<![CDATA[Energy efficiency is a critical determinant in the design and operation of Wireless Sensor Networks (WSNs), as sensor nodes are typically powered by constrained battery resources. Asynchronous duty cycle mechanisms have emerged as a viable strategy to optimize energy consumption while preserving network functionality. This research presents a comparative analysis of multiple energy-efficient Medium Access Control (MAC) protocols, including Low-Energy Adaptive Clustering Hierarchy (LEACH), Energy-Efficient Sensor Routing (EESR), B-MAC, L-MAC, WiseMAC, and hybrid approaches such as TDMA-CSMA. Performance metrics such as energy efficiency, latency, throughput, and packet delivery ratio (PDR) are evaluated under varying network conditions. The findings indicate that AI-driven protocols, particularly those incorporating Artificial Neural Networks (ANN), significantly outperform conventional methodologies by enhancing cluster head selection, distributing energy load effectively, and extending network lifetime. Hybrid ADC emerges as the most robust solution, demonstrating an optimal trade-off between energy efficiency and network reliability across dynamic traffic scenarios. Furthermore, This research highlights the implications of integrating adaptive duty cycling with intelligent network optimization, underscoring its potential to enhance WSN sustainability. The results provide a comprehensive framework for refining MAC protocol architectures, offering actionable insights for optimizing next-generation WSN deployments.]]>
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