# Research Article Ant Colony and Load Balancing Optimizations for AODV Routing Protocol

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5 International Journal of Sensor Networks and Data Communications 5 where P is pheromone of a node Src to a node Dest, N h is number of hops from Src to Dest, N n is number of nodes in the network, L C is accumulated link capacity from Src To Dest, and T is trip time of the ant between Src and Dest, for each neighbor in the pheromone matrix Ak that uses this node as next hop to destination do: update the pheromone entry in matrix to pheromone value calculated by equation (1), and find the highest pheromone neighbor and update the routing table Tk for that destination to make the node forward packets to that neighbor. (2) Else if it is an intermediate node then: update the reverse memory of the backward ant, and (2.1) If the route to destination does not exist then: create a new route table entry for that destination node in routing table Tk, for each neighbor in the pheromone matrix Ak that uses this node as next hop to destination do: update the pheromone entry in matrix Ak to pheromone value calculated by (1), and find the highest pheromone neighbor and update the routing table Tk for that destination to make the node forward packets to that neighbor, and increase the number of active destinations by 1. (2.2) Else if the route to destination exists then: increase the pheromone value of that neighbor to that destination using the following equation: Pn = α Po+ β P, (2) where Pn is the new pheromone value, Po is the old historic pheromone value, P is current pheromone value calculated using equation (1), α is weight of the historic pheromone value, and β is weight of the current pheromone value. 1 1 We have used 0.75 as a value for α and0.25asavaluefor β, which gives more weight to previous accumulated historic value of pheromone decrease the pheromone value of all other neighbors to that destination using the following equation Pn = α Po+ β P, (3) where Pn is the new pheromone value, Po is the old pheromone value, P is pheromone value calculated using equation (1), α is weight of the historic pheromone value, and β is weight of the current pheromone value. find the highest pheromone valued neighbor and update the route entry for that destination to make the node forward packets to that highest valued neighbor. (3) Else if it is the destination node: initialize the reverse memory of the backward ant, copy the memory of arriving forward ant to the memory of backward ant, and make the backward ant follow the same path which is stored in its memory to the source leaving behind a pheromone value accordingly on each hop in the back path to source node. 4.4 Route expiry After some time, route expiry timer timeouts. Then the route entry must be removed from routing table and pheromone array, by going through the following procedure. Procedure 4 Route Expiry (1) If the route expiry timeout event has occurred: remove that destination from the pheromone matrix Ak, remove the routing table entry from routing table Tk, and decrease number of active destinations. 4.5 Connection loss After some time, if no packets arrive whether data, ants or hello from a neighbor, then we do the following actions. Procedure 5 Connection Loss (1) If the connection timeout event has occurred: reset the pheromone value of that neighbor to 0.0 in the pheromone matrix Ak, remove the routing table Tk entries that go through that neighbor,

6 6 International Journal of Sensor Networks and Data Communications if any other available neighbor has a nonzero pheromone value to that destination, update the routing table Tk to use the other neighbor (Automatic Repair using Multi-Ant Routes), and decrement number of active neighbors. 4.6 Local repair If some request packet arrives and no routing entry exists for it, then the algorithm try to do local repair as follows. Procedure 6 Local Repair (1) If local repair is enabled on this node: if any other available neighbor has a nonzero pheromone value to that destination update the routing table Tk to use the other neighbor (Automatic Repair using Multi-Ant Routes). Then, send all packets in queue to the destination using this new neighbor as the next hop, and otherwise use default built-in AODV Local Repair strategy. (a) 5 Simulation and performance evaluation 5.1 Simulation environments In order to evaluate the performance of the proposed protocol, we use the network simulator OPNET Modeler V.14 and make modification to the AODV. We used more than one case to establish that MRAA was an improvement over AODV. The following is the explanation of the four chosen cases. (1) Number of 50 Nodes and running the same MANET Routing Protocol with default parameters but the simulation was run for 1 hour, FTP is configured to generate medium traffic loads and is allowed to make only serial connections with 3 repeats during the whole simulation. For traffic source, FTP application is used generating data starting randomly using uniform distribution from the 100th second to the end of simulation and each time the application continues to send data for 10 seconds three times during the whole simulation to random chosen destinations. However, the wireless nodes are fixed rather than mobile as shown in Figure 5. 2 (2) The same model exactly as above but with Number of 50 Nodes and running the same MANET Routing Protocol with default parameters but the simulation was run for 5 hours, FTP is configured to generate high traffic 2 Note that default parameters differ from protocol to another but for AODV the route expiry timeout is 3 seconds, allowed hello is 2 times, hello interval is uniform (1,1.1), net diameter is 35 hops, and local repair is enabled. (b) Figure 5: (a) A figure showing the 50 stationary nodes network topology and their positions. (b) A figure showing the 50 mobile nodes network topology and their initial positions, note that the upward arrow does not mean they all move upward but they move according to random way-point in random directions with changing speeds and making some stops during the simulation. loads and is allowed to make concurrent connections with unlimited repeats during the whole simulation as shown in Figure 5. (3) Number of 50 Nodes positioned randomly in an area of meters and running the same MANET Routing Protocol with default parameters but the simulation was run for 15 minutes. For traffic source, data was generated with interarrival time of packets is exponentially distributed with mean outcome of 10 seconds; for packet size we use exponential distribution with mean outcome of 1024 byte and the nodes start sending from start to end of simulation to randomly chosen destinations. As a mobility model, we use the random waypoint model

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