Ant Routing in Mobile Ad Hoc Networks

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3 node. For each destination in the network, the table contains a moving observation window,an estimated mean and an estimated variance 2 The moving observation window,ofsize,represents an array containing the trip times of last forward ants that travel from the node to the destination. The moving observation window is used to compute the best trip time i.e., the best trip time experienced by a forward ant travelling from the node to the destination among the last forward ants that travel from the node to the destination. The mean and variance 2 represent the mean and variance of the trip times experienced by the forward ants to move from the node to the destination node and are calculated using the exponential model: + ( ) (1) ³ ( ) 2 2 (2) In (1) and (2), represents the newly observed forward ant s trip time to travel from the node to the destination node and ( 1] is a factor that weighs the number of recent samples that will affect the mean and the variance AntNet Algorithm The AntNet algorithm [5] can be described as follows: 1. At regular intervals, from every network node,aforward ant is launched with a randomly selected destination node. While travelling towards their destination nodes, the identifier of every visited node and the time elapsed since the launching time of the forward ant to arrive at this -th node are pushed onto a memory stack stored in the data field of the forward ant. Forward ants share the same queues as data packets, so they experience the same traffic delays as data packets. 2. At each node, each forward ant chooses the next node as follows: If all the neighboring nodes have not been visited, then the next neighbor is chosen among the nodes that have not been visited as: = + 1+ ( 1) (3) In (3), represents the set of neighbors of the current node and the cardinality of that set, i.e., the number of neighbors while the heuristic correction is a normalized value [ 1] such that 1 is proportional to the length of the queue of the link connecting the node with its neighbor : =1 P =1 (4) The value of in (3) weighs the importance of the instantaneous state of the node s queue with respect to the probability values stored in the routing table. If all the neighboring nodes have been visited previously, then the next node is chosen uniformly among all the neighbors. In this case, since all the neighbors have been visited previously the forward ant is in a loop (cycle). With a small probability, the next node is chosen uniformly among all the neighboring nodes. The parameter ensures that the network is being constantly explored, though it introduces an element of inefficiency in the algorithm [4]. 3. If a cycle is detected, the cycle s nodes are popped from the ant s stack and all memory about the cycle is destroyed. If the cycle lasted longer than the lifetime of the forward ant before entering the cycle, the ant is destroyed. The lifetime of a forward ant is defined as the total time since the forward ant was generated. 4. When the destination node is reached, the forward ant generates a backward ant.theforward ant transfers all the memory contained in the stack to the backward ant, and dies. The backward ant takes the same path as the corresponding forward ant, but in the opposite direction. At each node, the backward ant pops the stack to move to the next node. Backward ants use high priority queues to quickly propagate to the routing tables the information collected by the forward ants. 5. Arriving at a node coming from a neighbor node, the backward ant updates the two main data structures of the node, the local model of the traffic and the routing table, for all the entries corresponding to the destination node. The mean and variance 2 entries in the local model of traffic are modified using (1) and (2). If the newly observed forward ant s trip time from the node to the destination is less then,then is replaced by The routing table is changed by incrementing the probability (i.e., the probability of

4 choosing neighbor when destination is )and decrementing, by normalization, the other probabilities The probability is increased by the reinforcement value as: 1..8 a / N k =.5 + (1 ) (5) The probabilities of the other neighboring nodes for destination are decreased by the negative reinforcement as: s(r)/s(1).6.4 a / N k = 1 a / N k = 2 a / N k = 4 a / N k = 16 6= (6).2 a / N k = 8 a / N k = 32 Thus, in AntNet, every path found by the forward ants receives a positive reinforcement r.6.8 a / N k = The reinforcement value used in (5) and (6) is a dimensionless constant ( 1] and is calculated as: = ( )+( ) (7) In (7), the parameter represents a better estimate of mean and is calculated as: = + 1 p where is the confidence level [4]. The first term in (7) evaluates the ratio between the current trip time and the best trip time observed over the moving observation window. The second term is a correction factor and indicates how far the value of is from in relation to the extension of the confidence interval [5]. The values of 1 and 2 indicate the relative importance of each term. The value calculated in (7) is finally transformed by means of a squash function () defined by: 1 () = ³ 1+exp where ( 1] R + () (1) (1) The squash function () is used so that small values of would have negligible effect in updating the routing tables [5]. Due to the squash function (), the low values of are reduced further, and therefore do not contribute in the update of routing tables 1. Figure 2 shows the effect of coefficient on the squash function (). Figure 2 shows that if the value of coefficient is less than 1, then even low values of get incremented due to the squash function (). Thus, the value of parameter should be chosen such that the coefficient is greater than 1. (8) (9) Figure 2. The squash function () for different values of the coefficient. Data packets use different routing tables than the forward ants for travelling from the source node to the destination node. The routing table values for data packets are obtained by re-mapping the routing table entries used by forward ants by means of a power function () and re-normalizing these entries. () = 1 (11) The power function () prevents the data packets from choosing links with very low probability. The data packets have a fixed time to live (TTL); if the data packets do not arrive at the destination within the TTL, they are dropped. Table 1 lists the optimized values of different parameters used in W_AntNet [4]. Table 1. The values of different parameters used in W_AntNet. Name, symbol Value used in (5) used in (9).7 2 used in (9).3 used in (11) used in (13) 3 Confidence interval () used in (1).95 1 Low values of indicate sub-optimal paths.

6 .2.15 Pr[H = k].1.5 N = 5 W_AntNet pause_time = E[H] = 22.2 pause_time = 3 E[H] = 21. pause_time = 6 E[H] = 22.6 pause_time = 12 E[H] = 19.3 pause_time = 3 E[H] = 12.8 pause_time = 6 E[H] = 7.3 Static Topology W_AntNet E[H] = 4.3 Dijkstra's algorithm E[H] = 2.9 Number of connectivity changes 12x our Simulation model Broch et al. [5] model N = pause time (sec) 8 Percentage of data packets in loops W_AntNet look_w_antnet E[H] pause time (s) look_w_antnet W_AntNet N = hop k pause time (s) 6 8 Figure 3. The probability distribution of hopcount for W_AntNet algorithm as a function of pause time. The legend shows the expected hopcount for different values of pause times. Figure 4. Percentage of data packets with a loop in their path as a function of pause time for W_AntNet (look_w_antnet). The insert shows the expected hopcount in W_AntNet for different pause times. duces the percentage of data packets in loops at increased mobility NS-2 Simulations The default settings for all the experiments and the simulation code and parameters for AODV, DSR are taken from the CMU/Monarch extensions for NS-2 [3]. The MAC layer is and the interface queue size is assumed to be 1 packets. The number of CBR sources is 1 and the data rate is 4 packets/sec. The capacity of links is 2 Mbps. In W_AntNet, we assume that each node can store 5 packets in the low and high priority queues Case Study 1: Static Network We compare the packet delivery ratio (PDR) and the endto-end delay for AODV, DSR and W_AntNet for a static scenario. The size of data packets is varied from 64 bytes to124 bytes. We also show results for W_AntNet when the forward ants are generated only during the T.P. (W_AntNet_antsTP). This reduces the number of control packets in the network. Since the topology is static and the amount of data traffic is small, the generation of forward ants only during T.P. is sufficient for routing under these conditions. The PDR and end-to-end delay in Figure 5 show that W_AntNet performs similar to AODV and DSR when forward ants are generated only during the T.P. However, as the forward ant generation rate is increased from (W_AntNet_onlyTP) to 2 forward ants/s, the routing overhead causes congestion in the queues. Thus, W_AntNet causes a dual problem in ad hoc networks. A large number of forward ants cause congestion in the network. However, with increasing mobility, more forward ants need to be generated to account for frequent changes in the topology Case Study 2: Mobile Scenario We study the performance of different routing protocols with mobility. The packet size is assumed to be 64 bytes. Figure 6 shows the PDR, end-to-end delay and the routing overhead (measured in number of bytes since the size of control packets varies in W_AntNet) for AODV, DSR, W_AntNet and W_AntNet with lookahead (W_AntNet_look). We also reduce the routing overhead in W_AntNet by generating forward ants only from source-destination pairs that have data packets to send (W_AntNet_onlysrc). Thus, the remaining nodes in the network do not maintain up-to-date routing tables. Figure 6 shows that with high node degree, AODV and DSR perform well in terms of PDR and end-to-end delay. Figure 6 shows that using optimizations such as look-ahead and generating ants only from source-destination pairs reduces the overhead in W_AntNet. However, the routing overhead is still more than in AODV and DSR. As a result, the PDR for W_AntNet_look and W_AntNet_onlysrc

7 Figure 5. The PDR and end-to-end delay for W_AntNet, AODV and DSR for static topology. is still lower than AODV and DSR Case Study 3: Large Area Network (Sparse Graph Topology) In this case, we compare the performance of different protocols in a larger area with the same number of nodes ( = 5). The area over which the nodes move is Under these conditions, the average node degree is 25 and the worst case hopcount is given by 11. The number of CBR sources is also increased to 2. Figure 7 shows the PDR and end-to-end delay for AODV, DSR and W_AntNet. The packet size is assumed to be 64 bytes. This scenario leads to a sparse graph topology and an increase in network diameter. As a result, the performance of all three protocols degrades considerably. Thus, the simulations show that all three protocols have scalability problems. To improve the scalability of routing protocols, additional schemes such as clustering need to be implemented [15]. Figure 7 also shows that the use of look-ahead does not im- Figure 6. The PDR, end-to-end delay and routing overhead for W_AntNet, AODV and DSR for mobile network of 5 nodes moving in an area 15 3m 2.

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