Publication

Abstract : Clustering is a very efficient technique in reducing the energy consumption by sensor nodes in an energy-constrained WSN. However, the combination of a static sink and poor clustering algorithms leads to hotspots in energy consumption, and thus reduces the lifetime of the network. Based on these issues, we propose a novel energy-efficient routing approach integrating PSO for CH selection along with a mobile sink. It dynamically identifies optimal CHs based on energy availability, coverage, and distance metrics. This ensures that there is effective energy balancing across the network. Meanwhile, mobile sink follows an adaptive movement pattern, thus minimizing energy-intensive transmissions and enhancing network lifetime. Adaptive clustering combined with PSO-based optimization, real-time sink trajectory recalculation, and dynamic reconfiguration provide robustness in large-scale WSNs, especially in applications such as environmental monitoring and disaster response. Simulation results show that energy efficiency, network lifetime, and data delivery reliability are significantly improved.