Paper Title (use style: paper title)

Implementing a New Power Aware Routing Algorithm Based on Existing DSR Protocol for MANETs

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Shivashankar1, B.Sivakumar1, G.Varaprasad21Dr.Ambedkar Institute of Technology, Bangalore 560 056, India.2B.M.S.College of Engineering, Bangalore 560 019, India.nshivu_ss@rediffmail.com, sivabs2000@yahoo.co.uk, varaprasad555555@yahoo.co.in

Abstract:- Energy consumption is a crucial design concern in mobile ad hoc networks, since nodes are powered by batteries with limited energy, whereas in existing DSR protocol does not take the energy limitation of MANET nodes into account. In this paper, we propose an efficient algorithm for mobile ad hoc networks, which maximizes the network lifetime by minimizing the power consumption while establishment path with the help of modified dynamic source routing. The design objective of modifying DSR is to select energy-efficient paths. The main features of modified DSR are:(1) Minimize energy consumed per packet(2) Maximize network life time for network(3) Minimize variance in node power levels and(4) Minimize maximum node cost. However, some intermediate nodes might act selfishly and drop packets for other nodes in order to save their own battery power. The proposed algorithm can find selfish(critical) nodes and deal with them by using a modified DSR protocol, which we call as a Efficient Dynamic Source Routing(EDSR). The simulation results show an increase in the packet delivery ratio in the network. The average node lifetime of proposed EDSR model is 45% to 60% longer than that of DSR model.Keywords:- Energy efficiency, MANET, DSR, EDSR, Network lifetime. Introduction

The nodes (mobile hosts) in an Adhoc network are constrained by battery power for their operation. To route a packet from a source to a destination involves a sufficient number of intermediate nodes. Hence, battery power of a node is a precious resource that must be used efficiently in order to avoid early termination of a node or a network. Hence, power consumption and clock frequency are important criteria in designing these hosts. Apart from static design optimizations of the hosts, it is possible to improve the performance and lifetime of network of such hosts by employing dynamic resource and power management. It is evident that minimizing the power consumption or maximizing the service speed of any given node may not be sufficient to achieve the lowest latency and longest lifetime of the network. The key factor is that in the network of nodes power consumption should be uniformly distributed among all nodes to increase the network lifetime.

The DSR protocol is a simple and efficient routing protocol designed specifically for use in multi-hop wireless ad hoc networks. The DSR allows the network to be completely self-organizing and self-configuring, without the need for any existing network infrastructure or administration. The protocol is composed of the two mechanisms of route discovery and route maintenance, which work together to allow nodes to discover and maintain source routes to arbitrary destinations in the ad hoc network.

Caching is an important part of any on-demand routing protocol for wireless ad hoc networks. In an ad hoc network, all nodes cooperate in order to dynamically establish and maintain routing in the network, forwarding packets for each other to allow communication between nodes not directly within wireless transmission range. Rather than using the periodic or background exchange of routing information common inmost routing protocols, an on-demand routing protocol is one that searches for and attempts to discover a route to some destination node only when a sending node originates a data packet addressed to that node. In order to avoid the need for such a route discovery to be performed before each data packet is sent, an on-demand routing protocol must cache routes previously discovered. Such caching then introduces the problem of proper strategies for managing the structure and contents of this cache as nodes in the network move in and out of wireless transmission range of one another, possibly invalidating some cached routing information. This paper defines an extension of DSR protocol that allows the routing of most packets without an explicit source route header. Further it reduces the overhead of the protocol while preserving the fundamental properties of DSR's operation. Once sending node has discovered a source route through DSR's route discovery mechanism, the flow state mechanism of EDSR allows the source node to establish hop-by-hop forwarding state within the network. Based on this, the source route, each node is enabled to forward the packet to the next hop. Flow state is dynamically initialized by the first packet using a source route and is then able to route subsequent packets along the same flow without use of source route header in the packet. The state established at each hop along a flow is soft state and thus automatically expires when no longer needed.

Of course, care must be exercised to ensure that the resulting latency for servicing requests meets an upper bound constraint. Wireless devices especially in ad hoc networks are typically battery-powered. The growing need for energy efficiency in wireless networks, in general, and in MANETs, in particular, calls for power enhancement features. The goal of this dissertation is to extend the network lifetime by improving energy utilization in MANET routing. The network model we consider is the wireless ad-hoc network, consisting of a set of nodes connected by wireless links. The topology of the network is not under our control, but is determined purely by the current geographic location of the nodes, other environmental conditions and the characteristics of the radio transceivers that the nodes possess. The nodes wish to communicate among each other. We assume that they are willing to relay packets in order to facilitate this communication. The problem is to design effective routing protocols to meet a variety of performance objectives.

The aim of this paper is to extend the network lifetime by improving the power utilization of the routing mechanism in MANETs. The rest of the paper is organized as follows. In section II, existing routing algorithms and various challenges are explained. Related previous research work are discussed in section III, describe. In section IV, it considers few power-aware routing metrics in the proposed algorithm. Simulation set up for the performance evaluation and justifies the choice of simulation parameters using NS-2.33 are presented in section V. Section VI concludes the paper. Routing Protocols and ChallengesReactive Routing Protocols

The common reactive-routing protocols used by MANET are DSR, AODV, TORA, ABR, SSA, LAR etc. The DSR protocols is a on-demand routing protocol, the difference between the DSR and other on-demand routing protocols is that in DSR, the source node completely specify the route to be taken by the packet between the source and destination. Furthermore, the DSR is beacon-less and thus does not require transmissions of periodic hello packet, which a node uses to inform its neighbors of its existence. The DSR limit the congestion consumed via control packets in the MANET by eliminating the periodic table-update messages necessary in the table-driven approach and thus increases the throughput. The DSR protocol consists of the two main operations of route discovery and route maintenance. A source node initiates a route discovery process by broadcasting a Route Request(RREQ) message in its locality. The source node adds its address and target address in the RREQ packet. When an intermediate node receives the RREQ packet and not having route to the destination in its cache, appends its own address in the RREQ packet and broadcast. As soon as the RREQ packet arrives at the destination node, the destination node throws a Route Reply(RREP) message back to the source. The RREP containing the accumulated list of intermediate nodes address, which RREQ has traversed. In case of network disjoint, the route maintenance phase is initiated whereby the route error packets are generated at a node. The erroneous hop will be removed from the node's route cache. All routes containing this erroneous hop are truncated. Again, the route discovery phase is initiated to determine the most viable route. In route maintenance process, the link-break and topology-change related things are being done. Fig.1 shows routing mechanism in the existing DSR without power aware metric. Proactive Routing ProtocolsIn proactive routing, the nodes periodically monitor the network for changes in the network topology. Therefore, every node in the network keeps an up-to-date copy information by periodically broadcasting and receiving control packets. For instance, when a node receives a packet destined to another node, it knows how and where to forward the packet for final delivery. This relatively detailed information about the topology helps to improve the routing performance. However, this improvement in routing may come at a cost of increased overhead and a decrease in network capacity for data. Several proactive-routing algorithms exist for MANETs. Two commonly referenced are Optimized Link State Routing(OLSR) and Destination Sequenced Distance Vector Routing(DSDV). The way in which network topology information is gathered in proactive routing protocols is usually based on either of two algorithms, link state or distance vector.

Fig.1. Routing mechanism in DSR with RREQ packet.Challenges

Of all potential wireless applications, wireless ad-hoc networks are special due to their self-configurable characteristics to form a network without the aid of any established infrastructure and their emphasis on communication between devices. Multi-hop routing, whereby intermediate nodes relay packets towards their final destination. They can improve the throughput and energy efficiency of the network as shown in fig.2. These networks have hard energy constraints, since each node is powered by a small battery that may not be rechargeable or renewable.

Fig.2. A typical multi-hop-routing network.

Hence, the nodes can only transmit a finite number of bits before they run out of energy. Reducing the energy consumption per bit for end-to-end data transmission is the most important design consideration for such networks. In addition, efficient use of the available bandwidth for end-to-end transmission must be exploited to optimize the system performance. These two goals of minimizing energy consumption and maximizing bandwidth utilization cannot be achieved simultaneously. Hence, it motivates analyzing the energy-bandwidth tradeoff in wireless multi-hop networks. Using multi-hop routing mechanism of DSR as shown in Fig.2, we can calculate energy rate tradeoff for all sensible routes which does not provide negative progress in reaching the destination such as SN45DN. At high rates, we find that fewer hops require less energy than more hops, while at low rates the reverse is true. Existing research work

In the last few years, thanks to the proliferation of wireless devices, the use of mobile networks is growing very fast. In particular, a very large number of recent studies focused on MANETs[1-2]. A MANET is a network without fixed infrastructure, in which every node can act as a router. This is required when the two end-points interchanging data are not directly within their radio range[6]. This kind of network, self-organizing and self-reconfiguring, is very useful when it is not economically practical or physically possible to provide a wired networking infrastructure. The performance of a mobile ad hoc network depends on the routing scheme employed, but the traditional routing protocols do not work efficiently in the MANET. This kind of network, in fact, has a dynamic topology and a limited bandwidth[5].

To limit the communication overhead of RREQ packets, a node processes route request packets that both it has not seen before and its address is not presented in the route record field [15]. If the RREQ packet reaches destination or an intermediate node has routing information, then RREP packet is generated. When the RREP packet is generated by the destination, it comprises addresses of nodes that have been traversed by the RREQ packet. Otherwise, the RREP packet comprises the addresses of nodes the RREQ packet has traversed concatenated with the route in the intermediate nodes route cache.

In[3], the authors have proposed Power Aware Multiple Access(PAMAS) protocol, where a node can switch off its radio link for a specific duration of time, if it perceives that it would not be able to send or receive packets due to multiple access interferences. Authors in[12] have introduced power-aware metrics resulting in power-efficient routes. Such metrics include maximizing the time of network partition and reducing the variance in power levels of nodes. These metrics can be directly implemented in the network with a centralized control. The routing algorithm, used here is based on minimizing the power level to transmit a packet between the source and destination. One such routing algorithm proposed by authors in[9], is conditional max-min battery capacity routing algorithm. This algorithm chooses a route with minimal transmission power where all the nodes along the route calculate the remaining battery capacity which is higher than a predefined threshold.

For instance in[7], by observing local and global topology information, the transmission power is changed while maintaining a connected topology. The node battery life is extended by using the radios minimum power level. However, in sparse networks, there may be network partition and high end-to-end delay, while a dense network can cause limited spatial reuse and network capacity. In[8], a distributed-power-control scheme is proposed, in which power control level is established by exchanging control messages, according to the estimated minimum and maximum power level. There will be frequent link ups and downs, causing more link errors from MAC layer due to interference and unexpected channel collision. Retransmission due to link breakage will consume extra energy and bandwidth[13].

The cluster-head is coordinator of all transmissions within the cluster, so it handles the inter-cluster traffic and also delivers the packets destined for the cluster etc. Obviously, these cluster-heads would experience a very high-energy consumption thereby leading to exhaust their energy resources more quickly than the ordinary nodes. It is therefore required that the cluster-heads' energy consumption be minimized thus maximizing the network lifetime [4],[10],[12],[14].

Shivashankar et. al[16] have concluded that the DSR routing protocol works better for smaller networks, but not for larger networks as it consumes less bandwidth and lower overhead when compared with DSDV and AODV routing protocols. The performance of DSR protocol is particularly noteworthy when TCP Reno with no enhancement technique is used. It indicates that for consistently high performance, some form of proactive route maintenance should be considered to compliment the route discovery used in the on-demand routing protocols. Metrics for Power-Aware RoutingLifetime Definition

The network lifetime is the time span from the deployment to instant when the network is considered nonfunctional. Our goal is to derive a general formula for network lifetime, which holds independently of the underlying network model. It should allow us to identify key parameters that affect the network lifetime without worrying about specific network settings. As a result, it can provide design guidelines applicable to various types of wireless networks.Network Life Time For Adhoc NetworkIn a MANET with total non-rechargeable initial energy 0, the average network lifetime E[L], measured as the average amount of time until the network dies, is given by

E[L]=(0-E[Ew]) /(Pc+E[Er]) ---------(1)

Here, Pc is the constant continuous power consumption over the whole network, E[Ew] is the expected wasted energy (i.e. the total unused energy in the network when it dies). is the average node reporting rate defined as the number of data collections per unit time. E[Er] is the expected reporting energy consumed by all nodes in a randomly chosen data collection. The trials on the same MANET to record the network lifetime L, the wasted energy Ew and the energy consumption in each data collection Ei has been done. For mth trial (1mM), we can write total energy consumed during the whole lifetime as

0 E(m)w = PcL(m) + --------- (2)

In equation(2), N(m) is the number of data collections during the network lifetime of the mth trial. In equation(1), it provides a quantitative characterization of key components that affect network lifetime under a general network setting. Specifically, a lifetime maximizing protocol should aim at reducing the average wasted energy E[Ew] and the average reporting energy E[Er]. To reduce E[Ew], the protocol should exploit the residual energy information of individual node to achieve balanced energy consumption across the network. To reduce E[Er], the protocol should exploit channel state information to prioritize nodes with better channels for transmissions, thus reduce the energy consumed in transmission. We point out that in equation(2), we can be easily extended to include other energy consumption sources.

The lifetime of the network is defined as the time at which the first node failures, that is, the time at which some nodes energy reserve is reduced to zero. Our goal is to route packets in such a manner that the lifetime is maximized, while the throughput requirements are satisfied. We denote the lifetime by T. We define a new variable Fij,c denoting total number of packets for connection c transmitted from nodes i to j over the lifetime of the network. The total energy consumed at node i is given by

Te=j,c Ei,j Fi,jC --------- (3)

Where, the summation is taken for all the nodes adjacent to i by considering its connections c. At every node except the source and destination for a particular connection c, the number of packets received must equal to the number of packets transmitted.Route Discovery Mechanism in Existing DSRThe source node when needs to send packet to destination node, starts the route-discovery procedure by sending the RREQ packet to all its neighbors. In this strategy, the source is not allowed to maintain route cache for longer time, as network conditions change very frequently in terms of position and energy levels of the nodes. Thus, when a nodes needs route to the destination, it initiates the RREQ packet, which is broadcasted to all the neighbors which satisfy the broadcasting condition. The RREQ packet of the DSR protocol is extended as RREQ packet by adding three extra fields for the modified DSR as LSD, energy model and bandwidth(B) as shown in the DSR header table 1. The RREQ packet contains type field, source-address field, destination-address field, unique-identification number field, hop-count field, LSD, bandwidth, time-to-live field, energy model and path fields.

Type(T) field: It indicates the type of packet.

Source Address(SA) field: It carries the source node address.

ID field: Unique identification number is generated by the source to identify the packet.

Destination Address(DA) field: It carries the destination address of node.

Time-To-Live(TTL) field: It is used to limit the lifetime of packet, initially, by default it contains zero.

Hop field: It carries the hop count. The value of hop-count is incremented by one for each node through which packet passes. Initially, by default this field contains zero value.

LSD field: When packet passes through a node, its LSD value with the node from which it has received this packet is also updated in the LSD field. Initially, by default this field contains zero value. Bandwidth field(B): It carries the cumulative bandwidth of links through which it passes. Initially, by default this field contains zero value.

Path field: It carries the path accumulations, when packet passes through a node. Its address is appended at end of this field.

Energy model: It is an extended metric to convert existing DSR protocol into power aware DSR protocol to include the battery power of each mobile node in the network topology.

Table 1. DSR Header TableSADATIDTTLHOPSBLSDPATHEnergy Model

Minimize energy consumed per packetConsider the network illustrated in fig.3. Here, node 6 will be selected as the route for packets going from 03, 14 and 25. As a result node 6 will expand its battery resources at a faster rate than the other nodes in the network and will be the first to die.

Let T(ni,ni+1)= energy consumed in transmitting and receiving one packet over one hop from ni to ni+1

ej=k-1i=1T(ni, ni+1) --------- (4)

Where ej is the total energy spent for packet j. Minimize ej for all packets j. In lightly loaded networks, this automatically finds shortest hop path. In heavily loaded networks, due to contention it might not be shortest.

Fig.3. Shortest-hop routing used in DSR protocol.Minimize Variance in Node Power LevelsThis metric is used to distribute load among all nodes so that the power consumption remains uniform to all nodes. This problem is very complex when the rate and size of the data packets vary. When every node has the same level in power, we can be sure that the network functions longer. When there is a node which has to switch off because of its power level, the whole network is in danger as it can break down if a node is a critical(important node). To keep all the nodes active as long as possible, a new route is established that considers the amount of data to be transmitted. Achieve some kind of load balancing to ensure similar rates of dissipation of energy throughout the network.Minimize Maximum Node Cost

In order to maximize the lifetime of all nodes in the network, the metrics other than energy consumed per packet need to be used. The path selected when using these metrics should be such that nodes with depleted energy reserves do not lie on many path. This metric ensures that the node failure is delayed. Unfortunately, there is no way to implement this metric directly in a routing protocol. However, the minimizing cost per packet does significantly reduce the maximum node cost in the network.

Let ci(t)=cost of routing a packet through node i at time t.(t)=maximum of the Ci(t)s, minimize (t), for all t>0.Therefore, total cost of sending packet j is

cj=k-1i=1fi(xi) --------- (5)

In equation(5), xi is the energy dissipated in node i. Let fi(xi ) is the cost of node i, then

fi(xi)=1/(1g(xi)) --------- (6)

Here, g(xi) is the normalized battery capacity. Minimize cost of sending packets cj for all packets j. The remaining-battery power level is incorporated into the routing decision. This also balances load by avoiding usage of weak nodes in presence of stronger ones. The congestion can be taken care of by increasing node cost in presence of contention.Power-Aware Source Routing (EFFICIENT DSR)

This is a reactive on-demand protocol based on the DSR protocol. The cost function, the cost of route at time t is C(,t) is given as

C(,t)=i Ci(t) --------- (7)

where Ci(t) is the cost of node i at time t.

Ci(t)=pi.[Fi/Ri(t)] --------- (8)

Where pi: transmit power of node i Fi = full-charge battery capacity of node i Ri(t)=remaining-battery power of node i at time t and =a positive weighting factor. This cost function takes into account both the transmission power and remaining battery power.EDSR route discovery mechanism

Fig.4 describes the power-aware routing mechanism with the RREQ and REP packets in the EDSR protocol. The RREQ broadcast is initiated by the number of sources. The intermediate nodes can reply to the RREQ packet from cache as in the DSR protocol. If there is no cache entry, receiving a new RREQ packet an intermediate node does following:1. Starts a timer. Keeps path cost in the header as minimum cost. Adds its own cost to the path cost in the header and broadcast. 2. On receiving duplicate RREQ packet, an intermediate node re-broadcasts it only if the timer for that RREQ packet has not expired. 3. Destination also waits for a specific time after the first RREQ packet arrives. It then replies to the best path in that period and ignores others.4. The new path cost in the header is less than the min-cost. The path cost is added to the RREP packet and is stored in cache by all nodes that hear the RREP packets.

Fig.4.Routing mechanism in EDSR protocol.Result and Discussion

There are two ways to implement the new algorithms in Network Simulator(NS-2.33).

1. Because the new algorithms are based on the DSR protocol, it is possible to modify directly the DSR protocol and do the tests with the modified version. Or 2. Add a new protocol in NS-2.33.

We have chosen the second solution, because it is more practical to have both the DSR and new protocols in the same version of NS-2.33 for testing purposes. Indeed, it is easier to compare the performance of the two protocols. So, we copied the DSR folder and create new one: the EDSR protocol for the new algorithm. By copying the folder, it is necessary to change all the class names and all the names of the static variables. If we do not do this, the simulator will not be able to compile, because of confusion in the object names. Then, each protocol uses its own packet. So it is essential to define specific packets for the new protocol in the common/packet.h file.

Other different C++ files have to be changed:/queue/dsr-priqueue.cc: Just to add the packet type of our protocol, declared in the packet.h file.

emph/trace/cmu-trace.cc(.h): To have the correct trace file corresponding to our protocol.

Make file: To compile NS-2.33 with new protocol. To finish, the link between TCL objects and new protocol objects has to be built. So, it is necessary to create a TCL object corresponding to the C++ object in the tcl/mobility folder, and to change these files:

tcl/lib/ns-default.tcl tcl/lib/ns-lib.tcl tcl/lib/ns-mobilenode.tcl

These three-files create correct TCL object corresponding to the protocol chosen in the simulation parameters.The DSR Protocol in NS-2.33

The DSR folder is inside the main NS-2.33 folder. It contains a lot of C++ files. Some of them are not used and are just there because they were used before an updated version. The main file is the dsragent.cc. It contains the DSRAgent class, which corresponds to TCL objects. All interactions with the simulator occur by using this class.

The other classes, defined in the other files, are some kind of tools, used in the DSR Agent object, to simplify the code:

hdr sr.cc(.h): Contains the class hdr sr, that represents the header of the DSR packet. It defines if the packet is a request, a reply, a route error, or a data packet. It contains the path that the packet has to use and all different parameters.

path.cc(.h): Contains two useful classes: ID and Path. ID is used to define the address of a node (MAC or IP) and path contains an array of nodes and a lot of possible operations that can be done on them.

srpacket.h: It contains class SRPacket. When a DSRAgent receives a packet, it is the common class packet. Then the packet is transform in SRPacket class, which is more useful to manipulate; hdr sr is contained in the packet object.

requesttable.cc(.h): It contains class RequestTable. It contains all information about the different requests received by a node. It is useful, because an intermediate node have to make only one broadcast, even if it receives it more than once.

flowstruct.cc(.h): Contains class FlowTable. This object contains a table that references all paths overhead by the node from any source to any destination. Then this table can be used to shorten the paths or to make a faster reply by sending directly a reply if an intermediate node already knows the destination.

Implementation of EDSR in NS-2.33

requesttable.cc (.h)/mobicache.cc: This file implements class Request-Table. This object contains a list of destination and different paths to reach every one of them. The other files are not used in the DSR implementation, it includes: add sr.cc, cache stats.h, dsr proto.cc(.h), linkcache.cc, simpleceche.cc, sr forwarder.cc(.h). The DSRAgent class uses all of those classes, to make the DSR protocol working, as described before.

Installation with Patch File

We just need to run the batch file/patch, by giving two arguments. The first one is the path to the NS2 directory(./patch /usr/bin/NS-2.33). Then the EDSR extension will be installed and we will be allowed to run simulation with EDSR protocol. After copying all the files we need, we have to run the command./configure, and then make in the NS-2.33 directory. The NS-2.33 will compile again, and we will be able to run simulation with the EDSR protocol.

The solution had been implemented and evaluated with 2-33. Since, we want to know how our protocol reacts in different mobility cases. Here we use two mobility patterns. First, we define the simulation in the 1000m X 1000m region, random waypoint mobile model, number of node is 100, the node original communication radius. Simulation results show that the created protocol behaves better than the DSR and AODV, the two main actual reactive protocols. Table 2 shows the simulation parameters used in the network setup for implementing EDSR protocol and select the alternate path for maintaining the continuous efficient network connection in the MANET. The EDSR protocol performs well in high mobility by using much less overhead than the two others mentioned before.

Table 2. Simulation parameters.Simulation time0-100 sec

Traffic typeCBR

Packet size1460

Hello packet interval2 sec

Node mobility0 to 100 mts/sec

Frequency1 Ghz

Channel capacity2 M bps

Transmit power 2.0 Mw

Receiver power2.0 Mw

Total number nodes100

Communication system MAC/IEEE 802.11G

Routing ProtocolsDSR,AODV,EDSR

Traffic sources are chosen as CBR with a packet size of 512 bytes. All traffic sessions are established at random times near the beginning of the simulation run and they remain active until the end of the simulation time. Each of 100 nodes has a 200J of energy at the start of every simulation while varying the number of traffic sources from 10 to 100. Total energy consumption is the difference of the total energy supplied to the network and the residual energy in joules. The initial energy supplied to the network in each scenario is 5000 J. Fig.5 shows the graph of energy consumption by the nodes during data transmission. It indicates that as the number of sent data packets increases, the amount of battery power in each node reduces. Ultimately, a node becomes a dead node(weak node) as energy consumed during each transmission increases.

Fig.5. Energy-consumed per data packets sent.

Fig.6 shows the total energy consumption(in J) for DSR protocol is less than the AODV protocol from low traffic condition to high traffic and performance of EDSR protocol is better than both the AODV and DSR protocols. As it is consuming less energy compared to other two protocols for varying number of traffic sources.

Fig.6. Total energy consumed for pause time 0 sec.

Fig.7 shows total energy consumed for 100 pause time has been evaluated for varying number of traffic sources. In the initial stage of the simulation, the EDSR protocol consumes more amount of energy as compared to the DSR protocol but later on it has less energy consumption as compared to AODV and DSR protocols. Thus, the curve is obtained for EDSR protocol in terms of energy consumption, which shows proper distribution of energy among all nodes.

Fig.7. Total energy consumed for pass time 100 sec.

It considers the network lifetime since after the death of 50% of the nodes network has been considered. In fig.8, it indicates the network lifetime for pause time 0 sec with varying traffic sources. The performance of EDSR protocol(providing greater network life with an approximate value of 6% to 9%) is better than that of both the AODV and DSR protocols.

Fig.8. Network lifetime for pause time 0 sec.

Fig.9 shows the reduction in initial network lifetime of AODV is comparatively lower than DSR. As the number of traffic sources increases, the DSR protocol contributes better network lifetime. The EDSR protocol is out performing as it provides better network lifetime when compare to the AODV and DSR protocols for all traffic conditions. The EDSR protocol has more than 25% of improvement in network lifetime for static network of having pause time 100 sec.

Fig.9. Network lifetime for pause time 100 sec.

In fig.10, the graph is plotted by considering the number of nodes, which are active nodes till the end of simulation time. If the number of active nodes are more in the network, then the protocol works efficiently. As shown in the fig.10, the average energy left per node in the DSR protocol is better as compare to the AODV protocol. But the energy distribution is best in case of EDSR protocol, as the average energy left per alive-node is more than in the AODV and DSR protocols.

Fig: 10: Average Energy remains in alive-nodes for pause time 0 sec.

In fig 11, it is observed that the average energy left per node with a pause time is 100 sec, the AODV protocol is slightly less than the DSR protocol. But the energy distribution is still best in case of EDSR protocol. Although for a large number of traffic sources(60), performance of all three protocols is same for a static network of pause time 100 sec. This efficient metric describes the time of successive deaths of the mobile nodes in the network. The result clearly indicates that greater the slope of the graph, higher is the node death rates and worse is the protocol performance.

Fig.11. Average energy remains in alive-nodes for pause time 100 sec.CONCLUSIONThe AODV, DSR, EDSR techniques consider the stability of the network from all aspects. The lifetime of the network can be reduced primarily by two causes. First, the node moving out of the radio range can lead to link breakage. Secondly, the node can be drained of its energy leading to network partitioning. The metric used in the proposed technique(EDSR) measures the stability of the network based on two factors. The routing decisions at each node lead to the multiple paths, which are node-disjoint. Thus, this technique is expected to provide highly stable, reliable, robust node-disjoint paths. As the paths are node-disjoint, energy drain rate of the nodes is expected to be less and hence longer lifetime. Also the paths are selected on the bandwidth constraints. They are the ones with higher capacity. Topology control is another approach, in which the transmission power is adjusted to achieve energy efficiency. Finally, we conclude that the network lifetime of EDSR protocol performs well when compare to the AODV and DSR protocols with different pause times. The simulation results show an increase in the packet delivery ratio in the network. The average node lifetime of EDSR improved from 45% to 60% longer than that of DSR and AODV protocols. References[1] Internet Engineering Task Force, MANET working group charter, http://www.ietf.org/html.charters/manetcharter.html.[2] Internet Engineering Task Force, "Manet working group charter", http://www.ietf.org/html.charters/manetcharter. html.[3] S. Singh, M.Woo, and C.S. Raghavendra. Power-Aware Routing in Mobile Ad-Hoc Networks, ACM/IEEE International Conference on Mobile Computing and Networking, 1998, pp.181-190. [4] Ramanathan and Rosales-Hain, Topology Control of Multihop Wireless Networks Using Transmit Power Adjustment, IEEE INFOCOM 2000, pp.181-189.[5] David B. Johnson, David A. Maltz, & Josh Broch, DSR: The Dynamic Source Routing Protocol for Multi-Hop Wireless Ad Hoc Networks, Ad Hoc Networking, Addison-Wesley, 2001, pp.139-172.[6] C.K. Toh, Maximum Battery Life Routing to Support Ubiquitous Mobilecomputing in Wireless Ad Hoc Networks, IEEE Communication Magazine, 2001, pp.2-11. [7] P. Bergamo, D. Maniezzo, A. Giovanardi, G. Mazzini, M. Zorzi, "Distributed Power Control for Power-aware Energy-efficient Routing in Ad Hoc Networks", European Wireless 2002, Florence,Italy, February 25-28, 2002, pp. 237-243.[8] Young J. Lee and George F. Riley, Dynamic NIx-Vector Routing for Mobile Ad Hoc Networks, IEEE Proceedings of Wireless Communications and Networking Conference 2005.[9] Chiasserini, C.F.,Chlamtac, I.,Monti,P., Nucci A., Energy Efficient Design of Wireless Ad-hoc Network, LNCS 2006, vol.2345, pp.376-386.[10] P. Appavoo and K. Khedo, SENCAST: A Scalable Protocol for Unicasting and Multicasting in a Large Ad hoc Emergency Network, International Journal of Computer Science and Network Security, Vol.8 No.2, 2008, pp.154-165. [11] Chakeres and C. Perkins, Dynamic MANET On-demand Routing Protocol(DYMO), http://tools.ietf.org/html/draft-ietf-manet-dymo, 2008.[12] N. Chilamkurti, S. Zeadally, A. Vasilakos, and V. Sharma, Cross-Layer Support for Energy Efficient Routing in Wireless Sensor Networks, Journal of Sensors, 2009, vol. 21, pp. 9.[13] C. Prehofer and C. Bettstetter, "Self-Organization in Communication Networks: Principles and Design Paradigms IEEE Communications Magazine, 2009, vol.43, pp.78-85.[14] K. Khamforoosh, and H. Khamforoush, A New Routing Algorithm for Energy Reduction in Wireless Sensor Networks, IEEE, 2010, vol.13,pp. 43-47.[15] Nitnaware, D. and Verma, A Energy Based Gossip Routing Algorithm for MANETs National Conference on Recent Trend in information, Telecommunication and Computing, 2010, pp-2327.[16] Shivashankar, B.Sivakumar, G.Varaprasad Improving of TCP Performance Evaluation in Mobile Ad Hoc Networks, Journal of Intelligent System Research, 2011, Vol.5, pp.69-81.