Energy efficient clustering in IoT-based wireless sensor networks using binary whale optimization algorithm and fuzzy inference system

The Internet of Things (IoT) offers substantial potential for enhancing real-time interaction between various smart components within a network. To reduce communication costs in the IoT infrastructure, wireless sensors can be employed as a cost-effective solution. The widespread applications of wireless sensor networks (WSNs) across various domains have significantly increased their adoption in recent years. A major challenge in these networks is the limited energy of nodes, which has driven efforts to improve energy management using more precise clustering techniques. Although numerous methods have been proposed to enhance clustering accuracy and reduce energy consumption, not all of them achieve optimal throughput. Addressing energy consumption challenges in IoT-based WSNs, this paper proposes an efficient clustering-based routing protocol. The protocol utilizes a multi-objective binary whale optimization algorithm (BWOA) for optimal cluster head (CH) selection, considering energy, node degree, and distance parameters. Additionally, a Mamdani-type fuzzy inference system (FIS) is employed for cluster formation to enhance energy efficiency. The FIS inputs include CH residual energy, neighborhood degree, and distance, with the output determining the connection probability of a sensor node to a CH. A multi-hop routing process based on the shortest path is implemented for data packet transmission. Simulations across various scenarios demonstrate the superior performance of the proposed BWOA based on V-shaped transfer function over the BWOA based on S-shaped transfer function and other related methods. Comparative analysis reveals that the proposed protocol effectively addresses key challenges in IoT-based WSNs, such as network lifetime and energy consumption, contributing to the development of more sustainable and efficient IoT infrastructures. When contrasted with the top-performing protocol, the proposed method exhibits substantial improvements in multiple crucial aspects. Notably, the FND metric has experienced a 4.5% increase, the HND measure has seen a 7.8% enhancement, and the LND benchmark has been elevated by 1.5%, indicating the potential impact of the proposed approach in the domain.