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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 Convergence Time Evaluation of AODV and AODV+G in MANETs Annapurna P Patil Harish.R Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology,Bangalore-54,India. firstname.lastname@example.org email@example.com Abstract -Wireless mobile ad-hoc networks are characterized WiFi-ready notebook PCs( MANET nodes) owned by rescue as networks without any physical connections. In these networks volunteers themselves to construct a MANET to support such there is no fixed topology due to the mobility of nodes, a need. interference, mulitpath propagation and path loss. Hence a MANET can be classified based on the dynamic routing protocol is needed for these networks to communication pattern or the devices used, the variants of function properly. Many routing protocols have been developed for accomplishing this task. Selecting most appropriate routing MANETs on the type of devices are sensor and ad hoc protocol for a particular network scenario is the critical issue. networks. Routing is one of the critical issue in MANET. Most attempts made at evaluating these algorithms so Selecting the energy efficient routing protocols improves the far have focused on parameters such as throughput, packet performance of the communication. The routing protocols are delivery ratio, overhead etc. An analysis of the convergence classified into three types. Proactive protocols maintain times of these algorithms is still an open issue. The work carried routing information for all the destinations, and keep updating out fills this gap by evaluating the algorithms on the basis of this information through periodic updates, an example for this convergence time. protocol is DSDV,OLSR. Reactive protocols don’t In this paper we present and examine the convergence maintain information for all the destination, rather they time evaluation of routing protocols AODV and AODV+G . The algorithm performances are compared by simulating them in discover the route to a destination on demand, an example for ns2. Tcl is used to conduct the simulations, while perl is used to this protocol is AODV. Hybrid protocols attempt to extract data from the simulation output and calculate combine the advantage of both proactive and reactive convergence time. After extensive testing we observed that protocols, an example for this protocol is TORA, ZRP, AODV+G converged well in all situations than AODV. The MPOLSR. AODV+G reduces unnecessary traffic will paper also evaluates the algorithms using the rudimentary effectively improve the efficiency of those mobile nodes in metrics-throughput and packet delivery ratio. network. AODV and AODV+G protocols performs differently Keywords- Routing Protocols, MANETS, Convergence Time. under different network scenarios. One protocol might perform better than others in specific situation. These I. INTRODUCTION protocols are compared in terms of convergence time to uncover in which situations these types of algorithms have A Mobile Ad-Hoc Network (MANET) is a self- their strengths and weaknesses. configuring network of mobile nodes connected by wireless links, to form an arbitrary topology. The nodes are free to II. RELATED WORK move randomly. Thus the network's wireless topology may be unpredictable and may change rapidly. Minimal There are many other works which are related to our configuration, quick deployment and absence of a central work in evaluating routing algorithms.  AODV and governing authority make ad hoc networks suitable for AODV+G has been compared in terms of Average delay, emergency situations like natural disasters, military conflicts, Packet delivery ratio, Normalized routing load and Routing emergency medical situations etc . load reduction, but not in terms of convergence time.  Every device in a MANET is also a router because it AODV and DSDV has been compared in terms of is required to forward traffic unrelated to its own use. Almost convergence time. Many papers have compared AODV with every year, the world is struck by numerous catastrophic other routing algorithms. In  AODV and DSDV have been natural disasters, such as earthquake, hurricane, typhoon, compared with average throughput, packet loss ratio, and tsunami, etc. In such a situation communication systems, routing overhead as the evaluation metrics,  has compared fixed or mobile, were usually down due to various reasons. AODV and DSDV in terms of delay and drop rate,  The loss of communication systems as well as information compares AODV and DSDV in terms of throughput, packets networks made the rescue operation extremely difficult. received, delay and overload. Similarly,  compares 1 175 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 AODV, DSDV and DSR in terms of throughput, delay, drop network, here GOSSIP3(.65,1,1) is used. The timeout period rate. of GOSSIP3 should be big enough to allow neighboring nodes to gossip. The NODE_TRAVERSAL_TIME parameter III. PROTOCOL SPECIFICATION of AODV is a conservative estimate of the average one hop traversal time for packets that includes queuing delays, This section gives the small presentation of two interrupt processing times and transfer times. GOSSIP3 is not protocols we evaluate in this paper. used in the expanding-ring search with a smaller radius, since flooding is more efficient than gossiping for zone with small A. AODV radius because of the back-propagation effects. The The Ad-hoc On-Demand Distance Vector (AODV) variant of AODV that uses GOSSIP3 is called AODV+G. routing protocol is designed for use in ad-hoc mobile networks. AODV is a reactive protocol: the routes are created only when they are needed. It uses traditional routing tables, IV SIMULATION AND PERFORMANCE ANALYSIS one entry per destination, and sequence numbers to determine whether routing information is up-to-date and to prevent A. Environment and Assumption routing loops. An important feature of AODV is the maintenance Simulator chosen : The proposed algorithms are simulated on of time-based states in each node: a routingentry not recently NS2(version 2.33). NS2 is popularly used in the used is expired. In case of a route is broken the neighbours simulation of routing and multicast protocols, among others, can be notified. Route discovery is based on query and reply and is heavily used in ad-hoc networking research. ns cycles, and route information is stored in all intermediate supports an array of popular network protocols, offering nodes along the route in the form of route table entries. The simulation results for wired and wireless networks alike. It following control packets are used: routing request message can be also used as limited-functionality network emulator. It (RREQ) is broadcasted by a node requiring a route to another was necessary to use available implementations of algorithms node, routing reply message (RREP) is unicasted back to the rather than implement them freshly ourselves, as it is source of RREQ, and route error message (RERR) is sent to important for the acceptance of an evaluation that the notify other nodes of the loss of the link. HELLO messages implementation used for evaluation has been scrutinized and are used for detecting and monitoring links to neighbours. accepted as correct by the community. Else the evaluation results will not be accepted as doubt will exist about the B. Gossiping & AODV+G correctness of the implementation of the algorithms The basic gossiping protocol is simple. A source Algorithms chosen : Here in this paper we have selected to sends the routing request with probability 1. When a node simulate and evaluate the performance of AODV and first receives a routing request, with probability p it AODV+G protocols. AODV is a reactive routing protocol and broadcasts the request to its neighbors and with probability 1 AODV+G is variant of AODV routing protocol with – p it discards the request; if the node receives the same rout GOSSIP3. Further experiments can be built based on the request again, it is discarded. Thus, a node broadcasts a given results of this project, to compare convergence time route request at most once.  proposes GOSSIP(p,k,m), an performance of algorithms within the same category as well. extension to the basic gossiping, and suggests that: A node broadcasts with probability 1 for the first k hops Mobility model : The Random Waypoint model is the most before continuing to gossip with probability p. commonly used mobility model in research community. At If a node with n neighbors receives a message and does not every instant, a node randomly chooses a destination and broadcast it, but then does not receive the message from at moves towards it with a velocity chosen randomly from a least m neighbors within a reasonable timeout period, it uniform distribution [0,V_max], where V_max is the broadcasts the message to all its neighbors . maximum allowable velocity for every mobile node. After Hass et al. implements GOSSIP(p,k,m) in Ad Hoc reaching the destination, the node stops for a duration defined On-Demand Distance Vector protocol (AODV) , a typical by the 'pause time' parameter. After this duration, it again and well-studied on-demond routing algorithm suited for chooses a random destination and repeats the whole process mobile nodes routing in ad hoc network. We refer this gossip- until the simulation ends. based AODV as AODV+G. The experiments in  shows To create Mobile node Movement Scenario files, the that gossiping can reduce control traffic up to 35% when command line that needs to be run under directory : ns- compared to flooding and the most significant performance of allinone-2.33/ns-2.33/indep-utils/cmu-scen-gen/setdest : GOSSIP is achieved by taking p=0.65, k=1 and m=1. ./setdest [-n num_of_nodes] [-p pausetime] [-s maxspeed] [-t In AODV+G, if the expanding-ring search with a simtime] [-x maxx] [-y maxy] > [output-file]. smaller radius fails, rather than flooding to the whole 2 176 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 Traffic pattern : Moreover, traffic sources may generate of detection of an interface being down. This failure event is packets at constant bit rate (CBR), or at variable bit rate said to end when the listener receives link state PDUs from (VBR). The CBR class is commonly used for voice and data both ends of the link. services. In this context, the data rate and the delay remain We arrive at the convergence time by measuring the constant during the packet transmission. More particularly, interval between the detection of route failure and successful CBR traffic sources provide a constant flow of data packets of arrival of a packet at the destination over the newly computed 512 bytes with a transmission rate of 4 packets per second. route. This includes not only the routing convergence time, All CBR traffic scenarios are generated using cbrgen.tcl in but also the time taken for the packet to traverse the network NS-2 from the source to the destination over the newly discovered To create CBR traffic scenario files, under directory path. Since this is a comparative analysis, and both the :ns-allinone-2.33/ns-2.33/indep-utils/cmu-scen-gen/cbrgen.tcl routing protocols use shortest distance with number of hops as ./ns cbrgen.tcl [-type cbr|tcp] [-nn nodes] [-seed seed] [-mc the metric for distance calculation, both protocols will arrive connections] [-rate packet/second for one connection] > at the same new route, and the time taken to reach the [output-file]. destination over this new route will be the same (since all physical characteristics are the same). Hence this extra time Network scenario : The simulations are conducted using the measured does not affect the comparative analysis. network simulator ns2 . Random Waypoint mobility In any case, the time taken for a packet to travel from model is used. The physical layer simulates the behavior of the source to the destination is negligible when compared to IEEE 802.11 (as included with ns2). Each node has a radio the time taken for the algorithm to discover the new route, range of 250 meter, and uses TwoRayGround as the radio either through route request – route reply sequences as in propagation model. reactive protocols, or by waiting for an update that contains All the scenarios are based on the following basic parameters: new route information as in proactive protocols. Also, this cbr (constant bit rate) traffic automatically verifies that the new path calculated is correct. topology of size 500 m x 500 m The cycle of invalidation of old path and discovery maximum speed of each node 20 m/s of a new path might occur many times, and for many source- simulation time 180s destination pairs over the course of the simulation. Hence the transmission rate (packet rate) 10 m/s average value of these times is taken as the convergence time The number of nodes is varied in the range [10,100] in steps of that algorithm for that scenario. of 10 (to represent 10 node densities). Pause time is varied in This procedure has been carried out in perl. the range [0,180] in steps of 20 (to represent 10 pause times). Throughput : If y number of packets delivered within t time B. Performance Metric at a node then the throughput at the node could be defined as y/t. By definition, the throughput needs to be calculated at the A trace file contains a lot of information which may not be bottleneck node, not sender. For the throughput calculation, in required to analyze the performance of the protocol. We are general divide the successfully received packets by the always interested in some amount of information that is simulation time will give the answer. In the trace file there are sufficient to predict the efficiency of the protocol. The different levels of received packets such as the RTR or AGT following performance metric is needed to be taken into level. The packets received by the node in its AGT level will consideration in order to analyze and compare the be the real received packets. Here these packets are filtered performance of AODV and AODV+G from the trace file using perl script. Convergence Time : In , convergence time has been Packet Delivery Ratio : The ratio between the number of defined as the time between detection of an interface being packets successfully received by the application layer of a down, and the time when the new routing information is destination node and the number of packets originated at the available.  defines a route convergence period as the application layer of each node for that destination. period that starts when a previously stable route to some destination becomes invalid and ends when the network has obtained a new stable route for. Similarly, we define V. EXPERIMENTAL RESULTS convergence time as the time between a fault detection, and restoration of new, valid, path information. Graphs are one of the ways to analyze and compare  calculates convergence time in the IP backbone. the results of the trace file. Other methods can also be used The authors arrive at the value of convergence time by for comparison like tabular form showing required output deploying entities called ‘listeners’, which listen to every link data of the trace file. Simple MS Excel or MATLAB also state PDU being sent by the is-is protocol. The time when the work for plotting graphs. In this paper the graphs are plotted first ‘adjacency down’ packet is observed is taken as the time using xgraph in NS2. 3 177 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 In order to be able to cover most if not all the types of scenarios the algorithms might face, we varied both the node density (number of nodes) and the node mobility (pause time). The node density (number of nodes) was varied in the range [10,100] in steps of 10 (10 different node densities). The upper limit of this range was chosen to be 180 because the simulation time is 180s in all the cases. Thus a pause time of 180 implies that the nodes pause in their initial positions for 180 seconds – the entire duration of the simulation. Hence this represents the case where nodes are completely static. Similarly, pause time 0 represents very high mobility where the nodes are in constant motion. Thus we tested each algorithm over 10 node densities x 10 pause times = 100 scenarios. A. Convergence Time Figure 3: 30 nodes, varying pause time Convergence time of AODV and AODV+G is calculated using perl script. This script parses trace file created by simulating AODV and AODV+G algorithms to calculate convergence time. In each graph, the node density is fixed and the pause time is varied. Figure 4: 40 nodes, varying pause time Figure 1: 10 nodes, varying paused time Figure 2: 20 nodes, varying pause time Figure 5: 50 nodes, varying paused time 4 178 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 Figure 6: 60 nodes, varying paused time Figure 9: 90 nodes, varying paused time Figure 7: 70 nodes, varying paused time Figure 10: 100 nodes, varying paused time Based on the above figures it is found AODV+G convergence time is less than AODV in all assumed network scenarios. It is also found that as the pause time increases the convergence time of AODV+G decreases. Convergence time of both AODV and AODV+G increases as the node density increases. B.. Throughput Here node density is varied from 10 to 100 in steps of 10 nodes. For each node density both the algorithms are simulated with varied paused time from 0s ts 180s in steps of Figure 8: 80 nodes, varying paused time 20s. Average throughput in each node density is taken and the graph is plotted as show in figure 12. 5 179 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 VI. CONCLUSION AODV and AODV+G mobile Ad-hoc routing protocols have been presented and evaluated using well know network simulator NS2( version 2.33 ). AODV+G is gossip based AODV, here GOSSIP3(.65,1,1) is used. These two protocols are evaluated using the network performance metric convergence time. Here we observed that AODV+G converged well than compared to AODV in all assumed network scenarios. We also noticed that with the very low node density throughput and packet delivery ratio of AODV is more than AODV+G. With node density more than 30 nodes AODV+G performs better than AODV. We can extend our work to compare the performance of Adaptive Gossip- Figure 12: Throughput AODV Vs AODV+G based Ad Hoc Routing (AGAR) with Gossip-based Ad Hoc Routing (AODV+G) using convergence time, throughput and From the above figure we observed that AODV with packet delivery ratio. the low density performance well than AODV+G. As the node density increases AODV+G throughput increases. With ACKNOWLEDGMENT the node density more than 30 nodes the throughput of AODV+G is almost double than AODV. We wish to acknowledge our Principal Dr K Rajanikanth, M. S .Ramaiah Institute of Technology,Bangalore-54 and C. Packet Delivery Ratio Professor and Head of the Department at CSE Prof .V. Muralidharan for their encouragement which helped us Here node density is varied from 10 to 100 in steps produce this work.. of 10 nodes. 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Halpern, Senior Member, IEEE, and Li (Erran) Li, Member, IEEE, “Gossip-Based Ad Hoc Routing”, IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 14, NO. 3, JUNE 2006 AUTHORS PROFILE The authors are Faculty and Post graduate Student at M S Ramaiah Institute of Technology, Bangalore working in the area of performance evaluation of routing algorithms at the R&D labs, Department of Computer Science and Engineering. 7 181 http://sites.google.com/site/ijcsis/ ISSN 1947-5500
"Convergence Time Evaluation of AODV and AODV G in MANETs"