Volume 2, Spl. Issue 2 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
A survey on Energy Efficient Protocols LEACH, Fuzzy-based approach and Neural Network Technique in Wireless Sensor Network Shaveta Gupta1, Vinay Bhatia2 1,2
ECE Deptt. Baddi University, Baddi
shavetaqca@gmail.com, Vinay4research@gmail.com
Abstract— Wireless Sensor Networks (WSN) plays a very important role in transmitting the data from source to destination but energy consumption is one of the major challenges in these networks. WSN consists of hundreds to thousands of nodes which consume energy while transmitting the information and with a span of time whole energy get consumed and network life time gets reduced. Clustering and Cluster head (CH) selection are important parameters used to enhance the lifetime of the WSN. Clustering use two methods: rotating CH periodically in every round to distribute the energy consumption among nodes and the node with more residual energy becomes CH.This research paper is focused on the performance of the techniques used to enhance the energy efficiency in Wireless Sensor Networks (WSNs). LowEnergy Adaptive Clustering Hierarchy (LEACH), FuzzyBased and Neural Network are some of the important techniques used. MATLAB simulation tool is considered in this paper. Keywords: Cluster Head, Clustering, Energy Efficiency, Fuzzy, LEACH, Neural Network and WSNs.
. I. INTRODUCTION Wireless sensor network (WSN) is most important technology and is therefore widely used in today life in a wide area of different applications. The WSN sensor nodes operate on battery power which is often deployed in a complex, rough or sometimes in extreme environment and consists of hundreds to thousands of nodes so it becomes difficult to change the batteries. This problem has forced engineers /scientists to develop some protocols to minimize the energy consumption especially of the nodes in order to increase the lifetime of the whole network [1-2]. The main challenge in WSN is energy perspective. The WSNs used to collect data from the environment, process it and transmit to the base station (BS). This process consumes energy so a most important and crucial technique used is clustering and cluster head selection (CH). Various protocols are used to overcome the problem of energy constrain. Low Energy Adaptive Clustering Hierarchy (LEACH) is the first hierarchical cluster-based routing set technique used for energy optimization in WSNs. It involves hard clustering, CH selection based on energy and distance between CH and nodes. This distance is determined by Euclidean distance formula [1]. Head is dependable for designing and employing a TDMA It can be seen that the architecture of the WSN and communication between the nodes with neuron network in the brain. Artificial Neural Network (ANN) concept is used for data prediction and communication amongst the
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WSNs. The ANN has the capability to predict the data and helps to avoid unnecessary data communication and thus saving energy in WSNs Neural Networks is energy efficient approaches of WSN because of their simple and parallel distributed computation, distributed storage, data robustness, auto-classification of sensor nodes and sensor reading. The CH selection is not an easy task , fuzzy logic employed different fuzzy descriptors, including the residual energy, expected residual energy, number of neighboring nodes, distance from cluster centroid, and network traffics, and finally evaluated their performance. This paper attempts to study three important techniques LEACH, Neural approach and fuzzy based approach for improving WSN efficiency. II. ENERGY EFFICIENT TECHNIQUES C. LEACH LEACH is one of the most important, earliest, basic and dynamic hierarchical routing protocols used in WSNs to minimize the energy consumption of the nodes. The clusters are formed and one of the node acting as CH. If a CH remains fixed throughout the network lifetime then the CH would die quickly and ends the useful life of all nodes belonging to that particular cluster. Each node in a cluster elects itself as local CH at any round with a certain probability and broadcast its information to the other nodes [1]. In every cluster one of sensor nodes acts as CH and remaining sensor nodes act as member nodes of that cluster. CHs collect the data from all nodes, aggregate received data, process/compress them and finally transmit information to BS. LEACH uses a Time Division Multiple Access (TDMA)/code-division multiple access (CDMA) MAC to avoid or reduce inter-cluster and intra-cluster collisions. After a given interval of time, random rotation for the selection of CH is conducted so that uniform energy get consumed by every node in each cluster. The operation of LEACH consists of two phases, the setup phase and the steady state phase. Setup phase: The clusters are organized and CHs are selected. In the steady state phase, the actual data transfer to the BS takes place. The duration of the steady state phase is longer than the duration of the setup phase in order to minimize overhead. During the setup phase, a predetermined fraction of nodes ‘p’ elects themselves as
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Volume 2, Spl. Issue 2 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
CHs. A sensor node or set of nodes that have not been selected as a CH in the last (1/p) rounds chooses a random number between 0 and 1 and if this random number is less than a threshold value T(n) the node becomes a CH for the current round. The threshold value is calculated by T (n) = (p/ (1-p (mod (1/p))))
Table I. FUZZY MAPPING RULES
nâ‚Ź G
where G is the set of nodes that are involved in the CH election. All the nodes elected as CHs broadcast an advertisement message to the rest of the nodes in the network that they are the new CHs. All the non-CH nodes, after receiving this advertisement, decide on the cluster to which they want to belong on the basis of signal strength of the advertisement. The non-CH nodes inform the appropriate CHs that they will be a member of that particular cluster. After receiving all the messages from the nodes, the CH node creates a TDMA schedule and assigns each node a time slot when it can transmit. This schedule is broadcast to all the nodes in the cluster. Steady state phase: The sensor nodes start sensing and transmitting data to the CHs. The CH node receives all the data, aggregates it, process it and transmit it to the BS. After this process again the network goes back into the setup phase and again selects new CHs for next round. B. FUZZY- BASED Clustering techniques used in earlier protocols selects CHs with more residual energy and rotates CH periodically so as to distribute the energy consumption among nodes in each cluster and extend the network lifetime. The previous algorithms do not consider the expected residual energy, which is the predicated remaining energy for being selected as a CH and running a round. A fuzzy-logic-based clustering approach used predicted as well as residual energy to prolong the lifetime of WSNs. In fuzzy-logic, two input variables for the fuzzifier are the residual energy Eresidual and the expected residual energy EexpResidual and one output parameter is the probability of a node to be selected as a CH, named as chance. The bigger chance means that the node has more chance to become a CH. The EexpResidual is the difference between the Eresidual and Eexpconsumed while Eexpconsumed depends upon three factors: size of the data, distance to BS and number of neighbours (degree of nodes). The fuzzy mapping rules describes the variables as high, rather high, medium, rather low, low, and very low[3-4]. The fuzzy set that describes that if residual and expected residual energy (ERE) is high than chance to become CH is very high. If residual energy is high and expected residual energy (ERE) is medium than chance to become CH is high and so on. The chance for becoming CH is calculated by using predefined fuzzy if-then mapping rules to handle the uncertainty [5].A node having more residual energy and ERE has highest probability to become a CH. The CH selection is same as LEACH. Then, the nodes calculate the chance using the FIS and broadcasts a Candidate-Message with the chance. The nodes broadcasts the massage with a high chance also receives the massage from other nodes. The node having high chance elect itself as CH and rest of the nodes remains normal node.these normal nodes choose the nearest CH as its CH and sends a JOIN-REQ request to the
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CH then starts transmitting the data. Based on the two fuzzy input variables, 18 fuzzy mapping rules are discussed in table I [3-4]
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H - High RH - Rather High RL -Rather Low L - Low
M - Medium VL -Very Low
1. Energy Model The energy model used in LEACH and Fuzzy techniques is same. This model used to calculate the amount of energy consumed for transferring the data from simple node to CH, from CH to intermediate CH, from CH to BS and also from node to BS. The radio dissipation energy model consists of transmitter having transmit electronics (Eelec) which depends upon factors like coding, modulation, filtering and transmit the signal and amplifier S.NO
Residual ERE Chance Energy 1 H H VH 2 H M H 3 H L M 4 RH H H 5 RH M RH 6 RH L M 7 M H M 8 M M M 9 M L RL 10 RL H VL 11 RL M M 12 RL L RL 13 L H VL 14 L M VL 15 L L L 16 VL H VL 17 VL M VL 18 VL L VL depends on the distance to the receiver and the tolerable bit-error rate
.Fig. 1. Radio Energy Dissipation Model If the distance between transmitter and receiver is less than threshold distance (say do) then free space (d2 power loss) channel model used and if distance between transmitter and receiver is greater than threshold distance (say do) then multi path fading (d4 power loss) channel model used [1].
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e-ISSN: 1694-2310 | p-ISSN: 1694-2426
The energy consumed by the specific nodes/CH for transmitting k bits of data is: Energy consumed by transmitter (for d<do)
EexpResidual(l, dtoBS, n) = Eresidual − EexpConsumed where the Eresidual is the residual energy of a sensor node before the cluster head selection [3].
Etx(L, d) = Eelec * L + L * (Ef s * d2) Transmission energy for intermediate node Etx(L, d) = ((Eelec + EDA) * L) + (Ef s * L * d2). Energy consumed by transmitter (for d ≥ d o) Etx(L, d) = Eelec * L + L * (Emp * d4) Transmission energy for intermediate node Etx(L, d) = ((Eelec + EDA) * L) + (Emp * L * d4) Energy consumed by Receiver Fig. 2. Fuzzy-Logic based CH selection
Erx(L) = Eelec * L Table II contains first order radio model parameter used to calculate the energy consumed by each node in a cluster at various distances. Table II. Radio Parameters Parameters Operation Values Transmitter / Receiver Eelec 50 nJ/bit Electronics Transmit amplifier (if d to BS<do) Transmit amplifier (if d to BS>do) Data aggregation energy
Efs
10 pJ/bit/4m2
Emp
0.0013 pJ/bit/m4
EDA
5 nJ/bit/signal
C. NEURAL- BASED Neural Networks does not conserve energy but they can help energy conservation methods to work intelligently to better approach to their goals. However there is enough motivation to implement full ANNs on each single sensor node due to analogy between WSNs and ANN as in [6]. Neural Network based energy efficient approaches can also be classified according to the role Neural Networks play role in neural topologies applied. In (table.III) you can see this classification where first column represents the role that neural network play in, second column represents the appropriate topology used for it with related references and third column represents the energy conservation approach category which it applied to. S.No
2. Expected Residual Energy in Fuzzy logic As shown in fig (2), after the cluster formation, the steady-state operation is broken into data frames, nodes transmit their data to the CH during their allocated transmission slot. In a frame,a CH has say n cluster members, it would receive n messages from all the members and then transmit one combined message to the base station with a distance dtoBS. The number of frames could be obtained by Nframe = tssPhase/(n ∗ tslot + tCHtoBS) where tssPhase is the time of a node to be a CH), tslot is the slotted time required for the transmission from node to the CH, and tCHtoBS is the time required for the transmission from CH to the BS. The expected consumed energy of a node to be a CH after a steady-state phase could be calculated as EexpConsumed(l, dtoBS, n) = Nframe ∗ETx(l, dtoBS)+n ∗ERx(l) All the sensor nodes are assumed to transmit and receive l bits of data. The distance to the BS, dtoBS, could be computed based on the received signal strength. Then, the expected residual energy of a node to be a CH after a steady-state phase could be calculated via
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1
2
3
4
The role N.N can play N.N topology applied Energy efficient path discovery Energy efficient Nodes clustering Cluster head selection Data prediction
Energy conservation
category(ies)
SOM[79],BP[10]
Duty cyclingrouting
SOM [11-12]
Duty cyclingCluster-based routing
SOM[13]
Duty cyclingCluster-based routing Data reductiondata prediction and duty cycling
BP[14],
REFERENCES [1] W. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks, in: System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conferenceon, IEEE, 2000, pp. 10pp. [2] M. Aslam, M. B. Rasheed, T. Shah, A. Rahim, Z. A. Khan , U. Qasim ,M. W. Qasim,,A. Hassan, A. Khan, N. Javaid “Energy optimization and Performance Analysis of Cluster Based Routing Protocols Extended from LEACH for WSNs” International Journal of Modern
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Volume 2, Spl. Issue 2 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Engineering Research (IJMER) www.ijmer.com Vol.3, Issue.2, March-April. 2013 pp-1100-1104 [3] Jin-Shyan Lee and Wei-Liang Cheng “Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication” IEEE Sensor Journal, VOL. 12, NO. 9, September 2012 [4] I. Gupta, D. Riordan, and S. Sampalli, “Cluster-head election using fuzzy logic for wireless sensor networks,” in Proc. Annu. Conf. Commun. Netw.Services Res., 2005, pp. 255–260 . [5] Z. Wang, L. Liu, M. C. Zhou, and N. Ansari, “A position-based clustering technique for ad hoc intervehicle communication,” IEEE Trans. Syst. Man Cybern. C Appl. Rev., vol. 38, no. 2, pp. 201–208, Mar. 2008 [6] Neda Enami , Reza Askari ,Moghadam, Kourosh Dadashtabar, Mojtaba Hoseini “Neural Network based energy efficiency in wireless sensor network:A survey” International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.1, August 2010. [7] Barbancho J, Leon C, Molina F.J, Barbancho A, (2007) “ Using artificial intelligence in routing scheme for wireless networks”, Computer Communications 30, Elsevier, pp. 2802-2811 [8] Vesanto J, Alhoniemi E. (2000) “Clustering of Self Organizing Map”. IEEE Transactions on Neural Networks, Vol. 11, No. 3, pp. 586600. [9] Shahbazi, H., Araghizadeh, M.A., Dalvi, M., (2008) “Minimum Power Intelligent Routing In Wireless Sensors Networks Using Self Organizing Neural Networks”, IEEE International Symposium on Telecommunications, pp. 354--358 [10] Hosseingholizadeh Ahmad and Abhari Abdolreza (2009) “A neural network approach for Wireless sensor network power management” [11]Aslam N, Philips W, Robertson W, Siva Kumar SH, (2010) “A multicriterion optimization technique for energy efficient cluster formation in Wireless Sensor networks”, Information Fusion, Elsevier [12] Dehni L, Krief F, Bennani Y., (2005) “Power Control and Clustering in Wireless Sensor Networks”, Challenges in Ad Hoc Networking, vol 2005, pp.31-40. [13] Mei, L., Haihao, Li., Shen, Y., Fan, J., Huang, SH., (2009) “Elastic neural network method formulti-target tracking task allocation in wireless sensor network”, Computers and Mathematicswith Applications, Vol. 57, Issue .11-12, pp. 1822—1828 [14] Shen, Y., Guo, B., (2008) “Wavelet Neural Network Approach for Dynamic Power Management in Wireless Sensor Networks”, International Conference on Embedded Software and Systems(ICESS2008), pp.376—381.
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