A multi-objective metaheuristic method for node placement in dynamic IoT environments

This study introduces an optimal Node Placement based on Enhanced Sand Cat Swarm Optimization (NP-ESCSO) algorithm, a novel metaheuristic approach for solving the node placement problem in dynamic IoT environments. By integrating a Tent chaotic map and a hybrid motion strategy, the algorithm achieves a robust balance between exploration and exploitation, ensuring superior performance in obstacle-rich environments. A newly developed multi-objective fitness function optimizes critical metrics such as coverage, energy efficiency, connectivity, and redundancy. The proposed method highlights its potential for scalable and cost-effective IoT network deployment, particularly in environments with complex obstacles. Furthermore, the algorithm exhibits faster convergence and superior adaptability, making it suitable for real-world applications. NP-ESCSO not only optimizes IoT systems efficiently but also offers significant advancements in reducing computational overhead, improving scalability, and ensuring dynamic adaptability. Simulations conducted on real-world maps demonstrate that NP-ESCSO achieves a coverage rate of 92.44%, an energy efficiency of 48.69%, and a redundancy value of 2.096, significantly outperforming baseline methods. Compared to existing algorithms, NP-ESCSO improves fitness values by up to 14% and other key performance indicators by 45%.