An enhanced fuzzy-based clustering protocol with an improved shuffled frog leaping algorithm for WSNs

Wireless sensor networks (WSNs), an important technology for the Internet of Things (IoT), consist of the communications among small sensor nodes in a large-scale network. Due to the limited battery capacity of each small node, a clustering-based approach is used to conserve energy among the nodes and increase the network lifetime. The tasks of a clustering-based protocol are related mainly to the selection of the most appropriate cluster head nodes (CHs) and next-hop nodes (NHs) based on many influencing factors that require an optimal decision criterion, which can be addressed using a fuzzy inference system (FIS). Although FISs can provide satisfying decisions for selecting CHs and NHs for clustering protocols, FIS components such as fuzzy input variables, fuzzy rules, and fuzzy membership functions continue to be defined manually in most methods. Thus, these parameters must be tuned appropriately for specific applications. Therefore, in this paper, an enhanced fuzzy-based clustering protocol and an improved shuffled frog leaping algorithm (ISFLA) are proposed. In the proposed protocol, named EFC-ISFLA, a fuzzy-based clustering protocol optimized by the ISFLA is developed to maintain the network lifetime. The appropriate CHs are selected based on the energy threshold and optimized FIS with respect to the distance between adjacent CHs defined by the overlay boundary, resulting in reduced energy consumption and a longer network lifetime. Additionally, cluster formation and NH selection are performed based on the optimized FISs. A new encoding scheme is also designed to tune the network parameters and the FIS components simultaneously through the ISFLA. In the ISFLA, opposition-based operators and a surrogate model are adopted to address the limitations of the traditional SFLA. The experimental results show that the proposed technique provides better results in terms of maximizing the network lifetime, network stability, and total number of data packets delivered to the base station (BS). •Develop a metaheuristic fuzzy-based clustering protocol for WSN.•Propose an overlay boundary concept to adjust proper distance for cluster head selection.•Design an encoding scheme of optimization process for fuzzy parameters.•Improve a shuffled frog leaping algorithm with opposition-based operators to reduce convergence.•Perform an intensive evaluation over recent state of the art WSN clustering methods.