Integrated Context-Aware Duty Cycling And Clustering For Sustainable Wireless Sensor Networks In Environmental Monitoring Applicatrions

Abstract

Wireless Sensor Networks (WSNs) face a critical trade-off between data fidelity and network lifetime. Conventional duty cycling protocols, which rely on fixed schedules or residual energy alone, often cause premature node depletion and network instability. This paper introduces a novel, lightweight, context-aware duty cycling (CA-DC) mechanism that significantly extends network lifetime by dynamically adjusting sleep-wake schedules. Our primary innovation is the integration of three parameters: residual energy, local traffic load, and data entropy. Using data entropy as a control parameter allows nodes to remain active when sensing high value information, thereby maximizing the intelligence gathered per unit of energy. Unlike computationally intensive machine learning or optimization-based solutions, our mechanism employs a simple adaptive rule to balance energy conservation with data quality and traffic management. This prevents premature node death, mitigates congestion, and prioritizes the collection of meaningful data. Simulations validate that our proposed CA-DC protocol demonstrates significant improvements in both stability and lifetime. In terms of stability, CA-DC achieves gains of 45.1% over LEACH, 18.1% over VDC, and 6.9% over TDC-MAC, while in terms of lifetime (Last Node Dead) (LND), it outperforms LEACH, VDC, and TDC-MAC by 51.8%, 33.1%, and 24.1%, respectively. These results confirm the effectiveness of CA-DC in enhancing energy efficiency, stability, and overall network sustainability. By achieving enhanced network longevity with minimal computational overhead, our proposed mechanism offers a practical and effective solution for resource-constrained WSN deployments, ensuring both stability and efficiency.