Hybrid Learning Algorithm for Effective Coverage in Wireless Sensor Networks

Coverage is one of the main problems to be solved for wireless sensor networks (WSN). In some monitoring regions, the condition is very bad and worse cases often suddenly occur, the nodes of wireless sensor network need to dynamically change their position quickly and automatically re-coverage according to the monitoring events to achieve better monitoring results. The current algorithms are often limited to realize the optimal coverage of fixed region. Combined with artificial neural network, putting the improved growing neural gas with utility criterion algorithm into wireless sensor network, the network can rapid re-coverage with respond to the changed region especially for special environments. In order to speed the learning procedure, we use GA and SA which combines the ability of evolution of GA and probability searching of SA. The simulation results show that, compared with growing neural gas algorithm, growing neural gas with utility criterion algorithm and improved GNG algorithm, the improve GNG-U algorithm can reduce a lot of redundant nodes, improve mobility of the network, accelerate the rate of convergence and arrive optimal re-coverage.