Int. Journal of Electrical & Electronics Engg.
Vol. 2, Spl. Issue 1 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Implementing Energy Efficient Strategies in the MANET on-demand routing Protocols and comparing their performances 1
P.Sivasankar, 2G.A.Rathy 1,2
Assistant Professor, Electronics Department, Electrical Department NITTTR, Chennai, India.
1
2
1 siva_sankar123p@yahoo.com
ABSTRACT:-Mobile Ad-hoc networks are self-configuring multi-hop wireless networks where, the structure of the network changes dynamically. Because of the nodes in the MANET are mobile and battery operated, energy optimization is one of the major constraints in the MANET. Failure of some nodes operation can greatly impede the performance of the network and even affect the basic availability of the network, i.e., routing. To improve the lifetime of these networks can be improving the energy levels of the individual nodes of the network. This paper presents an analysis of the effects of different design choices for this ondemand routing protocols DSR and AODV in wireless ad hoc networks. In this paper, the energy efficient strategies are implemented in the AODV and DSR protocols to improve the life time of the Mobile ad hoc network. The CBEER-NN is developed using the existing DSR protocol and the AOEEDTR is developed using the existing AODV protocol. GloMoSIM simulator is used to simulate the proposed MANET environment. This paper also compares the existing DSR and AODV protocols with proposed CBEERNN and AO-EEDTR protocols. From the simulated results, this paper concludes that the proposed CBEER-NN and AOEEDTR protocols are improving the life time of the network by improving the average residual energy of the nodes over the existing DSR and AO-EEDTR protocols. Keywords: AODV, DSR, AO-EEDTR, CBEER-NN, Cache
INTRODUCTION Mobile ad hoc networks [1] (MANETs) are instantly deployable without any wired base station or fixed infrastructure. A node communicates directly with the nodes within radio range and indirectly with all others using a dynamically determined multi-hop route. A critical issue for MANETs is that the activity of nodes is energy-constrained. In the past few years, extensive research has been carried out in developing routing protocols for MANETs. Past research for reducing energy consumption has focused on the hardware and the operating system level. However, significant energy savings can be obtained at the routing level by designing minimum energy routing protocols that take into consideration the energy costs of a route when choosing the appropriate route. This paper is worked on the network layer/routing layer & Radio layer and focuses on design and implementation of Cluster Based Energy Efficient Routing using Neural Networks(CBEER-NN) in the existing DSR protocol and AODV based Energy Efficient Delay Time Routing(AO-EEDTR) in the existing AODV protocol. These algorithms are designed and implemented using Global Mobile Simulator 111
(GloMoSim). Also the performance of the protocol is evaluated and compared with the existing DSR and AODV protocols. DSR AND AODV PROTOCOLS In this section, the existing on-demand routing protocols DSR and AODV protocols and their route discovery, route maintenance procedures will be discussed. DSR Protocol DSR [2] is an on demand, source routing protocol, with each packet carrying in its header the complete, ordered list of nodes through which the packet will be routed. DSR consists of two mechanisms: route discovery and route maintenance. When a node s has a packet to send for which it does not have a route, it initiates route discovery by broadcasting a route request (RREQ). The request is propagated in a controlled manner through the network until it reaches either the destination node T or some intermediate node, n, that knows of a route to node T. Node T then sends a route reply (RREP) to node s with the new route. In this case that multiple routes are located (i.e., multiple route replies are received), nodes s selects the one with the best metric (e.g., hop count). Route Maintenance is the mechanism by which a node detects whether or not a route kept in its cache has become stale as result of host mobility and topology change. When an (intermediate) node n detects that the next link in a packets route is broken, it first sends a route error (RERR) message to the source node s that generated the packets route. AODV Protocol Ad hoc on-demand distance vector in [2] (AODV) routing protocol uses an on-demand approach for finding routes, that is, a route is established only when it is required by a source node for transmitting data packets. It employs destination sequence numbers to identify the most recent path. The major difference between AODV and DSR stems out from the fact that DSR uses source routing in which a packet carries the complete path to be traversed. However, in AODV, the source node and the intermediate nodes store the next-hop information corresponding to each flow for data packet transmission. In an on-demand routing protocol, the source node floods the Route request packet in the network when a route is not available for the desired destination. It may obtain multiple routes to different destinations from a single Route request. The major difference between AODV and other on-demand routing protocols is that it uses a destination sequence number to determine an up-to-date path to the destination. A node updates its path information only if the NITTTR, Chandigarh
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Int. Journal of Electrical & Electronics Engg.
Vol. 2, Spl. Issue 1 (2015)
destination sequence number of the current packet received is greater than the last destination sequence number stored at the node. When an intermediate note receives a Route request, it either forwards it or prepares a Route reply if it has valid route to the destination. The validity of a route at the intermediate node is determined by comparing the sequence number at the intermediate node with the destination sequence number in the route request packet. CBEER-NN AND AO-EEDTR In this section, the proposed modified energy efficient routing algorithms [3,5] for MANET using DSR and AODV protocols will be discussed. The Cluster Based Energy Efficient Routing using Neural Networks(CBEERNN) algorithm is implemented in existing DSR protocol and the AODV based Energy Efficient Delay Time Routing(AO-EEDTR) Algorithm is implemented in existing AODV protocol. CBEER-NN This algorithm is implemented in DSR protocol to find energy efficient route between the source and the destination nodes. In this algorithm, selection of routes should be based on the remaining battery energy level of the node. This CBEER-NN algorithm selects the route based on the Energy metrics of a node participating in the mobile network to route and deliver the packet to the destination. The CBEER-NN protocol is designed to bring about energy aware route establishment in order to avoid the full drain of the energy from a node, which in the network forms a gateway to the other zones [7]. The proposed algorithm differs from the existing DSR protocol in the route discovery and energy aware route maintenance with higher percentage of reliable delivery of packets. The energy efficient route establishment in the CBEER-NN algorithm is described in the following section. . Energy Efficient Route Establishment The network is formed by the “divide and rule” policy for the nodes to deliver the packets to the destination. The tree structure with virtual backbone is used to deliver the packets reliably to the destination and optimized use of energy in the network. A node on entry to the network gets itself associated to one of the root if its energy level [8] is lesser than the root node else it will act as a root and the node which was a root becomes a leaf node. A virtual backbone is formed with the nodes having the highest energy in the domain to establish routes from one node at one end to other end. The Route discovery and maintenance phases of CBEERNN protocol is discuss in the following sections. 3.1.2. Route Discovery Phase The DSR protocol broadcasts a route discovery packet and the reply is formulated by the node, which has entry of the destination node in its cache or the destination replies with a route reply packet. The proposed algorithm makes sure that the route discovery packet is forwarded to its root and if the root has the cache entry for the packet it will reply back with the route reply or else it will in turn forward the packet to its root. Finally if the route is nonexistent till the root node, which participates in the backbone formation, then the route discovery packet is sent through the backbone to the other domain nodes, which may reply back with a route reply. Thus on NITTTR, Chandigarh
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e-ISSN: 1694-2310 | p-ISSN: 1694-2426
establishing a route, the route reply containing the route back to the source is routed back. Thus if the destination node is not existent in the domain then after a time out and resend attempts, the source node could find that the destination node is unreachable. 3.1.3. Route Maintenance Phase The CBEER-NN algorithm differs considerably from DSR in Route Maintenance. The route maintenance is easier as the hello packet which contains the cache contains of the node can be interchanged between the leaf node and root nodes. This will ensure that the route is existent and the route reply can be generated from the reference of the cache. The energy based tree formation ensures the participation of nodes in the network though their power remaining is less, by reception of packets intended to them and transmission of packets (acting as a source) but not as a router. A field namely the ROOT_NODE or LEAF_NODE need to be interchanged between the root and the leaf nodes (within the range of the root node) with the sequence number for tracking of the route and identify to which root the node belongs to. 3.1.4. Energy Efficient Cluster Head Selection using Neural Networks A five layered feed forward neural network is used to predict the final energy level of the individual nodes in the cluster. The input patterns belong to one wireless node and by using them as the inputs of the neural network can predict the energy level of the mobile node at the last of network lifetime. These patterns may be in the form of features coded from node’s distance from sink, node’s distance from the neighboring border, node’s number of neighbors, the number of neighbors which initially route their data through this node. After deploying nodes, the base station receives nodes positions and neighbor’s information, thus it can easily calculate these patterns for each node and the neural network can be able to predict their final energy level. A well-trained neural network would be able to receive each node’s features as the inputs and predict its final energy level. Thus, the neural network is used to increase the life time of the network. Selecting Group Heads amongst all the nodes is also energy conserving scheme. Each node collects data which are typically associated with other nodes in its neighborhood, and then the associated data is sent to the Base Station through Group Head for evaluating the tasks more efficiently. Assuming the periodic sensing of same period for all the nodes and Group Head is selected. Inside each fixed group of nodes, a node is periodically elected to act as Group Head through which communication to/from Group nodes takes place. The set of Group Head nodes can be selected on the basis of the routing cost metric explored by the equation: RCM = Ek/Ar{ ET(NkS,NmD)+ER (NkS,NmD)}------ 1 Where, Ek be the energy associated with the delivery ratio of the packet, delivered correctly from source node NS to the destination node ND ET(NkS,NmD) energy transmitted from NS and ER (NkS,NmD) is the energy received at ND, Ar be the range area of the network. AO-EEDTR 112
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The basic idea behind this AODV based Energy efficient delay time routing (AO-EEDTR) algorithm is to utilize a longer and more energy efficient routing [6] path instead of using a lesser energy efficient and shorter path. The Energy efficient delay time routing algorithm is based on the AODV protocol. The Route Discovery in the AODV protocol is modified so as to enable the selection of the most energy efficient routes [9] by the source nodes. The Route Maintenance is essentially the same as in AODV. 3.2.1. Route Discovery Phase Generally in on-demand routing algorithm, when a source needs to know the route to a destination, it broadcasts a RREQ packet. The neighboring nodes on receiving the first-arrived RREQ packet relay this packet immediately to their neighbors. But in our algorithm, this packet relaying does not occur immediately. The idea of EEDTR algorithm is as follows: Upon receiving a request packet, each node first holds the packet for a period of time, which is inversely proportional to its current energy level. After this delay, the node forwards the request packet. This simple delay mechanism is motivated by the fact that the destination accepts only the first request packet and discards other duplicate requests. With this delay mechanism, request packets from nodes with lower energy levels are transmitted after a larger delay, whereas the request packets from nodes with higher energy levels are transmitted after a smaller delay. Some nodes may receive several copies of the same RREQ packet from other neighbors. In AO-EEDTR, the duplicate copies of the same RREQ packets would be dropped as in the original AODV protocol. Fig. 1. Ilustrates AO-EEDTR algorithm. In the figure, it is assumed that the initial maximum battery capacity of all nodes is 10. The remaining energy levels after a finite amount of time are shown in Figure alongside the nodes. Due to transmission range limitations, nodes A and B can transmit the packet only to nodes C and D, respectively. The residual battery capacities of A and B nodes are same, so they flood the RREQ packets at the same time. We may ignore the travel time between nodes, without loss of generality. Since node D has more residual battery capacity than node C, other neighbors that can communicate with both nodes C and D first receive the RREQ packet from node D (because of the inverse delay). The process repeats until the RREQ packet arrives at the destination. In this figure, the destination node receives the following routes: (S-B-C-E-T), (S-A-D-F-T) and (S-A-D-G-T). Normally the route with the least hop is selected. But with AO-EEDTR, the route for communication from node S to node T is chosen as (S-AD-F-T), since nodes with lesser energy level delay the packet more than others. Thus a energy efficient path is chosen. Note that implementation of the algorithm requires minimal modification at local nodes by adding a delay mechanism. However, the penalty of this protocol is introduction of delay in route discovery procedure. Various delay functions which map energy value into delay, will be evaluated in the simulation section that follows. The selected route (S-A-D-F-T) may not always guarantee the total minimum energy, partially because it does not consider the number of hops in the route. Nevertheless, simulation results showed that AO-EEDTR prolongs the network lifetime significantly. 113
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Fig. 1. AO-EEDTR algorithm
In the algorithm the delay incorporated by each of the nodes is inversely proportional to the remaining energy level of each of the corresponding nodes. Following linear function incorporates the inverse proportionality Delay di = TM – (TM * er) /EM------------2 Where, di Delay to be introduced TM Maximum delay possible er Remaining energy of a node EM Maximal energy possible for a node RESULT AND ANALYSIS Simulation tool : GLOMOSIM GloMoSim in [4] (Global Mobile Information System Simulator) is a scalable simulation environment that effectively utilizes parallel execution to reduce the simulation time of detailed high-fidelity models of large communication networks. GloMoSim is a scalable simulation library for wireless network systems built using the PARSEC simulation environment. Glomosim can be modified to add new protocols and applications to the library. Therefore Glomosim is a good choice for implementing the different traffic sources. GloMoSim is aimed at simulating models that may contain as many as 100,000 mobile nodes with a reasonable execution time; this is done by using node aggregation. Performance Metrics The various parameters that were measured during the simulation are as follows: Packet Delivery Ratio: It is defined as the ratio of number of packets received to that of the number of packets sent. Routing overhead: It is defined as the sum of number of route requests, route replies & route errors. End to End Delay: It is the overall average delay experienced by a packet from the source to that of the destination. Average Residual Energy: It is taken as the average of the remaining energy levels of all the nodes in the network. These metrics were measured by varying the following three parameters 1. Number of Nodes 2. Speed(m/sec) 3. Number of Source Destination Pairs Simulation Results and Comparision Fig. 2 shows that the proposed CBEER-NN and AOEEDTR algorithms are performing well compared to the existing DSR and AODV protocols for maintaining the average residual energy in each of their nodes even if the Number of nodes pairs increased. Similarly the average residual energy in each of their nodes are improved in the proposed AO-EEDTR and CBEER-NN over the NITTTR, Chandigarh
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existing AODV and DSR protocols, though the nodes mobility or speed increased. This is shown in Fig. 3 by comparing the average residual energy for varying the nodes mobility. Avg. Residual Energy (mWhr)
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Fig. 5 shows that the proposed algorithms produces more End to End delay over the existing protocols because not following the shortest hop path. Fig. 6. shows the Control Overhead with Number of Nodes. It indicates that the Overhead increases as the Number of Nodes increases, due to increase in number of route requests and number of route replies flooded in the network. Also it is concluded that the CBEER-NN and AO-EEDTR algorithms are working well when compared to existing DSR and AODV protocols. No. of Nodes Vs End to End Delay
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CONCLUSION The DSR and AODV protocols have been implemented and compared with the modified energy aware CBEERNN and AO-EEDTR protocol and it is observed to have NITTTR, Chandigarh
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improved performances of the ad-hoc network. It is found that the modified algorithms have the comparable performance with respect to Average Residual Energy, Packet Delivery Ratio and Overhead with the existing DSR and AODV protocols. It has also been observed that by implementing a proper standardized energy model in the existing DSR and AODV protocols, our CBEER-NN and AO-EEDTR protocols are feasible and capable of better energy performance than the preset DSR and AODV protocols. REFERENCES 1. Journal Papers [1] Marco Conti and Silvia Giordano “ Multihop Ad Hoc Networking: The Theory”, IEEE communication Magazine, 2007. [2] X.Hong,K.Xu and Gerla, “Scalabe Routing Protocols for MANET ”, IEEE network vol 16, pp 11-21, 2002. [3] Shio Kumar Singh, D K Singh and M P Singh,. “A Survey of EnergyEfficient Hierarchical Cluster-Based Routing in Wireless Sensor Networks, Int. J. of Advanced Networking and Applications”, Volume: 02, Issue: 02, Pages: 570-580, 2010. [4] Rajiv Bagrodia, Xiang zeng and Mario Gerla, “Glomosim: A library for simulation of wireless networks”, University of califrnia ,Los Angels. 2. Conference Proceedings [5] C. Jinshong Hwang, Ashwani Kush, Sunil Taneja,. “Making MANET Energy Efficient, IEEE”, 2011 [6] Eei Yu and jangwon Lee, “DSR based Energy aware Routing Protocols in Ad Hoc networks”, IEEE,2003 [7] H.safa,Mirza.O, and Artail.H, “A Dynamic Energy Efficient Clustering Algorithm for MANET.IEEE International Conference on Wireless and Mobile Computing, 2008 [8] Young-sam Kim, Kyung-min Doo, and Kang-whan Lee,. “A Study on the Synchronization Clustering Control for MANET, ICCIT, IEEE. 2008. [9] Avni Khatkar, Yudhvir Singh, and Rohtak, “Performance Evaluation of Hybrid Routing Protocols in Mobile Adhoc Networks”, 2012 Second International Conference on Advanced Computing & Communication Technologies. 2012 [10] Imane M. A. Fahrnv, Hesham A. Hefny and Laila Nassef,. PEEBR: “Predictive Energy Efficient Bee Routing Algorithm for Ad-hoc Wireless Mobile Networks, The 8th International Conference on INFOrmatics and Systems (INFOS2012) , Computer Networks Track”, 2012
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