Abstract:

A novel hybrid algorithm based on particle swarm optimization and tabu search is proposed for multi-objective optimized TDMA scheduling in the applications for wireless sensor networks which require periodic global data snapshots, and weighted entropy based technique for order preference by similarity to ideal solution is introduced to evaluate and choose the result of the algorithm objectively. When the amount of data collection tasks of the network is constant, it needs only fewer slots and energy expenditure to complete them by using the algorithm. Simulation results validate that the proposed scheme has a highly search efficiency and can improve the effect of the TDMA scheduling on wireless sensor networks and outperforms compared with that of other algorithms.