AI-Driven Energy Management Techniques for Enhancing Network Longevity in Wireless Sensor Networks - Hadi, AL-Shukrawi Ali Abbas; Wahab, Aeizaal Azman Bin Abdul; Hamzah, Firdaus Mohamad; Veena, B. S. (Journal of Robotics and Control (JRC), 2025)
WSNs and mobile systems are critical for monitoring and data collection, but energy efficiency remains one of the biggest challenges due to very limited battery life in sensor nodes. The issue here is the challenge of energy management by adopting sophisticated optimization techniques and AI-driven methodologies. This research develops a Q-learning model of dynamic energy optimization. The proposed method uses MATLAB simulations and real-world testing to validate improvements. The methodology employs adaptive routing and real-time power adjustments, which optimize energy usage. The results show a 34.92% increase in energy savings compared to traditional methods, where baseline energy efficiency was 65%. The Packet Delivery Ratio (PDR) improved from a baseline of 85% to 96.38%, ensuring more reliable data communication. The network latency was reduced by 24 ms, from the initial 50 ms, thus enhancing real-time responsiveness. Q-learning approach was extended for an additional 10 hours against the 7-hour baseline established by conventional systems. These improvements are based on fully dynamic routing with online adjustments, which makes the network adaptive to changing environments. This methodology is promising for energy-efficient and high-performance communication systems in remote and critical applications. The findings contribute to sustainable network operations and reduce the maintenance costs, making WSNs viable for long-term deployments.