MULTILAYER PERCEPTRON WEIGHT OPTIMIZATION USING BEE SWARM ALGORITHM FOR MOBILITY PREDICTION

The progress in wireless networks has led to the rising demand of quality in service, reduced delay, seamless network, communication anytime/anywhere, and a lot more. The wireless network provides global services/communication through integrated networks. The wireless networks and beyond (4G) makes people free from cable and guarantee a fully distributed communication with promising Quality of Service (QoS). Hence, Mobility prediction or precise and competent forecast of mobile users trail is of prevailing significance for entire network performance. Mobility prediction along with wireless communication protocols helps in better energy, resource management in a network scenario and provides improved quality to the wireless users. This paper proposes mobility prediction based on a Multi-Layer Perceptron (MLP) network optimized with Bee Swarm Algorithm (BA). The proposed model evaluates mobility prediction using mobility traces from wide production wireless network. The Swarm Intelligence (SI) is used in many complex optimization problems in continuous search. The BA is the foraging behaviour of bees in searching food sources. The BA algorithm integrates the network for optimization of weights.