Energy Management using Optimal Fuzzy Logic Control in Wireless Sensor Network

Energy is a key factor that affects the lifetime of wireless sensor network (WSN). This paper proposes an adaptive energy management model to improve the energy efficiency in WSN. Unlike existing clustering routing protocols, the overall performance indicators are introduced as the inputs of fuzzy logic control (FLC). Meanwhile, the probability adjustment value, as the out of FLC, is fed back to the network for the generation of new clusters. Since the design of membership functions (MFs) of FLC has a significant impact on system performance, a particle swarm optimization (PSO) algorithm is used to optimize MFs and its optimization goal is to reduce the number of dead nodes and increase the remaining energy level in WSN. Simulation experiments were conducted for the low energy adaptive clustering hierarchy protocol (LEACH), the conventional FLC, FLC using genetic algorithm (GA), and FLC using PSO. The results show that the proposed FLC-PSO has the best performance among the four protocols and it can be used efficiently in energy management of WSN