Energy-efficient and secure WSN clustering for IoT using particle swarm optimization and advanced encryption standard

Abstract

Wireless sensor networks (WSNs) are made up of distributed sensor nodes that work together under energy and communication constraints. They support diverse internet of things (IoT) applications such as smart agriculture and environmental monitoring. This paper proposes a technique to optimize the WSN framework for secure and energy-efficient data transmission. To improve cluster formation and network energy consumption, the suggested model combines k-means clustering with particle swarm optimization (PSO). Inter-cluster data is encrypted by the cluster head (CH) using the advanced encryption standard (AES)-128. To protect data and save energy, the low-energy adaptive clustering hierarchy (LEACH) protocol uses a number of techniques. Energy efficiency, model accuracy, likelihood of privacy breaches, and network longevity are examples of performance metrics. The system is tested by Python simulations on the Intel Berkeley Research Lab (IBRL) real-world dataset, which includes 54 sensor nodes measuring temperature and humidity. The results demonstrate significant energy savings and a model accuracy of 96.50%, thereby reducing privacy breaches and extending network lifetime. The framework offers scalability, effective privacy monitoring, and adaptability to changing topologies.