Energy-Adaptive and Robust Monitoring for Smart Farms Based on Solar-Powered Wireless Sensors

While smart farm technologies significantly aid in reducing costs and boosting productivity for farmers, they often lack the necessary robustness against cyberattacks and adaptability to dynamic environmental changes. We propose a solar-powered sensor-based smart farm system to provide high-monitoring quality while preserving sensor energy in the presence of adversarial attacks. In a smart farm system, solar-powered sensors are attached to animals (e.g., cows) to monitor their health under varying weather conditions to provide energy-adaptive and high-quality monitoring services. Further, a smart farm system should be robust against adversarial attacks aiming to disrupt monitoring quality. We use deep reinforcement learning (DRL) to identify the optimal policy for maximizing monitoring quality and prolonging the system's lifetime while maintaining sufficient energy. We introduce transfer learning (TL) into the DRL process to achieve fast learning without experiencing a cold start problem in DRL. In addition, we develop an uncertainty-aware anomaly data detection method to filter out deceptive data caused by adversarial attacks. Via extensive comparative performance analysis conducted based on real data sets, we demonstrate the superior performance of the proposed TL-based DRL strategies over existing competitive counterparts in the system lifetime, the monitoring quality, the learning convergence time, and the energy consumption.