# PERFORMANCE STUDY AND SIMULATION OF AN ANYCAST PROTOCOL FOR WIRELESS MOBILE AD HOC NETWORKS

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3 This model assumes that nodes change position alternatively at discrete steps. This time interval for each step, is another system configuration parameter, and is defined by τ. Also each node is moving with a different speed in the range of (0, V max(x) ) and (0, V max(y) ) Mobility Model A mobility model controls the movement of mobile nodes, and change in their speed and location. The Random direction mobility model [10] is one of commonly used synthetic models. In this study, the random direction mobility model is used, but with some modifications. In the modified version, mobile nodes still continue to select random directions but can change their direction of movement at the end of any step, with the probability p. So nodes are not forced anymore to reach the borders to choose a new direction. By using the p probability as another system configuration, we can demonstrate various motion patterns. The original random direction model can be reached, if value of p is set to zero in extended version. A flooding-based [11] simulation system was developed relying on the chosen mobility model. System is used to localize an anycast server in wireless ad hoc network by employing anycast service. It is assumed that there are two types of nodes in the network. Simple nodes (clients) are the first type of nodes, they are sources of anycast requests. Simple nodes (intermediate) retransmit anycast requests, which come from source nodes, in multicast mode inside the network area. Simple nodes are also capable of forwarding unicast replies generated by server nodes. It is assumed that there is one group of anycast servers in the network with five identical mobile server nodes. These server nodes are distributed randomly in the network area. In the current model, there is only one source of requests. Therefore, logically, the system is equivalent to a finite population queuing system [12] with one client and a few identical servers. 3. SIMULATION SETUP Simulation experiments were organized and conducted according to the following setup. It is assumed that the network area is a rectangular (square) of 500 m 500 m. Such an area is quite realistic for small and medium-sized ad hoc wireless networks. The network area is populated by N=50 nodes, having numbers 1, 2, 3,, N. The first m nodes are anycast servers with numbers 1, 2,, m < N. The number of anycast servers, m, is specified in the file of parameters. The nodes with numbers m+1, m+2,, N are simple nodes. It is assumed that m<n-m, i.e. the number of anycast server nodes is less than the number of simple nodes. For the sake of simplicity, simple node N is the source node that generates anycast requests. Initial positions of the nodes (simple and servers) are random and different in different simulation runs with the uniform probability distribution [6] in given area. That is, the network area with its nodes can be approximated as a point Poisson field [6]. All nodes move from their initial positions according to the chosen mobility model. Any anycast server can receive more than one multicast request, but only the first received multicast request will be accepted and responded. Any simple node can receive more than one unicast reply from a few servers, but only the first unicast reply will be accepted and forwarded (if not the source node). As was mentioned earlier, only one network node was used as a source of anycast request messages. All other network nodes work as message routers or servers of anycast messages transmitted by the source node. Correspondingly, the source node discards all requests that can be transmitted by other nodes since these requests are copies of messages initiated by the source node. The source node assigns a unique number to each generated packet. Interval between transmissions of requests by the source node is set to be 500ms. For a small sized or medium 21

4 sized ad hoc network, this interval is sufficiently large to complete all activities in a network related to a request transmitted by the source node before it transmits the next request. As a result, at any moment of simulation time, the model will handle, at different nodes, messages with the same identifier. This considerably simplifies the model and its study. To obtain sufficiently stable statistical results of simulation, the total number of requests transmitted by the source node is set as Ns = 2000 messages. With this number of messages and inter-message interval of 500 ms, the simulation interval of each run is ms ms = ms, where 1000 ms is a small margin to provide the clearance of the model at the end of each simulation run [9]. Starting from a chosen random position, each network node (including the source node) moves in a random direction with a constant random speed in the given area. The random speed of a node is set according to uniform probability distribution in the range (0, V max ), where V max is the maximal speed set as a network configuration parameter. As it is explained in [13], the inter-node communication is considered as very reliable for nodes, which are very close to each other. For this reason, in the model, the distance to very close nodes is assumed to be a random variable which has a lower bound equal to zero and upper bound being uniformly distributed from 5 to 10 meters. Also when probability of changing direction is equal to zero, nodes change the direction of their movement randomly at the border of the network area was used in this mobility model. One more parameter of the inter-node communication scheme is the interval in which the states of oriented inter-node communication links are checked. This interval is the same for all simulation experiments and was set at 2000ms. At the end of this interval, the state of each link can change. The simulation experiments were conducted for maximal transmission distances 30m, 60m, 90m, 120m, 150m, 180m and 210m. Obviously, with these distances, the message transmitted or forwarded by a node can reach only a subset of network nodes in the given area. This is true for real ad hoc wireless networks. It is also assumed that each network node, intermediate or destination one, can lose any message, transmitted by another node, with some probability l. In the simulation experiments, as parameters, link availability l was used as the message loss probability in the range of 0< l <1. The value of time-to-leave (TTL) field in generated packets was fixed at seven or four in each request message. Two series of experiments were conducted. In the first series, the chosen performance metrics were studied for maximal node speeds 5 Km/h, 30 Km/h and 50 Km/h. It should be noted that, with the given value of V max, different network nodes will move with different speeds in the range (0, V max ). For these series of experiments link availability l=0.7 and probability of changing direction p=0.0 were used. In both series of experiments, the value of TTL in each request message was fixed at seven. In the second series of conducted experiments, the effect of TTL value on the performance metrics was investigated. For this reason, a set of experiments were performed by setting TTL to 4 and 7 with link availability l = 0.05, 0.1, 0.3, 0.5 and 0.7 when V max was set to 5 Km/h, probability of changing directions p=0.0 and the maximum transmission distance is varied in the range (30-210) meters. All the parameters and setup of simulation setup are shown in Table 1. 22

5 Table 1: Parameters of simulation setup Network area 500 m x 500 m Number of nodes 50 Total number of requests 2000 Interval between transmission of requests 500ms TTL (Time-to-leave) 4 and 7 Link availability (l) 0< l <1 Maximal transmission distances, m 30 to 210 Maximal node speed (V max ) 5 Km/h, 30 Km/h and 50 Km/h Changing direction probability (p) p = Performance Metrics Performance metrics are used to help researchers to investigate the wireless ad hoc networks. Delivery ratio and average number of hops per delivered packet are the most popular performance metrics used for this reason. The delivery ratio (referred as response ratio) characterizes how the network is effective in delivering packets from source nodes to server (destination) nodes. Average number of nodes that a packet traverses in its way to the source node is represented as the average number of hops. Both of these performance metrics have direct relation with the implemented routing algorithm, node mobility models and inter-node communication links specification. Response time is another performance metric. Response time is the time interval between the moments the source node sends a request, until the reception of the reply message. This metric is important for some real-time applications which need small time interval. Each packet that is transmitted by a source node will be usually retransmitted by some intermediate nodes until it is received by server nodes. Relative traffic is the performance metric that represents the number of times each packet is transmitted by other nodes. As matter of fact, it is necessary to keep relative traffic as low as possible to have less overloading in the network. All received replies in source node for a request, after receiving the first reply are taken as duplicated replies. This characteristic of the network behavior is shown by duplication ratio performance metric. It has a direct relation with robustness and availability of the network, but should not be large to have less traffic in the network. 4. RESULTS OF SIMULATIONS The results of the first series of simulations are presented in Figures 1 5. The results of the second series of simulation are shown in Figures Afterwards, Figures represent the comparison for second series of experiments. 23

6 Figure 1: Relative traffic versus transmission radius with link availability l=0.7 and different maximal node speed. Figure 2: Response ratio versus transmission radius with link availability l=0.7 and different maximal node speed. Figure 3: Duplicate ratio versus transmission radius with link availability l=0.7 and different maximal node speed. 24

7 Figure 4: Average number of hops versus transmission radius with link availability l=0.7 and different maximal node speed. Figure 5: Average response time versus transmission radius with link availability l=0.7 and different maximal node speed. Figure 6: Relative traffic versus transmission radius with different link availability for TTL=4. 25

8 Figure 7: Response ratio versus transmission radius with different link availability for TTL=4. Figure 8: Duplicate ratio versus transmission radius with different link availability for TTL=4. Figure 9: Average number of hops versus transmission radius with different link availability for TTL=4. 26

9 Figure 10: Average response time versus transmission radius with different link availability for TTL=4. Figure 11: Relative traffic versus transmission radius with different link availability for TTL=7. Figure 12: Response ratio versus transmission radius with different link availability for TTL=7. 27

10 Figure 13: Duplicate ratio versus transmission radius with different link availability for TTL=7. Figure 14: Average number of hops versus transmission radius with different link availability for TTL=7. Figure 15: Average response time versus transmission radius with different link availability for TTL=7. 28

11 Figure 16: Relative traffic versus transmission radius with different link availability for TTL=7, and 4. Figure 17: Response ratio versus transmission radius with different link availability for TTL=7 and 4. Figure 18: Duplicate ratio versus transmission radius with different link availability for TTL=7 and 4. 29

15 [13] Haci H., (2010) Performance investigation of simulation models of wireless mobile ad hoc networks, Thesis (M.S.), Eastern Mediterranean University, Institute of Graduate Studies and Research, Dept. of Computer Engineering, Famagusta, Cyprus. Author Reza Azizi was born in Bojnourd, Iran in He received the BS degree in Software Engineering from Islamic Azad University of Shirvan, Iran, a MS degree in Computer Engineering from Eastern Mediterranean University, Cyprus. He has been employed as a research assistant at computer engineering department, where he has been involved in multiple A class projects. He is currently working as Lecturer at Islamic Azad University of Bojnourd and a joint member of Educational Center of Applied-Science and Technology. Research interests: Simulation, Performance Evaluation and Optimization of Wireless Sensor and Mobile Ad-Hoc Networks. 33

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