Enhancing Network Lifetime in IoT-Based Wireless Sensor Networks Through MSSA-Driven Cluster Head Optimization

The integration of the Internet of Things with WSN s has given emergence to various applications in industries starting from precision agriculture to healthcare, and smart cities. These networks completely depend on well-organized data collection and transmission, where energy consumption is one of the crucial issues. One of the major challenges in WSN is the optimum selection of cluster heads that can directly influence energy efficiency and network lifetime and hence the overall performance. In this context, this paper deals with this challenge by proposing a new method of selecting cluster heads based on Multi-Objective Salp Swarm Algorithm-MSSA. MSSA is proposed to deploy loT for data gathering in the WSN, therefore optimization of cluster head selection have been done with MSSA application. We assess the performance of this approach by comparing it with various established algorithms such as LEACH, PSO, GA, and FCM. These will provide performance metrics necessary for energy consumption, network lifetime, and packet delivery ratio; thus, all critical parameters determining WSN's sustainability could be given in an overview. The experimental results depict that the MSSA-based approach outperforms the benchmark algorithms in terms of energy consumption, lifetime elongation of the network, and data transmission efficiency. This research contributes to the literature by providing an advanced solution to the problem of cluster head selection, offering a more energy-efficient and scalable approach to IoT-enabled WSN applications, such as environmental monitoring and smart infrastructure management.