Connectivity management in mobile ad hoc networks using particle swarm optimization
This paper proposes a dynamic mobile ad hoc network (MANET) management system to improve network connectivity by using controlled network nodes, called agents. Agents have predefined wireless communication capabilities similar to the other nodes in the MANET, however their movements, and thus their locations, are dynamically determined to optimize network connectivity. A new approach to measuring connectivity using a maximum flow formulation is proposed  this is both responsive and tractable. Furthermore, users locations are predicted for several time steps ahead and this is shown to improve network connectivity over the network operation period. A particle swarm optimization (PSO) algorithm uses the maximum flow objective to choose optimal locations of the agents during each time step of network operation. The proposed MANET management system is rigorously tested on numerous static and dynamic problems. Computational results show that the proposed approach is effective in improving the connectivity of MANETs and predicting movements of user nodes and deploying agents accordingly significantly improves the overall performance of a MANET.
ad hoc networks particle swarm optimization network connectivity location models