stator flux level (ď &#x2122;ref), a unique frequency of inverter operation is established. This is true because the time (T) required by the time integral of the voltage (Edc) to integrate up to the field flux level (ď &#x2122;ref) is unique and represents the half-period time of the frequency of operation. Since the operational frequency is established without a frequency reference, this operational mode is referred to as direct self control [1]. Output frequency is, thus, not requested, but rather, is selfcontrolled via the actual frequencies present. Once sensed, whether the frequency increases or decreases depends on what the torque reference from the speed regulator requests. Differential changes to operational frequency are determined by the torque request. Direct torque control combines field-oriented control theory, direct selfcontrol theory, and recent advances in digital signal processor Fig.2.VSI Switching Position (DSP) and application specific Integrated Circuit (ASIC) Technology to achieve a practical sensor less variable frequency drive.

+ A

S5 1

SUPPLY

INDUCTION MOTOR MODEL

S6 0

The main objective of DTC is to control the induction motor. The per-phase equivalent circuit of an induction motor is valid only in steady-state condition. In an adjustable speed drive like the DTC drive, the machine normally constitutes an element within a feedback loop and hence its transient behavior has to be taken into consideration [4]. The induction motor can be considered to be a transformer with short circuited and moving secondary. The coupling coefficients between the stator and rotor phases change continuously in the course of rotation of rotor [6], [5]. Hence the machine model can be described by differential equations with timevarying mutual inductances.

2. Literature review DIRECT TORQUE CONTROL CONCEPT

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S2 0

C 0

VSI Switching position For a sixpulse VSI, according to its switch positions (S1 to S6), there are six non-zero active voltage space vectors (V1, V2, V3, V4,V5 and V6) and two zero voltage space vectors (V7 and V8) as shown in fig 3 One switch per leg of the VSI conduct at any time, i.e., if S1 is ON then S4 is OFF; 1 represents the ON state of a upper switch of a leg and 0 represents the ON state of the lower switch of the same leg. The stator flux linkage vector will move fast if non-zero switching vectors are applied and for a zero switching vector it will almost stop (it will move very slowly due to the small ohmic voltage drop.)

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) increase stator flux while simultaneously reducing the angle between the stator and rotor flux vectors.

Fig.3 Voltage Switching Vectors

Corresponding to all possible combinations of switching states, active (non-zero) switching-voltage space vectors are shown in fig 3. By applying suitable space vector, changes in flux and torque demand can be met. If stator flux (s ) lies in sector 1 and if a reduced stator flux linkage space vector modulus is required, the modulus is controlled by applying switching voltage vector which are directed towards the center. Here FD, F1, TD, T1 represent flux decrease, flux increase, torque decrease and torque increase respectively

Torque is controlled by varying the angle between the stator flux vector and the rotor flux vector. This method is feasible because the rotor time constant is much larger than the stator time constant in reality, there are only six active voltage vectors and two zerovoltage vectors that a voltage-source inverter can produce. The analysis performed by the optimal switching logic is based on the mathematical spatial vector relationships of stator flux, rotor flux, stator current, and stator voltage. Thus, rotor flux is relatively stable and changes quite slowly, compared to stator flux. When an increase in torque is required, the optimal switching logic selects a stator voltage vector (Us) that develops a tangential pull on the stator flux vector (s), tending to rotate it counterclockwise with respect to the rotor flux vector (r). The enlarged angle created effectively increases the torque produced. When a decrease in torque is required, the optimal switching logic selects a zero-voltage vector, which allows both stator flux and produced torque to decay naturally. If stator flux decays below its normal lower limit the flux status output will again request an increase in stator flux. If the torque status output is still low, a new stator voltage vector (Us) is selected that tends to

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Fig.4.Trajectory of stator flux vector

Note that the combination of the hysteresis control block (torque and flux comparators) and the ASIC control block (Optimal switching logic) eliminate the need for a traditional PWM modulator. This provides two benefits. First, small signal delays associated with the modulator are eliminated and second, the discrete constant carrier frequencies used by the modulator are no longer present[4]. 3. DIRECT TORQUE CONTORL DRIVE AND MODELING OF INDUCTION MOTOR Torque Reference Controller-Within the Torque Reference Controller, the speed control output is limited by the torque limits and DC bus voltage. It also includes speed control for cases when an external torque signal is used. The internal torque reference from this block is fed to the torque comparator.

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) The model and control blocks used to arrive at the firing signals for the VSI are described below:

Fig.5. Complete Direct Torque Control Drive.

Fig. 6 DTC Model Speed Controller- the Speed Controller block consists both of a PID controller and an acceleration compensator. The external speed reference signal is compared to the actual speed produced in the motor model. The error signal is then fed to both the PID controller and the acceleration compensator. The output is the sum of outputs from both of them. Flux Reference Controller- An absolute value of stator flux can be given from the flux reference controller to the flux comparator block. The ability to control and modify this absolute value provides an easy way to realize many inverter functions such as flux optimization and flux braking.

3.3 FLUX ESTIMATOR Flux estimation is done using following equation s_est= √ ds2 +  qs2

3.4 TORQUE ESTIMATOR Torque estimations is done using following equation Test = (3/2) (P/2) (ds Iqs – qs Ids )

3.1 MODELING Induction motor can be represented by the following equations in “dq0” format, in arbitrary reference frame, which is rotating at an angular speed in the direction of rotation of the rotor:

For electromagnetic torque calculation following equations is used:

Speed estimation is done using open loop estimator governed by following equation: ωr_est = [dr ( dqr /dt) – qr (ddr / dt ) ][1/( dr2 + qr2)] – [(Lm / Tr) (dr Iqs – qr Ids )] [ 1/( dr2 + qr2)]

Tem = (3/2)(p/2)(Lm / Lr ) (dr Iqs– qr Ids) (19) Tem – Tload = J (dωr / dt)

3.5 SPEED ESTIMATOR

3.6 HYSTERESIS CONTROLLER

(20)

For motoring operation load torque is positive and for generating mode of operation load torque is negative.

The speed regulator which generates the reference torque signal is compared against estimated torque and torque error is calculated in hysteresis comparator block. Similarly, flux error signal is also calculated This is done using two level flux comparator and three level torque comparator using following consideration: d=I if │s│ │s ref │ – │ ∆s│

3.2 MODELING OF FUNCTIONAL BLOCKS

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) d=0 if │s│ ≥│s ref │ + │∆s│

Sector 1: (ds ≥qs ) & (ds ≥ 0)

and

Sector 2: (ds< qs ) & (ds ≥ 0)

dte =1 if │te│  │te_ref │- │∆te│

Sector 3: (│ds│< qs ) & (ds < 0)

dte = 0 if │te│≥ te_ ref dte = -1 if│te│  │ te_ref │ + │∆te│

Sector 4: (│ds │≥ qs ) & (ds< 0) (│ds│<│qs│ ) & (ds< 0) & (qs< 0)

dte = 0 if │te │  te_ ref

Sector 6: (ds<│qs│ ) & (ds≥ 0) & ( qs< 0)

Sector 5:

The voltage source inverter is modeled in form of S-function block, contained in voltage vector selection block . It is written in form of an M-file, which gives the output of the inverter in terms of three phase voltages as per the signals received from optimum pulse selector. The M-file is given in appendix B. In fact the inverter output voltages are reconstructed using dc link voltage.

Thus the digital output of flux comparator is 1,0 and that of torque comparator is 1,0,-1.

Depending on the flux and torque comparator outputs, switching vector is selected as per following look up table:

Sect.

 T

Sector3

Sector2 Sector1

Sector4 Sector6 Sector5 T

Fig. 7 Voltage Sectors

4. SIMULATION RESULTS The model which is developed was simulated for an induction motor of rating 149.2 KW (machine parameters are given in appendix A) Simulation results in form of computer traces of electromagnetic torque developed, estimated torque , estimated and actual speed, stator flux and stator current have been depicted in this chapter.

Table :- 1.1

V1, V2, V3, V4, V5 and V6 are active voltage switching vectors, V7 and V8 are zero voltage switching vectors. Optimum voltage vector selection is done using S-Function block contained in voltage vector selection block. This essentially constructs phase voltages using DC link voltage . The model also calculates sector (as shown in Table 1.1) in which the stator flux linkage space vector is lying as per following relations.

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Simulation is done for following case: Condition :- The motor was started at no load with a set speed of 150 rad/sec and at t=0.5 sec a load torque of 5 Nm.(65 % of rated torque) was applied .

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) with literature. It was also observed that for motoring operation, the performance was best in terms of starting time, overshoot and undershoot.. 6.REFERENCES [1] H. Kubota and K. Matsuse, “Speed sensorless field-oriented control of induction motor with rotor resistance adaptation, ” IEEE Trans.Ind. Applicat., vol.30,pp.1219-1224,Sept./Oct.1994. [2] J.Maes and J. Melkebeek,“ Speed Sensorless direct torque control of induction Motor using and adaptive flux observer,” IEEE Trans. Ind. Applicat., vol. 36, pp. 33-37, May/June 2000. [3] Bimal K. Bose, “ Modern Power Electronics and AC Drives” Ion Boldea, S.A. Nasar,” Electric Drives”. [4] I. Takahashi and T. Noguchi, “A new quickresponse and high efficiency control strategy of an induction machine,” IEEE Trans. Ind. Appl., vol. IA–22, pp.820–827, Sep./Oct. 1986.

A PI controller is employed in the speed loop to make the steady state error in speed zero. The values of Kp and Ki are changed to obtain plots showing the dependence of the Simulation result of Stator current , Rotor speed , Electromagnetic torque and DC bus voltage performance.

Estimated Flux remains the same in all the cases a shown in Fig. From then plots it is seen that estimated torque and electromagnetic torque are close to each other. Also actual and estimated speed plots are close to each other. By increasing the value of Kp, the pulsations in the electromagnetic torque increase. 5. CONCLUSION Direct torque control combines the benefit of direct flux and torque control into sensor less variable frequency drive that does not require a PWM modulator. Recent advances in digital signal processor and application specific integrated circuit and the theoretical concepts developed so far for direct self control makes this possible. The objective of the present work was to make a model of direct torque control of three phase induction motor .Various speed control schemes were studied and extensive literature survey was carried out for understanding the direct torque control technique. MATLAB/SIMULINK was chosen as modeling and simulation tool because of its versatility. Model for direct torque controlled induction motor was developed using MATLAB/SIMULINK and performance of the system for different operating condition like starting, load changes, speed reversal, effect of changing the values of Kp and Ki on the performance characteristics, was studied. The model was validated by comparing the plots of various performance parameters with those available

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[5] U. Baader, M. Depenbrock, and G. Gierse, “Direct self control (DSC) of inverter-fedinduction machine—A basis for speed control without speed measurement,” IEEE Trans. Ind. Appl., vol. 28, pp. 581–588, May/Jun. 1992. [6] C. Lascu, Son Boldea, “A Modified Direct Torque control for induction Motor Sensorless Drive,” IEEE Trans. Ind. Applicat., vol. 36, Jan/Feb 2000.

[7] M. Depenbrock, “Direct self control of inverter-fed induction machines,” IEEE Trans. Power Electron., vol. 3, pp. 420–429, Oct. 1988. [8] Mario Marchesoni, Paolo Seagrich and Ernesto soressi, “ A simple Approach to flux and speed observation in induction motor Drives”. IEEE Trans. Ind. Elect. vol.44 pp. 45, May/June 1997. [9] C. French and P. Acarnley, “Direct torque control of permanent magnet drives,” IEEE Trans. Ind. Appl., vol. IA–32, pp. 1080–1088, Sep./Oct. 1996. [10] J.N. Nash, “ Direct torque control, induction Motor vector control without encoder,” IEEE Trans. Ind. Applicat. vol.33, pp. 24, Mar/Apr 1997. [11] L. Zhong, M. F. Rahman, W. Y. Hu, and K. W. Lim, “Analysis of direct torque control in permanent magnet synchronous motor

Vol. 1 Issue II, September2013 ISSN: 2321-9653

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T ) drives,” IEEE Trans. Power Electron., vol. 12, pp. 528–536, May 1997. [12] D. Casadei, G. Serra, and A. Tani, “Implementation of a direct torque control algorithm for induction motors based on discrete space vector modulation,” IEEE Trans. Power Electron., vol. 15, pp. 769–777, Jul. 2000 [13] C. G. Mei, S. K. Panda, J. X. Xu, and K. W. Lim, “Direct torque control of induction motor-variable switching sectors,” in Proc. IEEEPEDS Annu. Meeting, Hong Kong, Jul. 1999, pp. 80–85 [14] A.Tripathi, A.M.Khambadkone,and S.K.Panda,“Space-vectorbased, constant frequency, direct torque control and dead beat stator flux control of ac machines,” in Proc. IEEEIECON Annu. Meeting, Nov. 2001, pp. 1219– 1224. [15] C. Lascu, I. Boldea, and F. Blaabjerg, “A modified direct torque control for induction motor sensorless drive,” IEEE Trans. Ind. Appl., vol. 36, pp. 122–130, Jan./Feb. 2000. [16] L. Tang , L. Zhong, M. F. Rahman, and Y. Hu “A novel direct torque control for interior permanent-magnet synchronous machine drive with low ripple in torque and flux-a speedsensorless approach,” in Proc. IEEE Trans. Ind. Appl., vol. 39, pp. 1748–1756, Nov./Dec. 2003. [17] C. Martins, X. Roboam, T. A. Meynard, and A. S. Caryalho, “Switching frequency imposition and ripple reduction in DTC drives by using a multilevel converter,” IEEE Trans. Power Electron., vol. 17, pp. 286–297, Mar. 2002. [18] Y. A. Chapuis, D. Roye, and J. Davoine, “Principles and implementation of direct torque control by stator flux orientation of an induction vol. 1, 1995, pp. 185–191. [19] M. R. Zolghadri and D. Roye, “A fully digital sensorless direct torque control system for synchronous machine,” Elect. Mach. Power Syst., vol. 26, pp. 709–721, 1998.

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INTERNATIONAL JOURNAL FOR RESEARCH IN APPLIED SCIENCE AND E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T)

Future MOSFET Devices using high-k (TiO2) dielectric Prerna Guru Jambheshwar University, G.J.U.S. & T., Hisar, Haryana, India, prernaa.29@gmail.com

Abstract: In this paper, an 80nm NMOS with high-k (TiO2) was designed and fabricated to study its electrical characteristics. ATHENA & ATLAS module of SILVACO software are used in simulating the electrical performance of the transistor. The parameters under simulation were the threshold voltage (Vt), Id-Vg & Id-Vd Characteristics. High-k gate technology is a strong alternative for replacing the conventional SiO2 gates in MOSFETs for both high performance and low power applications. High-k oxides offer a solution to leakage problems that occurs as the gate oxide thickness is scaled down. Non-ideal effects such as short channel effects mainly channel modulation and drain induced barrier lowering (DIBL) are investigated in it. It is observed in the results that the threshold voltage could be varied by changing the above mentioned device parameters. The effectiveness is also observed on performance parameters of the MOSFET such as drain induced barrier lowering, sub-threshold slope and threshold voltage. Hence device engineering would play an important role in optimizing the device parameters. Keywords: MOSFET, SCE-short channel effect, High-k, DIBL-drain induced barrier lowering. 1. INTRODUCTION Since the advent of MOS devices over 40 years ago, SiO2 has been used as an efficient gate dielectric. The need for increased speed at constant power density has led to shrinking of MOSFET dimensions and as per scaling rules; the oxide thickness is also reduced in step. With scaling reaching sub nanometer technology nodes, the introduction of novel materials became inevitable as scaling of SiO2 raises a serious concern in terms of tunneling current and oxide breakdown [7]. In order to prevent direct gate tunneling in very thin oxides, the SiO2 is replaced by alternative materials with higher permittivity and greater physical thickness. However, the introduction of these high-k dielectrics posses several problems, such as bi-dimensional electrostatic effects which may have a dramatic impact on the device performances when the gate dielectric thickness becomes comparable to the device gate length [7]. In this paper section 1.1 contains a description about High-k dielectric (TiO2) while section 2 describes the design and simulation of N-channel MOSFET device with all the results and analysis discussed in section 3. 1.1

Titanium Dioxide (TiO2) as gate dielectric.

TiO2 has been used as an alternative gate dielectric material for deep submicron MOSFET’s earlier in 1995 [2]. Advantages: The dielectric constant of TiO2 is 80. The bandgap of the material is 3.5eV for amorphous films and 3.2eV for crystalline films. These band gaps are good for semiconductor but higher bandgap is required to act as an effective insulator [2].

due to oxygen vacancy. An empirical relationship between the effective mobility and the interface state density which is given by [2], μ μ

(1)

∝

where Dit is the concentration of charged states at the bias condition and α is a constant. So the effective mobility is inversely related with Dit [2]. In Si/SiO2 interface bond strain causes fixed charge which is about 0.1% of the interface atoms (1010-1011cm2). So for strained-Si/TiO2 leakage occurs from these defects as well as from low conduction band discontinuity. Therefore, ultra-thin SiO2 can be incorporated between TiO2 and strained-Si layer to reduce the defect states at the interface. Therefore if TiO2 is grown on (100) Si substrate Dit decreases and the mobility increases [2]. Mobility also can be increased by growing TiO2 gate dielectric stack on Si substrate. The device speed can be improved by 20-80% at a constant gate length by using high mobility strained-Si at the channel region. TiO2 reduces gate leakage and Si enhances the device speed [2]. Hence TiO2 is our choice of high-k dielectric gate material. The other high-k materials are shown in table-1 [2] with their properties along with TiO2. Table 1: High-k dielectric materials and their properties. Gate Dielectric Energy Conduction Valence dielectric

constant

bandgap

band offset

band

Material

(k)

Eg (eV)

∆Ec (eV)

offset

The TiO2 has low energy band offset with respect to Si. TiO2 has EOT of less than 10Å. Transistors made with TiO2 shows near ideal behavior but they have challenges with mobility. It has been shown that the low field effective mobility is approximately 160cm2/V-s, which is about a three order lower than the mobility in SiO2 based MOSFET’s. This mobility reduction is due to interface trap state and surface roughness at TiO2/Si interface. The electron traps in TiO2 is

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∆Ec(eV) SiO2

3.9

3.5

4.4

Al2O3

8.8

4.7

TiO2

3.5

1.1

1.3

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INTERNATIONAL JOURNAL FOR RESEARCH IN APPLIED SCIENCE AND E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) ZrO2

5.8

1.4

3.3

HfO2

5.8

1.4

3.3

Ta2O5

1.5

3.4

Y2O3

2.3

2.6

Ya2O3

4.3

2.3

0.9

The complete structure now can be simulated in ATLAS to provide specific characteristics such as Id-Vg & Id-Vd curve. The simulated device structure (figure 1) is a symmetric N-channel NMOS with following parameters mentioned in table 3.

2. DESIGN & SIMULATION Simulations are performed with a two-dimensional (2-D) device simulator, SILVACO. The physical structure of the high-k NMOS used in our present study are designed using ATHENA considering the standard Silicon Integrated chip processing technology and the electrical characteristics are simulated using ATLAS device simulator. The specifications of the Silicon substrate considered for the design are p-type Boron doped substrate with doping concentration of 1 x 1018 atoms cm-3 and <100> orientation. The design structure consists of TiO2 dielectric with Polysilicon gate is considered to explore the advantages of TiO2 over SiO2 dielectric. The simulated structure, which are based on fully scaled 80 nm gate length MOSFET’s proposed in the ITRS, have gate length of 80 nm, with effective oxide thicknesses (EOT) of 2nm [4]. The dielectric constant of TiO2 gate dielectric was considered to be 80. Steep retrograde channel doping is used with surface doping concentration of 8 x 1013 cm-3. The complete summary of NMOS process flow is given below in table 2.

Source/Drain implant

Final Rapid Thermal (RTA)

Table 3: Device parameters taken for process simulation of device design using ATHENA simulation tool. PARAMETERS NMOS 1624.31 Sheet Resistance (Ω/square) Channel Surface Concentration (atoms/cm3) Gate oxide thickness,

Table 2: Summary of NMOS design features process flow. PROCESS NMOS Device Boron, Initial substrate B = 1 x 1018 cm-3 doping, Na Boron, Retrograde well B = 8 x 1013 cm-2 E =200keV 2.0nm Gate oxide thickness, tox Boron, Source/drain B = 9.5 x 1011 cm-2 extension Halo Implantation

FIGURE 1 : COMPLETE STRUCTURE OF 80NM NMOS WITH TIO 2 .

5.34893x 1017 2.0

tox(nm) Gate Length, L(µm)

.08

Gate Width, W(µm)

Channel Length (µm)

.04

Channel Width (µm)

The simulated NMOS structure with source/drain junction depth and net doping concentration is shown below in figure 2.

Boron, B = 3 x 1013cm-2 E =20keV Arsenic, As = 5 x 1015cm-2 E =60keV 1000oC/1 sec

3. RESULTS & DISCUSSION. The results of fabrication & simulation of 80nm NMOS can be viewed in the TONYPLOT is as shown below. Figure1 shows the electrodes are highlighted in this final structure of this NMOS device.

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INTERNATIONAL JOURNAL FOR RESEARCH IN APPLIED SCIENCE AND E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T)

FIGURE 2 : NMOS STRUCTURE SHOWING THE JUNCTION DEPTH. The complete structure can now be simulated using ATLAS to provide specific characteristics such as Id-Vg, Id-Vd, sub-threshold and DIBL curve. Figure 3 shows that Id vs. Vg curve, which gives the extraction of threshold voltage. The threshold voltage of this operation happens when the current reaches zero. Vd = -0.1V is applied for this graph. When Vg<Vt, the current is zero but the current start increasing when Vg>Vt. With a small value of Vd applied it is possible to examine the effect of an increase gate voltage. After reaching the threshold voltage the induced n-channel begins to increase in depth. The name enhancement type is tacked onto this type of MOSFET as a result of the gate voltage having to overcome the threshold voltage & enhance the channel [3].

FIGURE 4 : THE I D VS. V D CURVE. Figure 5 shows the sub-threshold characteristic curve for NMOS device with TiO2 as gate dielectric. Sub-threshold characteristic of a MOSFET is an important parameter which determines the holding time in dynamic circuits as well as the static power dissipation in static CMOS circuits [5]. The sub-threshold current is due to weak inversion in the channel between flat-band and threshold voltage (for band-bending between zero and 2Ď&#x2020;F), which leads to a diffusion current from source to drain [6].

Figure 5 : Sub-threshold characteristic.

FIGURE 3 : THE I D VS. V G CURVE. Figure 4 shows the families of Id vs. Vd curves for NMOS. This curve is plotted using ATLAS simulator. The gate voltages that apply for 50.5V, 100.5V, & 150.5V denoted by red, green & blue lines [3]. The graph in figure 4 shows Id -Vd not saturated due to the punch through effect only for Punch through causes a rapidly increasing current with increasing drain-source voltage. It is an extreme cause of channel length modulation where the depletion layers around the drain & source region merge into single depletion region. Suppose the red, green & blue line in graph start saturate at 0.2515V, 0.2517V & 0.2519V. But, this graph not saturate due to punch through effect [3].

If small channel length MOSFETs are not scaled properly and the source/drain junctions are too deep or the channel doping is too low, there can be unintended electrostatic interactions between the source and the drain known as Drain Induced Barrier Lowering (DIBL). This leads to punch-through leakage or breakdown between the source and the drain, and loss of gate control [6]. A DIBL test is performed for the transistor, which results in a Id-Vg plotted at different drain voltages (Vd). Figure 6 shows a decreasing DIBL curve for NMOS transistor with 0.025V and 0.05V drain voltage are shown by green & red curves respectively.

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INTERNATIONAL JOURNAL FOR RESEARCH IN APPLIED SCIENCE AND E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) Figure 8 shows effect of gate oxide thickness on threshold voltage by varying the gate oxide thickness from 2.0nm to 3.5nm. The gate oxide thickness was the first parameter that was modified. The value of gate oxide thickness was modified to get the gate oxide thickness value in line with ITRS guideline for 80nm device [2]. With Increase in gate oxide thickness, Vt increases. The gate oxide thickness is a reverse proportion to the gate capacitance. When the gate oxide capacitance goes down, which means that the gate has less control over the channel in order to invert the channel, the Vt will increase.

Figure 6 : DIBL curve.

Gate oxide thickness vs Threshold voltage Threshold Voltage (V)

3.1 Effect of drain voltage on threshold voltage The value of gate to source voltage (Vgs) for which sufficient amount of mobile electrons accumulates in the channel region so that a conducting channel is formed is called the threshold voltage [7]. The Figure 7 describes the effect of drain voltage on threshold voltage for NMOS with TiO2 gate dielectric. It is observed from the analysis that as the drain voltage increases, the threshold voltage decreases for NMOS. Thus, the threshold voltage of the device could be varied by applying different drain voltage to the transistor. However, a reduction in the threshold voltage gives rise to an increase in the sub-threshold leakage current, which is the current that is conducted through a transistor from its source to drain when the device is intended to be off. Due to this increase in sub-threshold current, static power consumption is increased and the overall device performance is degraded [1].

-0.55 -0.6 -0.65 -0.7 -0.75 -0.8 -1.5

-1

-0.5

2.2

2.5

3.3

3.5

Gate oxide thickness (nm)

Figure 8 : Effect of gate oxide thickness on threshold voltage. 3.3 EFFECT OF GATE OXIDE THICKNESS ON SHEET RESISTANCE. Sheet resistance is a measure of resistance of thin films that are nominally uniform in thickness. It is commonly used to characterize materials made by semiconductor doping, metal deposition etc. Examples of these processes are: doped semiconductor region (e.g., silicon or polysilicon).This parameter is applicable to twodimensional systems in which thin films are considered as twodimensional entities. This term implies that current flow is along the plane of the sheet, not perpendicular to it. Figure 9 shows effect of gate oxide thickness on Sheet resistance. With Increase in gate oxide thickness, sheet resistance decreases.

-0.1

Drain Bias (V)

Figure 7 : Effect of drain voltage on threshold voltage. An increase in threshold voltage is observed if the drain voltage applied to the transistor is quite low in magnitude, with an accompanying decrease in off state leakage current. Increasing the threshold voltage of the NMOS is an effective way to reduce subthreshold leakage.

Sheet Resistance (立/square)

Threshold Voltage (V)

Threshold Voltage vs Drain Voltage

-0.5 -0.6 -0.7 -0.8

Gate oxide thickness vs Sheet Resistance 1624.32 1624.3 1624.28 1624.26 1624.24 2

2.2

2.5

Gate oxide thickness (nm)

3.2 EFFECT O F GATE OXIDE THICKNESS ON THRESHOLD VOLTAGE

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3.3

3.5

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INTERNATIONAL JOURNAL FOR RESEARCH IN APPLIED SCIENCE AND E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) Figure 9 : Effect of gate oxide thickness on Sheet resistance.

with thickness of 80nm in respect with their threshold voltages (Vt) is shown in figure 11. Figure shows Vt for SiO2 is 0.25V & for TiO2 is 0.76V.

3.4 Effect of gate oxide thickness on Channel Surface concentration.

Effect of TiO2 on Threshold voltage Threshold voltage (V)

Channel surface Concentration (atoms/cm3)

Figure 10 shows effect of gate oxide thickness on threshold voltage. Here the gate oxide thickness is varied from 2.0nm to 3.5nm which proves that channel surface concentration increases with increase in gate oxide thickness.

Gate oxide thickness Vs Channel Surface Concentration

5.50E+17

0.5 0 -0.5 -1 TiO2

SiO2 NMOS

5.40E+17

Figure 11 : Effect of TiO2 on Threshold voltage.

5.30E+17 5.20E+17 2

2.2 2.5 3.3 3.5

Gate oxide thickness (nm)

Figure 10 : Effect of gate oxide thickness on Channel Surface concentration. The analysis of Threshold voltage, sheet resistance & channel surface concentration is studied for NMOS device. The summary of NMOS effect of gate oxide thickness on device parameters such as threshold voltage, sheet resistance, and channel surface concentration is shown in table 4 describing the above mentioned parameters are given below. Table 4 : Summary of the effect of oxide thickness on device parameters for NMOS. Gate

Threshold

n++ Sheet

Channel

Oxide

Voltage

Resistance

Surface

Thickness

(V)

(â&#x201E;Ś/square)

Concentration

It is of significant importance to reduce the sub-threshold swing, which is a measure of the rate of change in current (Id) as a function of gate voltage (Vg) in a MOSFET, since a lower sub-threshold swing will lower the supply voltage and hence the dissipation [1]. From Figure 14 it can be observed that the sub-threshold swing (1/S) decreases when SiO2 is replaced with TiO2 dielectrics. This may be due to reduction in leakage current between drain and gate while using high-k dielectric material. The reduction in 1/S values with TiO2 can also be attributed to heavy threshold adjust implants which blocks shallow paths for punch-through current thereby reducing 1/S in short channel devices [5]. The sub-threshold curve for TiO2 is already shown in figure 5. Id-Vg curve for NMOS with SiO2 as gate oxide is also shown in figure 12 & DIBL curves for SiO2 as gate dielectric is shown in figure 13. All these curves are plotted for 80nm SiO2 gate dielectric N-channel MOSFET.

(atoms/cm3)

(nm) 3.5

-0.65

1624.27

5.36551e+017

3.3

-0.66

1624.28

5.36324e+017

2.5

-0.68

1624.30

5.35432e+017

2.2

-0.71

1624.31

5.35108e+017

2.0

-0.76

1624.31

5.34893e+017 Figure 12 : Id-Vg relation for 80nm NMOS with SiO2 as gate oxide.

3.5 Effect of high-k (TiO2) on threshold voltage The high-k dielectric not only results in reducing the threshold voltage of the transistor but also reduces the major problem of shortchannel affects i.e. Drain-Induced-Barrier-Lowering (DIBL). A comparison for both NMOS devices (SiO2 & TiO2 as gate dielectrics)

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INTERNATIONAL JOURNAL FOR RESEARCH IN APPLIED SCIENCE AND E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) MOSFET device structure. The sub-threshold leakage current was found to be decreased with increasing threshold voltage; this reduces the power consumption and thus improves the device performance. The reduction in gate leakage and sub-threshold swing projects the high-k MOSFET structure to be a strong alternative for future Nanoscale MOS devices. It can also be concluded from the analysis that as device was scaled down, the threshold voltage of the device decreases. Hence, to adjust the threshold voltage and other short channel effects within the permissible limits device engineering can be employed.

References FIGURE 13 : DIBL CURVE FOR 80NM NMOS WITH SIO 2 AS GATE OXIDE.

Figure 14 : Sub-threshold curve for 80nm NMOS with SiO2 as gate oxide.

4. CONCLUSION N-channel MOSFET structure with 80 nm gate length was designed and simulated to study the effect of high-k dielectric (TiO2), drain voltage and oxide thickness on the device performance. Performance of the two structures- NMOS using TiO2 with Polysilicon gate & NMOS using SiO2 with Polysilicon gate were compared. It was found that some of the parameters like threshold voltage, sub-threshold swing and DIBL were reduced while drain current was increased upon applying high-k dielectric on planar

[1] George James T, Saji Joseph and Vincent Mathew, “Effect of counter-doping thickness on Double-Gate MOSFET characteristics”, Journal of Semiconductor Tachnology and Sciences, Vol.10, No. 2, pp. 130,132, June 2010. [2] M. H. Chowdhury, M. A. Mannan and S. A. Mahmood, “High-k Dielectrics for Submicron MOSFET”, IJETSE International Journal of Emerging Technologies in Sciences and Engineering, vol. 2, no. 2, pp. 8-10, July 2010. [3] Maizan Muhamad, Sunaily Lokman, Hanin Hussin, “optimization in fabricating 90nm NMOS transistors using silvaco”, IEEE student conference on research and development. pp. 2 ,2009. [4] S. A. Campbell, Member, IEEE, David C. Gilmer, Xiaochuan Wang, Ming-ta Hsieh, Hyeon-Seag Kim, Wayne L. Gladfelter, International Business Machines Corporation, “Titanium dioxide (TiO2)-based gate insulators” IBM J. Research Development, vol. 43, no. 3, pp. 2, May 1999. [5] Shashank N Sensors & Nanotechnology Group, S Basak Birla Institute of Technology and Science, India, R K Nahar, Sensors & Nanotechnology Group, Central Electronics Engineering Research Institute Council of Scientific and Industrial Research (CSIR), India, "Design and Simulation of Nano Scale High-K Based MOSFETs with Poly Silicon and Metal Gate Electrodes", International Journal of Advancements in Technology, IJOAT, vol. 1, no. 2, pp. 2, October 2010. [6] Syafeeza Binti Ahmad Radzi, Electronics and Telecommunication “Simulation of 0.18 micron mosfet and its characterization”, M.Tech. Thesis under faculty of Electrical Engineering University Technology Malaysia, pp47, 55, 71-76, October 2005. [7] Vinay K. Yadav and Ashwani K.Rana, “Impact of channeldoping on DGMOSFET parameters in Nano Regime-TCAD simulation”, International Journal of Computer Applications, Vol 37, No. 11, pp.36-40, January 2012.

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A Review paper on Routing Protocol Comparison Ms. Aastha kohli 1, Mr. Sukhbir 2 1

M.Tech(CSE) (N.C College of Engineering, Israna Panipat)

HOD Computer Science Dept.( N.C College of Engineering, Israna Panipat)

ABSTRACT This paper presents a broad study on the work of common MANET (mobile adhoc network) routing protocols. The routing protocols used in this study include AODV, DSR and DSDV which consist of a mixture of reactive and proactive protocols. The paper is a survey of research on routing protocols for MANET. The paper has been made to compare three well know protocols AODV, DSR and DSDV by using three performance metrics packet delivery ratio, average end to end delay and routing overhead. The comparison has been done by using simulation tool NS2, NAM (Network Animator) and excel graph which is used for preparing the graphs from the trace files. Keywords: MANET, NS-2, AODV, DSDV, DSR 1.

INTRODUCTION



The routing protocol should be power-efficient.

Mobile ad hoc networks (MANETs) are freely selforganized networks without infrastructure support. In a mobile ad hoc network, nodes move readily. Because nodes in a MANET normally have low transmission ranges, some nodes cannot communicate directly with each other. Hence, routing paths in mobile ad hoc networks potentially contain multiple hops, and every node in mobile adhoc networks has the responsibility to act as a router. Mobile Ad-hoc networks are selfconfigured multihop wireless networks where, the structure of the network changes dynamically. This is mainly due to the mobility of the nodes [2]. Nodes in these networks utilize the same random access wireless channel, cooperating in a friendly manner to engaging themselves in multihop forwarding. The node in the network not only acts as hosts but also as routers that route data to/from other nodes in network [1]. Some examples include interaction of students during lecture, sharing of files by business associates in an airport terminal. The group of mobile hosts may form their ad hoc network, if every mobile host is equipped with wireless local area network interface.



The routing protocol should be secure.



A hybrid routing protocol should be more reactive than proactive to avoid overhead.



A routing protocol should qualify the Quality of Service (QoS)

Wireless ad-hoc networks have got a lot of importance in wireless communications. Wireless communication is established by nodes acting as routers and transferring packets from one to another in ad-hoc networks. Routing in these networks is more complicated due to moving nodes and hence many protocols have been developed. In this paper we have selected three main routing protocols.Figure1 below represents the scenario of MANET.

Routing protocols of MANET should have the following Properties:  

A routing protocol should be assigned in manner in order to increase its reliability. A routing protocol must be designed considering unidirectional links because wireless medium may cause a wireless link to be opened in unidirectional only due to physical factors.

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Figure 1

Ad-hoc Network Architecture

2 Applications of MANET: MANETs are useful in places where no communication infrastructure or the infrastructure is damaged. Typical applications are:

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Military or police exercises.



Disaster relief operations.



Mine cite operations.



Urgent Business meetings.

frequent path breaks due to mobility of nodes, and the inherent fading properties of the wireless channel [3]. 4 Classification of Adhoc Routing Protocol Routing protocol depending upon routing Strategy, these are done structure.

3 Challenges of MANET: 

Finite wireless transmission range: In wireless networks the radio band will be limited and hence data rates it can offer are much lesser than what a wired network can offer. This requires the routing protocols in wireless networks to use the bandwidth always in an optimal manner by keeping the overhead as low as possible [3].



Routing Overhead: In wireless adhoc networks, nodes are random they often change their location within network. Some weak routes are generated in the routing table which leads to unnecessary routing overhead.



Battery constraints: This is one of the limited resources that form a major constraint for the nodes in an ad hoc network. Devices used in these networks have restrictions on the power source in order to maintain portability, size and weight of the device. By increasing the power and processing ability makes the nodes bulky and less portable. So only MANET nodes has to optimally use this resource [4].



Asymmetric links: Most of the wired networks rely on the symmetric links which are always fixed. But this is not a case with adhoc networks as the nodes are mobile and constantly changing their position within network. For example consider a MANET (Mobile Ad-hoc Network) where node B sends a signal to node A but this does not tell anything about the quality of the connection in the reverse direction [5].



Time-varying wireless link characteristics: The wireless channel is susceptible to a variety of transmission impediments such as path loss, fading, interference and blockage. These factors resist the range, data rate, and the reliability of the wireless transmission. The extent to which these factors affect the transmission depends upon the environmental conditions and the mobility of the transmitter and receiver. Even the two different key constraints, Nyquist’s and Shannon’s theorems, that govern the ability to transmit information at different data rates can be considered [3].



Packet losses due to transmission errors: Ad hoc wireless networks experiences a much higher packet loss due to factors such as high bit error rate (BER) in the wireless channel, increased collisions due to the presence of hidden terminals, presence of interference, location dependent contention, uni-directional links,

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in MANET can be classified into several ways the network structure, communication model, and state information and so on but most of depending on routing strategy and network

Figure 2 Classification of Adhoc Routing Protocol. 4.1 Destination-Sequenced Distance-Vector Routing (DSDV) DSDV is a hop –to –hop distance vector routing protocol. In this protocol, each node has a routing table that stores the next hop, number of hops for all the reachable destinations. Each node broadcast routing updates periodically. The advantage of DSDV over traditional distance vector routing protocols is that DSDV guarantees loop-free routing. Destination-Sequenced DistanceVector Routing (DSDV) is a table-driven routing scheme for adhoc mobile networks based on the Bellman-Ford algorithm. It was developed by C. Perkins and P.Bhagwat in 1994. In DSDV, each node maintains a next-hop table, which it exchanges with its neighbors. There are two types of next-hop table exchanges: periodic fulltable broadcast and event-driven incremental updating. The relative frequency of the full-table broadcast and the incremental updating is determined by the node mobility. In each data packet sent during a next-hop table broadcast or incremental updating, the source node appends a sequence number. This sequence number is propagated by all nodes receiving the corresponding distance-vector updates, and is stored in the next-hop table entry of these nodes. A node, after receiving a new next-hop table from its neighbor, updates its route to a destination only if the new sequence number is larger than the recorded one, or if the new sequence number is the same as the recorded one, but the new route is shorter. In order to further reduce the control message overhead, a settling time is estimated for each route. A node updates to its neighbors with a new route only if the settling time of the route has expired and the route remains optimal [8].

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5.1 Simulation Tool

The Dynamic Source Routing protocol (DSR) is (Perkins, 2007), an on demand routing protocol. DSR is a simple and efficient routing protocol designed specifically for use in multi-hop wireless ad hoc networks of mobile nodes. Using DSR, the network is completely self organizing and self-configuring, requiring no existing network infrastructure or administration. The DSR protocol is composed of two main mechanisms that work together to allow the discovery and maintenance of source routes in the ad hoc network [7]:

In this paper the simulation tool used for analysis is NS-2 which is highly preferred by research communities.



Route Discovery is the mechanism by which a node S wishing to send a packet to a destination node D obtains a source route to D.



Route Discovery is used only when S attempts to send a packet to D and does not already know a route to D.



Route Maintenance is the mechanism by which node S is able to detect, while using a source route to D, if the network topology has changed such that it can no longer use its route to D because a link along the route no longer works. When Route Maintenance indicates a source route is broken, S can attempt to use any other route it happens to know to D, or it can invoke Route Discovery again to find a new route for subsequent packets to D. Route Maintenance for this route is used only when S is actually sending packets to D.



In DSR Route Discovery and Route Maintenance each operates entirely" on demand"[7].

Software used for the performance analysis of taken protocol is based on NS-2 version 2.27. NS Simulator based on two languages: an object oriented simulator, written in C++, and OTcl (an object oriented extension of Tcl) interpreter, use to execute users command scripts. NS2 is an object oriented simulator, written in C++, with an OTcl interpreter as a frontend. This means that most of the simulation scripts are created in Tcl (Tool Command Language). If the components have to be developed for ns2, then both tcl and C++ have to be used. The flow diagram given in figure3 shows the complete structure of NS2.

4.3 Ad hoc On-Demand Distance Vector (AODV) Routing Protocol The ad hoc on demand distance vector (AODV) routing protocol is a multi-hop routing protocol. It enables multi-hop routing between the nodes who wish to establish and maintain ad hoc network. It is based on distance vector routing algorithm. AODV allows mobile nodes to respond to link breakages and changes in network topology in a timely manner. The operation of AODV is loop-free, and by avoiding the Bellman-Ford” counting to infinity" problem offers quick convergence when the adhoc network topology changes (typically, when a node moves in the network). When links break, AODV causes the affected set of nodes to be notified so that they are able to invalidate the routes using the lost link. Route Requests (RREQs), Route Replies (RREPs) and Route Errors (RERRs) are message types defined by AODV [6].

5.2 Simulation parameters are as follows: Platform

Windows XP

NS version

Ns –allinone-2.26

Simulation time

200 s

Number of nodes

50 Wireless Nodes

Transmission Range

250 m

Simulation Area size

500 x 500 m

5 Simulation Based Analysis using Network Simulator (NS-2)

Traffic

CBR(Constant Bit Rate)

Simulation Setup (traffic scenario, Mobility model) performance metrics used and finally the performance of protocols is represented by using excel graph.

Node Speed

fixed to 20 m/s

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DSR,AODV and DSDV

Pause time

0, 20, 40, 80, 120, 160, 200

5.3 Simulation Results

Figure6. Avg. end to end delay versus pause time for AODV, DSR and DSDV (Number of node = 50, Area space = 500m x 500m) Figure4. Routing overhead versus pause time for AODV, DSR and DSDV (Number of node = 50, Area space = 500m x 500m

7. Conclusion Here we analyzed the performance of different routing protocol done in the mentioned mobility and traffic pattern on different pause time. We analyzed that when pause time set to 0 each of the routing protocols obtained around 97% to 99% for packet delivery ratio except DSDV which obtained 76%. DSR and AODV reached approx 100% packet delivery ratio when pause time equal to 200 while DSDV obtained only approx 94% packet delivery ratio.DSR and DSDV has low and stable routing overhead as comparison to AODV that varies a lot. Avg. End to End delay of DSDV is very high for pause time 0 but it starts decreasing as pause time increases. DSR performs well as having low end to end delay. When we compare the three protocols in the analyzed scenario we found that overall performance of DSR is better than other two routing protocols.

REFERENCES: [1] Mehran Abolhasan, Tadeusz Wysocki, and Eryk Dutkiewicz. A review of routing protocols for mobile ad hoc networks. Technical report, Telecommunication and Information Research Institute, University of Wollongong,Wollongong, NSW 2522.

Figure5. Packet delivery ratio versus pause time for AODV, DSR and DSDV(Number of node = 50, Area space = 500m x 500m)

Motorola Australia Research Centre, 12 Lord St., Botany, NSW 2525, Australia,2003. [2] Xiaoyan Hong, Kaixin Xu, and Mario Gerla. Scalable routing protocols for mobile ad hoc networks. 2002.

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) [3] C. Siva Ram Murthy and B. S. Manoj, “Ad Hoc Wireless Networks, Architectures and Protocols”, Second Edition, Low price Edition, Pearson Education, 2007. [4] International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.1, August 2010“ANALYZING THE MANET VARIATIONS, CHALLENGES, CAPACITY AND PROTOCOL ISSUES” G. S.Mamatha1 and Dr. S. C. Sharma [5] Jochen Schiller. Mobile Communications. Addison-Wesley, 2000. [6] Mobile Ad Hoc Networking Working Group – AODV, http://www.ietf.org/rfc/rfc3561.txt [7] Mobile Ad Hoc Networking Working Group – DSR, http://www.ietf.org/rfc/rfc4728.txt. [8] “Wireless Ad Hoc Networks” Zygmunt J. Haas, Jing Deng, Ben Liang, Panagiotis Papadimitratos, and S. SajamaCornell University School of Electrical and Computer Engineering.

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Mobile robot Navigation Techniques: A Survey Vikas Verma1 1Jaipur National University, jagatpura, Jaipur Vikasverma1988@gmail.com

Abstract: This paper describes the developments of different basic techniques for mobile robot navigation during the last 10 years. Now a day’s mobile robots are vastly used in many industries for performing different activities. Controlling a robot is generally done using a remote control, which can control the robot to a fixed distance, we discuss three basic techniques for mobile robot navigation the first technique is the combination of neural network and Fuzzy logic makes a neuro fuzzy approach. Second method is the Radio frequency technique which is control system for a robot such that the mobile robot is controlled using mobile and wireless RF communication. Third method is robot navigation using a sensor network embedded in the environment. Sensor nodes act as signposts for the robot to follow, thus obviating the need for a map or localization on the part of the robot. Navigation directions are computed within the network (not on the robot) using value iteration. Keywords: Fuzzy logic, neural network, Radio frequency, signposts

Introduction

In the field of industrial and service robotics, the problem of wheelbased mobile robot navigation has attracted considerable attention in the recent years. Solution techniques to this complex problem involve handling many issues including among the others acquisition and processing of sensory data, decision making, trajectory planning, and motion control. From the motion control point of view, one major problem is the development of good and robust trajectory tracking algorithms in a variety that must also cover the many different types of mobile robots. In fact, various mobility configurations can be found depending, e.g., on the number and type of the wheels, their actuation, the single- or multibody vehicle structure, etc. Main issue in mobile robot is navigation in an uncertain and complex environment and considerable research has been done for making an efficient algorithm for the mobile robot navigation. Among them, adaptive control and behavior based control are most popular control algorithms and driving research in robot navigation. Adaptive navigation control is a method using pre-defined equations that represent the robot’s moving path to reach targets and show strong ability in well-known environment [1]. However it is hard to build precise path generating equation for unknown and complex environment. Evolutionary computation provides an alternative design method that adapts the robot's behavior without requiring a precisely specified model of the world. Its adaptive power enables the robot to deal with changes in the environment and to acquire a robust behavior tolerating noisy and unreliable sensor information This paper contain basic three techniques of mobile robot navigation. In which first technique is Neuro fuzzy approach which is the combination of fuzzy logic and neural network. Fuzzy systems employ a mode of approximate reasoning, which allows them to make decisions based on imprecise and incomplete information in a way similar to human beings. A fuzzy system offers the advantage of

knowledge description by means of linguistic mathematical or logical models. Fuzzy control provides a flexible tool to model the relationship between input information and control output and is distinguished by its robustness with respect to noise and variation of system parameters. Soft computing is concerned with the design of intelligent and robust systems, which exploit the tolerance for imprecision inherent in many real world problems. In order to achieve this objective, soft computing suggests fuzzy logic reasoning. The other popular Technique for Controlling a robot is generally done using a remote control, which can control the robot to a fixed distance, but here by designing control system for a robot such that the mobile robot is controlled using mobile and wireless RF communication. In this method controlling is done depending on the feedback provided by the sensor. This contains different modules such as  Wireless unit module  Sensing and controlling module In the sensing module when the PIC micro controller is powered up the high-speed dc motors. The sensor is mounted on the robot. The encoder mounted on the robot transmitting the data continuously. Here the robot consists of Transmitter and receiver. Here the frequency used is 433 kHz. The third method we discuss in this paper is Sensor network based mobile robot navigation for controlling a robot. Sensor nodes act as signposts for the robot to follow, thus obviating the need for a map or localization on the part of the robot. Navigation directions are computed within the network (not on the robot) using value iteration. Using small low power radios, the robot communicates with nodes in the network locally, and makes navigation decisions based on which node it is near. An algorithm based on processing of radio signal strength data was developed so the robot could

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2. Nuero Fuzzy approach The neural network used is a four-layer perception. This number of layers has been found empirically to facilitate training. The input layer has four neurons, three for receiving the values of the distances from obstacles in front and to the left and right of the robot and one for the target bearing. If no target is detected, the input to the fourth neuron is set to 0. The output layer has a single neuron, which produces the steering angle to control the direction of movement of the robot. The first hidden layer has 10 neurons and the second hidden layer has 3 neurons. These numbers of hidden layer have also been found empirically. Figure 2 depicts the neural network with its input and output signals. The neural network is trained to navigate by presenting it with patterns representing typical scenarios, some of which are depicted in Figure 1. For example, a robot advances towards an obstacle, another obstacle being on its right hand side. There are no obstacles to the left of the robot and no target within sight. The neural network is trained to output a command to the robot to steer towards its left side. During training and during normal operation, input patterns fed to the neural network comprise the following components. y1[1]= Left obstacle distance y2[1]= Front obstacle distance y3[1]= Right obstacle distance y4[1]= Target bearing

These input values are distributed to the hidden neurons which generate outputs given by: yj[lay]=f(Vj[lay])

(1)

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Figure 1. Neuro-fuzzy controller for mobile robots navigation The inputs and outputs from the fuzzy controller are analyzed in the following section. The inputs to the fuzzy controller are left_obs (left obstacle distance), right_obs (right obstacle distance) and front_obs (front obstacle distance) and initialsteering-angle (out put from the neural network controller). Terms such as near, medium and far are used forleft_obs, right_obs and front_obs (Figure 2). Terms such as pos (Positive), zero and neg (Negative) are defined for initialsteering-angle (Figure 1). The out-put from the the fuzzy controller are left_velo and right_velo. Terms such as fast, medium and slow, are defined for left_velo (left velocity) and right_velo (right velocity). The member ship functions described above are shown in Figure 1. All these membership function are triangular or trapezoidal which can be determined by three inputs. The experimental paths followed by mobile robots to reach the target are obtained as shown in Figure 3. From the fuzzy controller (inputs: left, front, right obstacle distances and heading angle) after defuzzyfication, robots get the left and right wheel velocities which subsequently give the new steering angles.

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Figure 2: Fuzzy controller for mobile robot navigation The paths traced by the robots are marked on the floor by a pen (fixed to the front of the robots) as they move. From these figures, it can be seen that the robots can indeed avoid obstacles and reach the targets. The experimentally obtained paths follow closely those traced by the robots during simulation (shown in Figure 3). From this figures, it can be seen that the robots can indeed avoid obstacles and reach the targets. Table 1 shows the times taken by the robots in simulations and in the experimental tests to find the targets. The figures given are the averages of 12 Experiments on each environmental scenario being conducted in the laboratory. Table 1. Time taken by robots in simulation and experiment to reach targets.

RFID Technology

An innovative mobile robot navigation technique using radio frequency technology. Navigation based on processing some analog features of an Rf signal is a promising alternative to different types of navigation methods in the state of the art. The main idea is to exploit the ability of a mobile robot to navigate a priori unknown environments without a vision system and without building an approximate map of the robot workspace, as is the case in most other navigation algorithms.

Figure 3. Path traced by simulated and actual real mobile robot. The suggested algorithm is capable of reaching a target point in its a priori unknown workspace, as well as tracking a desired trajectory with a high precision. The proposed solution offers a modular, computationally efficient and cost-effective alternative to other navigation techniques for a large number of mobile robot applications, particularly for service robots, such as, for instance, in large offices and assembly lines. The effectiveness of the proposed approach is illustrated through a number of computer simulations considering test beds of various complexities 3.1 RFID Systems RFID is an automatic identification method that relies on storing and remotely retrieving data. The basic communication between the robot and the system based on radio frequency (RFID) technology. A communication antenna is usually built within the wireless control unit and sensor and flying unit. The RF encoder in the wireless control unit sends the information to the flying robot. RF Decoder in the sensing and control unit receives the information and controls the motor and the information from the sensing and control unit transmitted to the wireless control unit. Encoder HT 12E and decoder HT12D is used for mobile robot navigation technique. The 212 encoders are a series of CMOS LSIs for remote control system applications. They are capable of encoding information, which consists of N address bits and 12_N data bits. Each address/ data input can be set to one of the two logic states. The Programmed addresses/data are transmitted together with the header bits via an RF or an infrared transmission medium upon receipt of a trigger signal. The capability to select a TE trigger on the HT12E or a DATA trigger on the HT12A further enhances the application flexibility of the 212 series of encoders. The HT12A additionally provides a 38 kHz carrier for infrared systems.

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Figure 4. PC and wireless control unit block diagram

Figure 5. Sensing and Control Unit-Block Diagram Embedded systems are integral part of our life and play a major role in improving the standard and quality of life. Consumer appliances to Bio-Medical equipment, communications, nuclear application and space research are some of the key areas where embedded systems play a vital role. The use of micro controller has been enhanced to such an extent that we cannot expect the world without micro controller, the advantage over the much used micro processor is that it has got the internal memory to store the program which makes it more usable in the real time world. With the help of sensor feedback mechanism with RF communication the mobile robot can be controlled from a far distance, which is desirable fact when the robot is working in hazardous environment. A potential future research avenue to extend this paper is to append the algorithm with a real-time path-planning module to which the RFID tag locations in the 3-D space would be a priori known (but not to the navigation module, however).It would also be important to

Navigation using a Sensor Network

The global navigation problem deals with navigation on a larger scale in which the robot cannot observe the goal state from its initial position. A number of solutions have been proposed in the literature to address this problem. Most rely either on navigating using a prespecified map or constructing a map on the fly. Most approaches also rely on some technique of localization. Some work on robot navigation is landmark based relying on topological maps [2], which have a compact representation of the environment and do not depend on geometric accuracy. The downside of such approaches is that they suffer from sensors being noisy and the problem of sensor antialiasing (i.e. distinguishing between similar landmarks). Metric approaches to localization based on Kalman filtering [3] Provide precision, however the representation itself is unimodal and hence cannot recover from a lost situation (Misidentified features or states). Approaches developed in recent years based on ’Markov localization’ [4] provide both accuracy and multimodality to represent probabilistic distributions of different kinds, but require significant processing power for update and hence are impractical for large environments. One of the attempts to solve this problem is presented in [5] where a sampling-based technique is used. Rather than storing and updating a complex probability distribution, a number of samples are drawn from it. The other approaches utilize partially observable Markov decision process (POMDP) models to approximate distance information given a topological map, sensor and actuator characteristics [6]. POMDP models for robotic navigation provide reliable performance, but fail in certain environments (e.g symmetric) or suffer from large state spaces (i.e. state explosion). These approaches have different advantages, but also disadvantages or fail cases. Note that all of the above approaches assume that a map of the environment (topological and/or metric) is given a priori. None of the above approaches deal with highly dynamic environments in which topology might change. Our approach, presented here, instruments the environment with a sensor network. An ant-like trail laying algorithm is presented in [7], where ‘virtual’ trails are formed by a group of robots. Navigation is accomplished through trail following. The shortcoming of the algorithm is that it is dependent on perfect communication between the members of the group. In addition, the ’virtual’ trails are shared between the robots, which means redundant sharing of the state space in the group. Moreover, a common localization space is assumed. We are broadly interested in the mutually beneficial collaboration between mobile robots and a static sensor network. The underlying principle in interaction between the network and robots is: the network serves as the communication, sensing and computation medium for the robots, whereas the robots provide actuation, which is used among other things for network management and updating the network state. In this work results from such a system which accurately and reliably (100% correct navigation out of 50 experiments totaling over 1km in distance) solves the problem of

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) robot navigation. Some properties of the approach are summarized below: 1) The sensor network is redeployed into the environment using the algorithm given in [8]. 2) In addition to deploying the network nodes, the deployment algorithm also computes the distributions of transition probabilities P (s0js; a) from network nodes to s0, when the robot executes action a [9]. 3) The nodes of the sensor network are synchronized in time (high precision is not required). For an example of a time synchronization algorithm see [10]. 4) The robot does not have a pre-decided environment map or access to GPS, IMU or a compass. 5) The environment is not required to be static. 6) The robot does not perform localization or mapping. 7) The robot does not have to be sophisticated – the primary computation is performed distributive in the Sensor network, the only sensor required is for obstacle avoidance.

every state and then pick the actions that yield a path towards the goal with maximum expected utility. The rationale is that the robot should ’pay’ for taking an action (otherwise any path that the robot might take would have the same utility), however, the cost should not be to big (otherwise the robot might prefer to stay at the same state). Initially the utility of the goal state is set to 1 and of the other states to 0. 2) Distributed Computation and In-network Processing: A much more attractive solution is to compute the action policy distributive in the deployed network. The idea is that every node in the network updates its utility and computes the optimal navigation action (for a robot in its vicinity) on its

4.1 Probabilistic navigation Stage I - Planning When the navigation goal is specified (either the robot requests to be guided to a certain place, or a sensor node requires the robot’s assistance), the node that is closest to the goal triggers the navigation field computation. During this computation every node probabilistically determines the optimal direction in which the robot should move, when in its vicinity. The computed optimal directions of all nodes in conjunction compose the navigation field. The Navigation Field provides the robot with the ‘best possible’ direction that has to be taken in order to reach the goal. Note that a ‘kidnapped’ robot problem is solved by our system implicitly and does not require re-computation (or re-planning). It may be noted that a parallel approach for the construction of a navigation field has been proposed in the sensor network literature [11]. Instead of value iteration [11] uses potential fields and the hop count to compute the magnitude of the directional vectors. 1) Theoretical Framework - Value Iteration: Consider the deployed sensor network as a graph, where the sensor nodes are vertices. Assume a finite set of vertices S in the deployed network graph and a finite set of actions A the robot can take at each node. Given a subset of actions A(s) _ A, for every two vertices s; s0 2 S in the deployed network graph, and action a 2 A(s) the transition probabilities P(s0js; a) (probability of arriving at vertex s0 given that the robot started at vertex s and commanded an action a) for all vertices are determined [9]. Figure 6 shows a typical discrete probability distribution for a vertex (sensor node) per action (direction). Note that in practice the probability mass is distributed around neighboring nodes and zero otherwise. This was the proposed system Markovian – the state the robot transitions to depends only on the current state and action. We model the navigation problem as a Markov Decision Process [12]. To compute the best action at a given vertex we use the Value Iteration [13] algorithm on the set of vertices S sg, where sg is the goal state. The general idea behind Value Iteration is to compute the utilities for

Figure 6. An example of a discrete probability distribution of vertex (sensor node) k for direction (action) “East” (i.e. right). own. When the navigation goal is determined (either a robot requested to be guided to a certain node, or a node requires robot’s assistance), the node that is closest to the goal triggers the computation by injecting a Start Computation packet into the network containing its id. Every node redirects this packet to its neighbors using flooding. Nodes that receive the Start Computation packet initialize utilities and the cost values depending on whether the particular node is specified as a goal or not. Every node updates the utilities according to equation 1. Note that the utilities of neighboring nodes are needed as well, hence, the node queries its neighbors for corresponding utilities. Since computation of some nodes can proceed faster than others, every node stores computed utilities in a list, so that even if it is queried by its neighbors for a utility several steps prior to the current one, the list is accessed and the corresponding utility is sent. After the utilities are computed, every node computes an optimal policy for itself according to equation 2. Neighboring nodes are queried once again for the final utility values. The computed optimal action is stored at each node and is emitted as part of a suggestion packet that the robot would receive if in the vicinity of the node. This

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) technique allows the robot to navigate through the environment between any two nodes of the deployed network. Note that the action policy computation is done only once and does not need to be recomputed unless the goal changes. Also, note that the utility update equations have to be executed until the desired accuracy is achieved. For practical reasons the accuracy in our algorithm is set to 10�3, which requires a reasonable number of executions of the utility update equation per state and thus, the list of utilities that every node needs to store is small. Since the computation and memory requirements are small it is possible to implement this approach on the real node device that we are using (the Mote [15]). Note that if neighbors of all nodes are known exactly (for every direction each node has at most one neighbor), then P (s0js; a) = 1. Hence, equations 1 and 2 reduce to the maximization of utilities of neighboring nodes only. In this case the system converges after a single iteration.

node switches and the navigation algorithm starts from phase 1 again, but with the current node set to 2. But how to determine when the robot is in the neighborhood of some node? A straightforward approach is to use signal strength thresholding. In this case, prior to the experiment an observation model can be built which given a signal strength value would approximate the distance from the node. Hence, ideally, while in phase 2, the robot would simply collect signal strength values from the packets of all nodes in the vicinity, feed the model with these values and threshold an output picking the shortest distance. We conducted experiments at Intel Research facilities in Hillsboro, Oregon. We used a Pioneer 2DX mobile robot, with 180o laser range finder used for obstacle avoidance, and a base station (Mica 2 mote) for communicating with the sensor network. Mica 2 motes were used as nodes of the sensor network. The sensor network of 9 nodes was redeployed into the environment. Every node is preprogrammed with information about its neighbors. We assume that the sensor network is deployed and transition probabilities set as

Figure 7. Navigation - node-wise approach B. Stage II - Navigation Note that the deployed sensor network discretizes the environment. Consider Figure 7. On the way from starting node 1 to goal node 5, the robot would first navigate from node 1 to 2, then from 2 to 3, and so on. Hence, the navigation is node wise. A node whose directional suggestion the robot follows at the moment is called current node. Initially the current node is set to the node closest to the robot. The bottom part of Figure 7 shows the three phases of navigation. Suppose initially current node is set to node 1 (robot’s position at the bottom right corner on the Figure 2). Node 1 suggests the robot to go ’UP’. In the first phase the robot accepts this command and positions itself in the correct direction. During the second phase, the robot moves ’forward’ using the VFH [2] algorithm for local navigation and obstacle avoidance. Note that throughout the second phase the current node is set to node 1. Phase 3 is triggered when the robot determines that it has entered the neighborhood of the next node say, node 2 (an oval M2 on Figure 7). During phase 3 the current

Figure 8. Map of the experimental environment. Nodes were manually Predeployed (nodes marked 1 - 9) described in [9]. The map of the experimental environment and deployed sensor network of 9 sensor nodes is shown in Figure 8. The environment itself resembles a regular cubicle office- like environment with narrow corridors (about 1 m), changing topology, crowded with people and obstacles. The experimental scenario that we consider for navigation is alarm handling An Alarm occurs when a certain node detects an event. The algorithm proceeds as discussed in previous sections. The task of the robot is to navigate from the ’home base’ (around node 1) towards the triggered alarm. The requirements that we impose for the experiment to be successful are that the navigation field should yield shortest paths from any point towards the goal node, the robot should follow the

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) shortest path, and the robot should stop within 3 meters of the goal node. The algorithm allows the robot to navigate precisely and reliably using a deployed sensor network. Our approach differs from systems described in the literature by assuming that a map, localization, GPS, IMU or compass is not available. The navigation occurs through node-wise motion from node to node on the path from starting node to the goal node. We conducted 50 experiments for 5 different goals, totaling over 1 km of traveled distance. In each of the 50 cases the robot successfully navigated to the goal node. Note that we considered an experiment to be successful if the robot approached the goal node to within 3m. This distance was experimentally set as ‘good enough’. since goal nodes represent a ‘local neighborhood’ requiring robot’s presence. Hence, when the robot arrives at such a ‘local neighborhood’, local navigation algorithms, like VFH [2], can be used to drive the robot exactly to where the robot’s presence is required. Furthermore, in practice, sensor network nodes would be mounted on top of the cubicles (in places where current markers are), which makes the 3m range reasonable.

[11] Q. Li, M. DeRosa, and D. Rus, “Distributed algorithms for guiding navigation across a sensor network,” Dartmouth, Computer Science Technical Report, Tech. Rep. TR2002-435, October 2002. [12] D. J. White, Markov Decision Process. West Sussex, England: John Wiley & Sons, 1993. [13] S. Koenig and R. G. Simmons, “Complexity analysis of real-time reinforcement learning applied to finding shortest paths in deterministic domains,” Carnegie Mellon University, School of Computer Science, Carnegie Mellon University, Pittsburg, PA 15213, Tech. Rep. CMU-CS- 93-106, Decembe

References [1] Zadeh, L.A. Fuzzy SetsInformation and Control. 8, 1965, 338353. Tanaka, K, An introduction to fuzzy logic for practical application, 1991. [2] D. Kortenkamp and T.Weymouth, “Topological mapping for obile robots using a combination of sonar and vision sensing,” in Proceedings of the AAAI, 1994, pp. 979–984. [3] K. Arras, N. Tomaris, B. Jensen, and R. Siegwart, “Multisensor on-thefly localization: Precision and reliability for applications,” Robotics and Autonomous Systems, vol. 34, no. (2-3), pp. 131–143, 2001. [4] D. Fox, “Markov localization: A probabilistic framework for mobile robot localization and naviagation,” Ph.D. dissertation, Institute of Computer Science III, University of Bonn, 1998. [5] F. Dellaert, D. Fox, W. Burgard, and S. Thrun, “Monte carlo localization for mobile robots,” in Proc. of ICRA-99, 1999, pp. 1322– 1328. [6] R. Simmons and S. Koenig, “Probabilistic robot navigation in partially observable environments,” in Proceedings of the International Joint Conference on Artificial Intelligence, 1995, pp. 1080–1087. [7] R. Vaughan, K. Stoy, G. S. Sukhatme, and M. Mataric, “Lost: Localization-space trails for robot teams,” IEEE Transactions on Robotics and Automation, vol. 18, no. 5, pp. 796–812, 2002. [8] M. A. Batalin and G. S. Sukhatme, “Efficient exploration without localization,” in In Proc. of IEEE International Conference on Robotics and Automation (ICRA’03), Taipei, Taiwan, 2003, pp. 2714–2719. [9]“Coverage, exploration and deployment by a mobile robot and communication network,” in The 2nd International Workshop on Information Processing in Sensor Networks (IPSN ’03), Palo Alto, 2003, pp. 376–391. [10] J. Elson, “Time synchronization in wireless sensor networks,” Ph.D. dissertation, University of California, Los Angeles, May 2003.

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Mobile Technology: The Strategic Impact Of Mobile Communications Within An Organization 1

Abhinav Sharma 2Mahesh Chandra Sharma 1

(Research Scholar in Dr.K.N.Modi University, Newai) 2

(Professor in Govt. R.R.PG College, Alwar)

Abstract: Mobile Technology is a growing field converging mobile computing with telecommunications in a wireless environment that has become more robust in its own right. This convergence of mobile communications enables an organization and its individual workforce to become more flexible in communication as well as work structure. Mobile technology is enabling an organization to work together or separately on a global basis while maintaining its collaborative information sharing capabilities. This allows for a near-seamless work environment to maintain its operational tempo as a work force is dispersed temporarily or on a permanent basis globally. With the evolution of faster, more robust mobile technologies, organizations and the workforce are breaking out of the traditional work environment and moving into the global community allowing them to be continually tied into the virtual office. This thesis is intended to show how mobile technology has reshaped the strategic vision of an organization and has affected society on a global scale. As society moves forward, mobile technology will continue to play a greater role in the strategic direction of organizations and on individual lives. People always strive for the perfect work/life balance based on an individual’s perceived notion of how that balance is defined. Mobile technology plays a significant role in today’s society increasing an organization’s productivity as it relates to its strategic goal, but also enhancing the lives of individuals outside of work. Organizations are realizing the benefits mobile communications bring and the results of successfully leveraging mobile technology. Each new generation of people grow up more mobile savvy and more comfortable with technology. This drives the ability of an organization to more successfully implement and use mobile technology to its advantage. Keywords: Communication, Generation, Mobile, Organization, Technology.

Introduction:-Mobile Technology is a growing field converging mobile computing with telecommunications in a wireless environment that has become more robust in its own right. This convergence of mobile communications enables an organization and its individual workforce to become more flexible in communication as well as work structure. Mobile technology is enabling an organization to work together or separately on a global basis while maintaining its collaborative information sharing capabilities. This allows for a near-seamless work environment to maintain its operational tempo as a work force is dispersed temporarily or on a permanent basis globally. With the evolution of faster, more robust mobile technologies, organizations and the workforce are breaking out of the traditional work environment and moving into the global community allowing them to be continually tied into the virtual office. This thesis is intended to show how mobile technology has reshaped the strategic vision of an organization and has affected society on a global scale.

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As society moves forward, mobile technology will continue to play a greater role in the strategic direction of organizations and on individual lives. People always strive for the perfect work/life balance based on an individual’s perceived notion of how that balance is defined. Mobile technology plays a significant role in today’s society increasing an organization’s productivity as it relates to its strategic goal, but also enhancing the lives of individuals outside of work. Organizations are realizing the benefits mobile communications bring and the results of successfully leveraging mobile technology. Each new generation of people grow up more mobile savvy and more comfortable with technology. This drives the ability of an organization to more successfully implement and use mobile technology to its advantage. The primary benefits of deploying mobile technologies in an organizational setting include: • Revenue growth • Reduction of operating costs • Streamlined processes and procedures • Competitive edge over other organizations • Increased face time with customers • Improved stakeholder relationships

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These benefits are only realized when the mobile technology leveraged is customized to an organizations unique strategic vision and direction driven by requirements. An organization must take a good internal look at its business model and outwardly at its strategic direction, its mission, and the overall goals it wishes to achieve. Whether an organization is a looking to add to its bottom line and increase profits, save lives and assist in disaster recovery or serve the public, or protect and defend an country’s way of life through military defence and action, the organization must clearly understand their strategic focus and direction and how mobile communications and technology can support this strategic vision. Getting closer to the customer is the goal of most organizations today. No matter what type of organization, close interaction with the customer is what drives organizations to have a deployed workforce. Of course, a deployed workforce has a requirement to be tied into the organizational infrastructure to connect to critical resources. Increased diffusion of mobile networks and technologies enables geographically separated entities and nomadic workers to utilize mobile communications to be closer to their customer base while remaining constantly connected to the critical resources needed to support the customer. This pushes organizations to rely on mobile technology to drive their strategic direction and goals in today’s mobile environment. The Technology of Mobile Communications:-

The evolution of mobile technologies is driven by user requirements and the increased demands for converged mobility anywhere and at anytime. Users require access to voice, data, and multimedia applications while staying connected to an organization’s infrastructure, which is vital for the successful and fully equipped nomadic worker. As the technology that enables mobility is still growing, a significant change has been seen in the use of wireless systems. Between 1990 and 2005, Global System for Mobile Communication, or GSM, enabled the transition from first generation analog wireless systems with a very small user base, to a subscriber base of over 1.5 billion. This fifteen year span experienced the explosion of robust wireless and mobile architectures and the significant price reduction of mobile and wireless equipment. The underlying theme with mobility is low cost access. Though the equipment may vary in price, it is important to enable a large envelope of access and a standard rate, varying only when service is transitioned to a distant network, say in another country or region of the world. Universal Mobile Telecommunications System, or UMTS, is a third generation technology developed to bring together telecommunications and information technology into a converged, mobile solution for people on the go. As mobile multi-media applications and services become more accessible, the development and demand for new, more robust and better

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equipped mobile devices will grow along with it. Organizations requiring mobile workers to have access to all of the resources a wired desktop system has residing in the office space. This demand drives the telecommunications market to create more converged mobile technology such as smart phones and other personal digital assistant-like devices. Wireless technology standards are the backbone of the mobile industry and shape the way mobile systems are developed and how they are supported. The cyclic effect of wireless standards development is a cause and effect relationship that is traced back to the first generation of wireless standards First Generation (1G) First Generation, or 1G, mobile technology was developed in from the late 1970’s to the mid-1980s introducing analog mobile phone standards to the telecommunications industry. Examples of these analog standards include (ICT Regulation Toolkit, 2008): Advanced Mobile Phone System (AMPS) used in the United States • Total Access Communications System (TACS) used in the United Kingdom • Nordic Mobile Telephone (NMT) used in Norway, Sweden, Finland, Switzerland, and Russia • C-450 was used in West Germany, Portugal, and South Africa • Radiocom 2000 used in France Second Generation (2G) Second Generation, or 2G, mobile technology was developed to improve on the 1G technology and make mobile technology digital. The primary digital standards include (ICT Regulation Toolkit, 2008): • GSM – Global System Mobile Communications, or GSM, is a Time Division Multiple Access (TDMA) based standard is used in most of the world. • IS-95 – This Code Division Multiple Access (CDMA) based standard was used mainly in North, Central and South America before most carriers moved to the GSM standard. • PDC – (TDMA-based) is used in Japan. 2.5G, the extension of second generation mobile technology, offers connection rates of up to 384 Kbps. Enhanced Data for GSM Environment, or EDGE, is considered to be a 2.5G network technology that is based on the GSM cell phone standard and, with vendors such as Nokia Siemens, software upgrades doubling the download speeds of up 592 Kbps of these 2.5G networks extend the life of this already deployed technology (Reardon, 2008).

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• MIMO – Multiple-input multiple-output wireless LAN technology

Third Generation, or 3G, mobile technology

Third Generation, or 3G, mobile technology was developed to improve on the 2.5G technology and make mobile technology digital. The 3G digital standards include (ICT Regulation Toolkit, 2008): • W-CDMA – Wideband Code Division Multiple Access is the scheme defined by the ITU as the main platform for UMTS. • CDMA2000 – Code Division Multiple Access 2000 is the family of technologies that included: o CDMA2000 1X – Doubles voice capacity of CDMAOne networks and delivers data speeds of 307 kbps in mobile environments. o CDMA2000 1xEV – This includes: � CDMA2000 1xEV-DO – delivering data speeds of 2.4Mpbs and supports applications such as MP3 transfers and video conferencing. � CDMA2000 1xEV-DV – provides integrated voice and simulations high-speed Packet data multimedia services up to 3.09Mbps. • TD-CDMA – Time Division – Code Division Multiple Access. Fourth Generation, or 4G, mobile technology Fourth Generation, or 4G, mobile technology is the next generation of wireless technology that is still being developed to fully replace 3G technology. 4G mobile technology brings datatransmissions speeds into the 100 Mbps and above range along with quality of service QoS and traffic prioritization. This combination of speed and traffic prioritization will enable the mobile worker to have the ability to join a video teleconference and other bandwidth intensive applications from virtually anywhere using their 4G supported mobile devices, thus bringing yet another capability normally reserved for the desktop office workforce.

The technology standards that will help shape 4G mobile technology include:

• OFDM – Orthogonal Frequency Division Multiplexing • OFDMA – Orthogonal Frequency Division Multiple Access • Mobile MiMAX – 802.16e IEEE specification designed to support up to 12Mbps transmission speeds using OFDMA • UMB - Ultra Mobile Broadband (also known as CDMA2000 EV-DO)

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4G will be designed as an IP-based, heterogeneous network enabling mobile users to be connected and have access to any mobile device at anywhere and at anytime. 4G will provide mobile users with flexible, fast, sharp quality, global coverage. One of the big benefits of 4G is the support for resource intensive applications such as video teleconferencing. References:[1] Kornak, A, Teutloff, J, & Welin-Berger, M (2004). Enterprise guide to gaining business value from mobile technologies. Hoboken, NJ: Wiley Publishing, Inc. [2] L-3 Communications (2007). L3 Guardian SME PED [Brochure]. Camden, NJ: No Author Given. [3] MoNet Lab (2008). 4G Mobile Systems. Retrieved December 14, 2008, from MoNet Mobile Networking Web site: http://monet.postech.ac.kr/new2008/research.html#4G. [4]Sauter, M (2006). Communication Systems for the Mobile Information Society. West Sussex, England: John Wiley & Sons Ltd.

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Advanced Chord Algorithm Vikash Jaglan1 Dr. Sukhvir Singh2 Research Scholar, NCCE, ISRANA1 Head and Associate Professor, NCCE, ISRANA2

Abstract: Network is a peer to peer (P2P) increasing popularity and now over a day. Systems on top of peer-peer overlay network abstraction of the machine or the physical network topology. Networks for structured and unstructured two types of networks are peer-topeer name. The network peer to peer unstructured is not applied to cover the network structure. No network unstructured peer provide any organization or network connection optimized algorithms. The structural arrangement of such networks, according to specific rules or algorithms nodes provides specific performance and coverage topology. Therefore, the structure of the peer network coverage link to be set. They are typically distributed hash table (DHT) indexes, such as Chord system. Chord algorithm is relatively simple and successful alternative program to promote the diffusion of structured P2P network, but it must be better to further improve search efficiency. Therefore, this paper aims to improve the Chord structured P2P network discovery process. In order to improve search efficiency, the other to introduce this routing table in the counterclockwise direction, and the original routing table (in the clockwise direction search). Simulation results show that this method improves the work efficiency chord structured P2P network search process to reduce the number of hops. Keyword used: P2P, DHT, 1 Introduction a. Becoming a structured peer network is becoming increasingly popular, because it has good performance and high scalability. Distributed hash table is used to generate less data traffic and prospecting program layout node. Chord protocol approach is popular and well-known DHT-based. Chord protocol is designed, simple protocol structured peer network architecture. Chord protocol is well distributed, scalability, stability, and load balancing. There are a lot of improvements and research Chord protocol In this paper, we focus on structured peer networks and string algorithms Chord protocol performance and efficiency of the proposed changes to improve.

2 Chord Algorithms Chord protocol is structured peer, solve problems, and effectively find the target node using the new approach. Cord protocol with provable performance, simplicity and provable correctness. Chord protocol is a key node mapping. 2.1 Properties of Chord Properties of the chord peer-to peer networks are as follows:

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Decentralization: Protocol string is not friendly, peer use of any central server or a super node. With others in the same system, the importance of each node. In this system, it has no single point of failure, the network is very strong [1] Availability: Agreement in constant state of change for the network, it works fine: nodes can always find the way to the result, if there is no network fault, must surely be the key nodes and a large number of nodes network without adding chords. [1] Scalability: Chord protocol can be used for very large systems, the cost of the logarithmic growth chords query protocol. [1] Load balance: Since nodes use a key distribution cord same hash function. Therefore, the key is uniformly distributed in the node [1]. Flexible naming: The core structure of the cord without any restrictions, so its name is a great flexibility in the amount of data. [1]

Basic Chord Algorithm Have some form of cord algorithm in this section is only a basic discussion of all form.

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) Chord algorithm [2] is a hash function, in network construction, resource discovery and resource information to join and processing nodes. The hash function can be changed at a fixed length output destination (node information, resource information). Hash function H (X) has the following properties: H (x), and can generate a fixed-length output, where H (x) the operation of the data of any length. H (x) can be readily calculated for any specific x, where the process is reversed apparatus gained almost impossible to XH. So it is clear from the attributes hash functions can ensure uniqueness of data.

Figure 1.2 polyphonic ringtones sent to each node needs information to their successors to store information. In Figure 1.3, M = 6, so Chord network size = 26 = 64, but in fact only 10 nodes. These nodes are aware of the successor nodes. 8 is the information stored 14s node node node 21 node 14 and the like.The search process continues polyphonic ringtones for each node and its successor.

2.2.1Topological structure of Chord Chord protocol service Stoica [3] proposed a distributed algorithm. Keywords are calculated taking into account the available data in the network resources and chords map contains peer. In topological string value of all peer> <IDK. IDK hash value is to find resources. Access to resources of the actual value of the storage location. It is necessary to hash the string and each node of the network resources. The M-bit binary identifier idk as a result of the hash function. Hash node is the IP address of the node ID, and international domain names (IDN) represent the number of resource hash key same name, and is expressed in the IDK. IDnis range [0,2 M-1], internationalized domain names (IDN) arranged in a circle in the form of small macrocycles. Figure 1.1 is an example of chord ring based on the basic chord algorithm with M=3.so maximum allowable node ID= (23=8).

FIGURE1.1: CHORD RING M=3

The Construction process: First, the node may need from a small ring to the line organization based IDNs (IDN). In Figure 1.1, the network nodes and keywords 0,1,3 1,2,4, IDK the IDN or equal to the first in a clockwise direction, the node is assigned to ID5.Keyword value. Successor nodes are called nodes IDK (successor (K)). As a follow-node (K) is equal to or less than the IDK IDK ring line, and set in a clockwise direction after the first node in the node. So, in the true capacity is a successor node (1) = 1, information, information resources on node 2 = 3 successor (2) Information Resource 4 and 5, the successor node (4); successor (5) = 0.

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FIGURE1.2: NODE 8 SEARCHING IDK=45 IN BASIC CHORD

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) Flow chart for above algorithm is shown in figure 1.3. According to flow chart in figure1.3: Receive any node (n) in question (IDK) first checks, if IDK IDK = IDN or <IDn,successor(n)> node, if it is real or the result of the requested node or query message than the real successor (N), repeat this process for each node.

order. Thus, the search time required node problem is the search. To search for the key that is located on the other side of the circular line, in the clockwise direction to scan network requirements. Therefore, the search process took a long time, it may reduce the effectiveness of the search process and the protocol string. Therefore, it is necessary to improve the string search algorithm to improve the efficiency and reduce the search time node in the ring. For our protocol strings need to consider the key elements to quickly find agreement and effectiveness. In chord protocol each peer maintains the routing information so it possible to use this routing information to improve the chord searches efficiency. Since time needed to search the key in any network can be as follows

____ (3.1) So reduction in the number of hops can reduce the search time. Chord protocol restricting the use of paper in this project. String algorithms to solve the problems described above. In this project two directed search, will achieve two-way pointer table. Find a way to use the process to reduce the search time in the pointer table polyphonic ringtones. In other words, the concept FIGURE1.3: LOOKUP PROCESS IN BASIC CHORD

is for each node in the routing table of the two existing tables: a first finger and the second finger table counter. By reverse pointer table each node can jump closer to the target peers.

The algorithm is a simple algorithm of the basic chords, and low efficiency because it requires multiple hops to reach the worst possible destination will node.tha n-1 times. Is an enhanced version of the chord algorithm, which is widely used now. The algorithm uses a fixed-length digital format, reducing the number of hops routing algorithm parameters.

There are two routing tables are implemented on each node

Figure table stores the successors and their key

2.2 Improvement of the chord algorithm

mapping for the node.

Because it is all ready to discuss your finger table maintenance agreements existing string and the search is completed clockwise

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node.

Anti finger table stores the predecessors of the

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Vol. 1 Issue II, September2013 ISSN: 2321-9653

I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) So finger table links the peer of the network in clockwise

Lookup algorithm

direction where as anti finger table links them in counter

In order not to change the original key and chord needs to

clockwise. The ways data are stored on

change data mapping algorithm search algorithm. In can see the

nodes remain same.

Hence, no change in the networkâ&#x20AC;&#x2122;s topological structure.

original algorithm, only the key chord successor. In the

So in this improved chord algorithm there are three

improved algorithm, we have a table to store the predecessor

things maintain by each node

node fingerprint resistant, so you can not use the previous search

Finger table

algorithm to find a unique key. Use key fingerprint resistant

Anti finger table

sheet than its predecessor is the search for a successor. The

Successor list

original mapping key to maintain data.

Finger table and successor list is same as the original chord algorithm. This thesis have introduced new anti

Sometimes, it does not require all of the search process: the use of anti fingers. Use only fingerprint resistant sheet perspective, if a node in this table exceeds the value of the key. Node, which is

finger table. There is no improvement if same half in ring have key

used

find

key

data.

.if key is in other half of the chord ring then has significant improvement in the lookup efficiency of the

If the resulting fingerprint resistant data in the table, then the table with your fingers and search algorithms, according to the

chord algorithm.

normal

power

cord.

facilities

clockwise

and

Following example explains the process

counterclockwise, the key can be found than the original

In figure 1.4 node 0 has predecessor node 6 and 7 and

algorithm.

node 0 is in the finger table of the both table and hence

2.4 Implementation

both node 6 and 7 are in the anti finger table of the node

This section will discuss the algorithm to modify the original string OpenChord discussion. Openchord chord algorithm is a simulation platform open source. Finger table does not change, so do not require any modification of the finger table definition. It is known that m finger table entries. Table entry is similar finger against the finger table also m. Algorithm used to calculate the number of hops the algorithm performance. Since implementation of the simulator used for small scale (<50 nodes), it is also based on the JVM (Java Virtual Machine). So polyphonic ringtones lookup speed is high, so we avoid the timer to calculate costs. Pseudo code of proposed algorithm is:

0. List1.1: Pseudo code of the modified chord algorithm

FIGURE 1.4: EXAMPLE

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Vol. 1 Issue II, September2013 ISSN: 2321-9653

I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) Node findPredecessor(key,n){

while(!retrieved){

Node pred=n.getPredecessor();

Node responsibleNode_R=null;

if (pred==null)

responsibleNode_R = findPredecessor(id);

return n; //n is the current node

hops_R+=1; //while not retrieve the desired key, add the hop counter by 1

else if (key.isInInterval(pred.ID, n.ID) //check if the key is between the pred and current node return n;

try{ result_R = responsibleNode_R.retrieveEntries(id); // get the responsibleNode to fetch the entry

else { Node n'=getClosestPrecedingNode(key) //if not, track the closest preceding node and lookup again

retrieved = true; //if successfully get the value, set retrieved state to true }catch(Exception e){}

return findPredecessor(key,n') // recursively find the predecessor of node n'

continue;

}

Node findSuccessor(key,n){

if(result_R !=null) values1.add(entry.getValue()); // add the lookup result to the valueset

Node succ=n.getSuccessor(); if (succ==null)

final_hopsR=hops_R; //get the hop counter for the current lookup operation

return n; //n is the current node else if (key.isInInterval(n.ID, succ.ID) //check if the key is between the current node and successor's node

return values1; }

return n;

Set<Serializable> retrieve(key){

else {

hops=0; //initialized the hops counter in finger table direction

Node n'=getClosestPrecedingNode(key) //if not, track the closest preceding node and lookup again return findSuccessor(key,n') // recursively find the predecessor of node n'

whild(!retrieved){ Node responsibleNode=null; responsibleNode = findSuccessor(id);

} hops+=1; //while not retrieve the desired key, add the hop counter by 1

} Set<Serializable> retrieve_R(key){

try{

hops_R=0; //initialized the hops counter in anti-finger table direction

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result = responsibleNode.retrieveEntries(id); // get the responsibleNode to fetch the entry

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) retrieved = true; //if successfully get the value, set retrieved state to true }catch(Exception e){} continue; } } if(result !=null) values.add(entry.getValue()); // add the lookup result to the valueset final_hops=hops; //get the hop counter for the current lookup operation

Using the above method using the X1 and X2 on the query to the next node in a clockwise direction and the counterclockwise direction finding. After that calculate the distance between X1 and X2, we look forward questions less than the distance from the destination node node. Like D1 = | X1-K | D2 = | X2-K |, if d1 is less than d2 question to the X2 X1 otherwise. By using this method selectable forward direction or in the other direction can be automated. 3.1 Results Open chords for the implementation and performance evaluation algorithms simulator modified chords. Will be used to improve the Chord algorithm and the original Chord algorithm to compare the performance of the two algorithms.

return values; }

Test Results

Obviously, the counter is set to jump from the pseudocode your search results, search and search clockwise counterclockwise operation mode. Return value jump, eventually leading to the search process. Combined value will be helpful, to see or modify the chord algorithm performance comparison. Choose whether to continue automatically between clockwise or counterclockwise problem, we just get the next hop address using a lookup table and table antifinger methods fingers original chord algorithm. List1.2: Pseudo code of lookup method of chord algorithm

Page 49

The tests results will be show in term of two values .which are as follows: a) Routing table size Modified algorithm routing table has no entry in the program size fingerprint resistant finger table and the total number of entries. Because it is all ready know, not the finger table entry is less than or equal to M. In the experiment, the network is to simulate different node numbers and calculate two tables fingers and fingerprint resistant entries in the table, in hops clockwise and counterclockwise. The size of each peer routing table almost 2M.

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) Figure1.5: comparison of routing table size for original and modified chord algorithm

TABLE1.1: EVALUATION ALGORITHM

TABLE

FOR

CHORD

b) Look up Hops In table 1.1 numbers of entries and size of the finger table are calculated and also calculated the hop count for different network size using original chord algorithm.

TABLE1.2: EVALUATION TABLE FOR MODIFIED CHORD ALGORITHM

FIGURE1.6: SCREENSHOT #2 Search process performance algorithm to calculate the modified string comparison, we chose the original chord find and modify the look and recorded on the same node and search the same key. Inspection process, for different network size.

Table 1.2 shows result for modified Chord algorithm. It is clear from table3 that, use of anti finger table, make it possible to reduce hop count to search any key. To get the better result test this in an environment which is capable of generates nodes equal to the actual network.

Page 50

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I N T E R N A T I O N A L J O U R N A L F O R R E S E A R C H I N A P P L I E D S C I E N C E AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T) FIGURE1.7: COMPARING HOP ORIGINAL AND MODIFIED CHORD

COUNT

FOR

Application, JOURNAL OF NETWORKS, VOL. 6, NO. 10, OCTOBER 2011.

In the graph in Figure 1.7 shows the algorithm less hops Cord better original Chord algorithm as the network size is increased compared. Thus, the experiment showed that the modified algorithm Cord cord than the original search algorithm improved efficiency, better performance is.

[3] Eng Keong Lua, Jon Crowcroft, Marcelo Pias, Ravi Sharma and Steven Lim, A Survey and Comparison of Peer-to-Peer Overlay Network Schemes,

4. Conclusion

IEEE

COMMUNICATIONS

SURVEY AND TUTORIAL, MARCH 2004

The original chord key table search algorithms use a finger in one direction ie clockwise. It takes more time to find any hops to search for a particular key is on the other side of the ring line thesis proposal Chord routing table search algorithm in both clockwise and counterclockwise directions. For this purpose, he used a finger fingerprint resistant tables and tables. Appearance chord algorithm efficiency.

[4] R. Denneman, P2P searching methods, research issues, solutions and their comparison, 11th Twente Student Conference on IT, Enschede, June 29th, 2009 [5] John Risson, Tim Moors,Survey of research towards robust peer-to-peer networks: Search

Open chords chord algorithm simulator for simulating changes. By simulating a modified conclusion Hops Chord algorithm,

methods,ScienceDirect, Computer Networks 50 (2006) 3485–3521

the need for specific search network to reduce the number of

[6] Chen Gang, Wu Guoxin, Yang Wang. “G-

keys. In Chapter 5, it shows that as the network size increases,

Chord: an improved routing algorithm for

more hops to search for the key ring lying on the other side of

Chord”.

the table by using only your fingers, but using table antifinger

(Natural Science Edition), Vol.37, 9-12. Jan.

can search for a particular key No fewer hops. Therefore, the

2007.

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university

proposed algorithm Chord chords algorithm efficiency and [7] Jiajing Li, Xu Dongyang. “An Optimized Chord

reduce the search time.

Algorithm for Accelerating the Query of Hot 5. References

Resources”.

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Science

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http://groups.yahoo.com/group/the gdf/files/ [10] Wang Biqing, He Peng. “L-Chord: Routing Model for Chord Based on Layer-Dividing”. International Conference on Computational Intelligence and Security, Harbin, China, Dec. 2007: 262-265. [11] Hongwei Chen, Zhiwei Ye. “BChord: Bidirectional routing DHT based on chord”. 12 th International

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