An energy-efficient fuzzy-based scheme for unequal multihop clustering in wireless sensor networks

Currently, wireless sensor networks (WSNs) are providing practical solutions for various applications, including smart agriculture and healthcare, and have provided essential support by wirelessly connecting the numerous nodes or sensors that function in sensing systems needed for transmission to backends via multiple hops for data analysis. One key limitation of these sensors is the self-contained energy provided by the embedded battery due to their (tiny) size, (in) accessibility, and (low) cost constraints. Therefore, a key challenge is to efficiently control the energy consumption of the sensors, or in other words, to prolong the overall network lifetime of a large-scale sensor farm. Studies have worked toward optimizing energy in communication, and one promising approach focuses on clustering. In this approach, a cluster of sensors is formed, and its representatives, namely, a cluster head (CH) and cluster members (CMs), with the latter transmitting the sensing data within a short range to the CH. The CH then aggregates the data and forwards it to the base station (BS) using a multihop method. However, maintaining equal clustering regardless of key parameters such as distance and density potentially results in a shortened network lifetime. Thus, this study investigates the application of fuzzy logic (FL) to determine various parameters and membership functions and thereby obtain appropriate clustering criteria. We propose an FL-based clustering architecture consisting of four stages: competition radius (CR) determination, CH election, CM joining, and determination of selection criteria for the next CH (relaying). A performance analysis was conducted against state-of-the-art distributed clustering protocols, i.e., the multiobjective optimization fuzzy clustering algorithm (MOFCA), energy-efficient unequal clustering (EEUC), distributed unequal clustering using FL (DUCF), and the energy-aware unequal clustering fuzzy (EAUCF) scheme. The proposed method displayed promising performance in terms of network lifetime and energy usage.