Memetic fuzzy clustering protocol for wireless sensor networks: Shuffled frog leaping algorithm

Abstract

The importance of Wireless Sensor Networks (WSNs) has increased owing to their extensive advances, allowing integration of nano-sensors, wireless networks, and smart software. The main challenge in WSNs is the fast sensor energy discharging. One of the most effective approaches to deal with this issue is clustering and selecting appropriate cluster heads. This study presents a Fuzzy Shuffled Frog Leaping Algorithm (FSFLA), which employs the memetic Shuffled Frog Leaping Algorithm (SFLA) to optimize the Mamdani fuzzy rule-base table based on the application specifications. In addition to automatically adjusting the if-then fuzzy rules, this protocol optimizes five controllable parameters associated with the inputs to the fuzzy system in an offline procedure prior to launching the network. The inputs of the fuzzy systems include remaining energy, distance from the base station, the number of neighboring nodes, and node histories. The proposed clustering algorithm can be adjusted according to the application due to having two determined thresholds for turning candidate nodes to final cluster heads. The proposed FSFLA protocol is compared to various protocols such as LEACH, LEACH-DT, SIF, and ASLPR, in terms of the number of the network lifetime, remaining energy, the number of packets successfully received at the base station and intra-cluster distance. The simulation results indicate that the proposed FSFLA clustering protocol, which is implemented in two versions, significantly outperforms other protocols in all scenarios.