Tracking the events in the coverage of wireless sensor networks based on artificial neural-networks algorithms

Abstract

Sensor deployment is an important problem in mobile wireless sensor networks. This paper presents a distributed self-spreading deployment algorithm (SOMDA) for mobile sensors based on artificial neural-networks selforganizing maps algorithm. During the deployment, the nodes compete to track the event and cooperate to form an ordered topology. After going through the algorithm, the statistical distribution of the nodes approaches that of the events in the interest area. The performance of the algorithm is evaluated by the covered percentage of region/events, the detecting ability and the energy equalization of the networks. The simulation results indicate that SOMDA outperforms uniform and random deployment with lossless coverage, enhancive detecting ability and significant energy equalization.

Keywords

wireless sensor network, coverage, artificial neural-networks, self-organizing maps algorithm, genetic algorithm