Optimizing Military Tactical MANETs efficiently using PSO

Abstract

A mobile ad-hoc network (MANET) is a self-configuring network of autonomous agents designed to continuously support users who change the topology of the network dynamically and independently. The dynamic mobility of user nodes can cause communication links to disconnect if the network is not managed correctly. The autonomous agents are controlled to maximize the connectivity of user nodes considering military aspects. Military operations require the efficient use of limited resources and sometimes sacrifice network performance to accomplish given missions. Especially, military MANETs are exposed to a dangerous environment due to the enemy activities, so these hostile effects should be considered. Under these requirements and circumstances, the primary objective of the agents is to maximize the connection and quality of communication between user nodes and a control node. This is formulated as a shortest path problem using a hop-count metric. A population based heuristic algorithm, Particle Swarm Optimization (PSO), is developed to solve the military MANET problem. To best accommodate the aspects of military MANETs several crucial constructs are devised and tested. These are the Pre-deployed Agent Level (PAL), the Messenger Agent and the Priority node. Testing involves the common random waypoint model and two specific military scenarios - search and rescue and patrol. The proposed model in this thesis tries better represents military MANETs by considering enemy obstacles and military operation characteristics. It can be used to evaluate network performance before deploying an actual network in the battlefield and to properly size the number of agent nodes. Also, the proposed approach could be used for commercial applications by slight modification of the objective function.