Author : M. Pujitha, Ch. SriLakshmi Prasanna and M. Chenna Keshava

A mobile ad hoc network is a collection of self-organized mobile nodes. This network doesn’t require existing infrastructure or central administration. As the nodes have mobility, the biggest challenge in this kind of networks is to find a path i.e., routing between the communications end points. Ant colony optimization (ACO) is a technique to solve problems like routing in ad hoc networks based on food searching behavior of ants. All ant colony algorithms are subset of Swarm Intelligence which means collective behavior of individual ants. All the ant based algorithms are mainly depended on pheromone concentration. Pheromone is a volatile chemical substance secreted by ants from nest to food source in order to influence other ants to follow them. The route will be discovered by the concentration of pheromone values. AntHocNet is based on ideas from ACO. It is a hybrid algorithm consisting of both reactive and proactive components. AntHocNet has a reactive path setup but a proactive path management. It does not maintain routes to all possible destinations at all times but only sets up paths when they are needed at the start of a data session. This is done in a reactive route setup phase, where ant agents called reactive forward ants are launched by the source in order to find multiple paths to the destination, and backward ants return to the source to set up the paths. Routing information is stored in pheromone tables that are similar to the ones used in other ACO routing algorithms. Frederick ducatelle used open and urban scenarios using QualNet and the performance evaluation was done with relevant parameters such as node speed, data send rate, network size etc., in which a number of nodes move in an open, rectangular area along straight line segments with fixed data send rates. This scenario does not provide a correct image of situations that occur in reality. Therefore, observed results are not necessarily representative for what can be expected when the network is deployed for a practical application. In this project, the previous tests are complemented with new ones that use more realistic scenarios. The scenarios that are modeled include ManhattanGrid. Ns-2.34 simulator has been used along with Bonnmotion-2.1a for generating scenarios of different mobility models for limiting node movements to streets and open space of town. A comparative test has been done by calculating metrics end-to-end delay, throughput, packet delivery factor, routing overhead for AntHocNet and AODV routing algorithms. By using this, the limitations of AntHocNet have been identified. The energy consumption of AODV protocol has been evaluated in ad hoc network scenario with different number of source nodes and the performance of energy consumption at different nodes has been observed.

Keywords: AntHocNet, AODV, ManhattanGrid

Article published in International Journal of Current Engineering and Technology, Vol.5, No.4 (Aug-2015)