SEACDSC: secure and energy-aware clustering based on discrete sand cat swarm optimization for IoT-enabled WSN applications

Abstract

Wireless sensor networks (WSNs) hold the promise of delivering new intelligent, cost-effective, and collaborative applications with the potential to have a great impact on our daily life. WSNs are often employed for detecting and tracking a wide range of entities involved in realistic scenarios where security is of vital importance. While selecting energy-efficient Cluster Heads (CHs) is the primary focus of the majority of clustering approaches currently in use in WSNs, researchers have not given adequate consideration to the security aspects of CHs when developing a CH selection strategy. Estimating the trust between the nodes not only makes the WSN secure, but also improves communication between nodes and makes the WSN more reliable. In this paper, we develop a secure and energy-aware clustering approach (SEACDSC) for WSNs by adapting sand cat swarm optimization algorithm (SCSO). SEACDSC incorporates a novel mechanism for determining secure and energy-efficient CHs among the WSN nodes. In particular, we propose a Discrete SCSO method, a variant of the traditional SCSO, to facilitate the secure and efficacious selection of CHs. The fitness function is designed by considering nodes’ remaining energy and trust values for choosing CH efficiently. Furthermore, the exponential weighted moving average (EWMA) is used for dynamically updating the predefined threshold values following the network state. As demonstrated by the simulation results, SEACDSC outperforms the existing BAT-Based, MG-LEACH, Enhanced-LEACH, Improved-Leach, and RCH-LEACH techniques in terms of network stability, number of alive nodes, energy efficiency, reliability, average trust value of CHs and network lifetime.