FRHO: Fuzzy rule-based hybrid optimization for optimal cluster head selection and enhancing quality of service in wireless sensor network

Abstract

The applications based on Wireless Sensor Network-enabled Internet of Things (IoT) are gaining more interest in civic and research communities. In IoT-based applications, network sustainability is considered the most significant factor. WSN provides a better sustainable network as it serves as a subnet in an IoT system; however, it suffers from redundancies such as packet loss during data transmission, more delay, minimized network lifetime, and increased energy consumption of nodes. Therefore, to address these issues, in this article, we proposed a fuzzy rule-based hybrid barnacle mating enhanced butterfly (FR-based hybrid BMEB) clustering approach to select the optimal Cluster Head (CH) to transfer data packets to the base station. The hybrid BMEB approach optimizes the hyperparameters and is used to select the fuzzy rules. To ensure the quality of service (QoS) requirements, the proposed system is classified into three main levels which include the setup process, steady-state process, and maintenance process. Thus, the proposed system efficiently minimized the energy consumption of nodes required for selecting the CH and prolongs the network lifetime. To verify the network performance, a comparative analysis is carried out between the proposed FR-based hybrid BMEB approach and state-of-the-art methods in terms of different evaluation metrics. The analytic result manifests that the proposed method yields high network lifetime and low energy consumption than other compared methods. Using the proposed FR-based hybrid BMEB approach, the network remained active for about 1100 s at 150 nodes and 975 s at 250 nodes which are greater than other existing methods.