Energy-Saving Clustering Routing Protocol for Wireless Sensor Networks Using Fuzzy Inference

Abstract

One of the most important researches in wireless sensor networks is energy saving. The clustering algorithm can efficiently save energy. However, most of the existing clustering algorithms use a fixed cluster head election algorithm and perform each round of clustering, so that unsuitable nodes are continuously elected as cluster heads, leading to rapid exhaustion of node energy and short network life. In order to solve these problems, this paper proposes an Energy-saving Fuzzy Clustering Routing algorithm (EFCR) for wireless sensor networks,which designs two types of clustering, Clustering Type1 and Clustering Type2, and alternately executes them in different rounds according to the threshold. Clustering Type1 considers the parameters that affect the energy consumption of the node: the distance from the node to the base station, the number of neighbor nodes of the node, and the remaining energy of the node as fuzzy inputs, and calculates the fitness of the node as a cluster head through fuzzy inference to avoid the occurrence of unsuitable nodes being successively elected as cluster heads. Clustering Type2 performs data transmission by trusting the last round of cluster heads, which reduces the number of control data packets in each round of clustering and reduces the overall energy consumption of the network. This paper conducted simulations in four different scenarios, compared other clustering algorithms of the same type, and analyzed the remaining energy of the network, the number of surviving nodes, and the life of the network. The results show that EFCR algorithm is better than others.