Smart Cluster Head Selection Using Novel Duty Cycling and Sleep Schedule Optimization with Data-Aggregation for Wireless Sensor Network

This research aims to enhance the energy efficiency and operational lifetime of Wireless Sensor Networks (WSNs) by introducing a novel approach for selecting cluster heads. The proposed methodology focuses on smart cluster head selection using innovative duty cycling and sleep schedule optimization strategies. It involves dynamic adjustments of duty cycling parameters and sleep schedules based on real-time network conditions and performance metrics. Additionally, advanced data aggregation techniques are incorporated to reduce redundancy and conserve energy. Adaptive algorithms are utilized to optimize cluster head selection criteria for energy-efficient operation while meeting application-specific requirements. By aggregating data at the network level, we effectively mitigate redundancy and conserve energy, thereby enhancing overall efficiency. Incorporating adaptive algorithms, our framework optimizes cluster head selection criteria to ensure energy-efficient operation while accommodating application-specific requirements. The proposed methodology demonstrates significant improvements in network performance, as validated by experimental results, ultimately leading to prolonged Wireless Sensor Network lifetimes.