Energy-Efficient Cognitive Wireless Sensor Networks: Performance Evaluation and Optimization Techniques

The growing intricacy of systems and the need for energy-efficient methods for addressing communications in wireless sensor networks (WSNs) pose challenges in network design, functionality, and data integrity due to the limited energy budget. Dynamic power management (DPM) can reduce energy expenditure by turning certain parts of a node on and off, but the cost of this switching can offset energy savings. This paper presents a new framework for cognitive wireless sensor networks (CWSNs) aimed at enhancing DPM. The framework incorporates a reinforcement logic based approach to select optimal relay nodes and channels based on their remaining energy and the level of congestion in the channel. Additionally, when its coverage

coefficient is highest, a node acts as a distribution center, thereby improving the network’s throughput and reducing power loss. The simulations achieved 4,000,000 bits of throughput, 99.98% energy efficiency, 95% packet delivery, and 78% spectrum utilization. The results demonstrate improved balance between energy efficiency, data reliability, and spectral efficiency and provide a basis for constructing resilient WSNs for intelligent monitoring and the future Internet of Things (IoT) networks.

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