Energy-Adaptive, Robust Monitoring for Solar Sensor-based Smart Farms
Under Adversarial Attacks
Abstract
We propose a solar sensor-based smart farm system to provide high
monitoring quality while preserving sensor energy in the presence of
adversarial attacks. Since solar sensors are attached to cows to monitor
their health under varying weather conditions, ensuring that the system
provides energy-adaptive, high-quality monitoring services is critical.
Further, the smart farm system should be robust against diverse
adversarial attacks that will disrupt its monitoring quality. We use
deep reinforcement learning (DRL) to identify the optimal policy for
maximizing monitoring quality and prolonging the systemâ\euro™s
lifetime while maintaining sufficient energy. We introduce transfer
learning (TR) into the DRL process to achieve fast learning by DRL
without experiencing a cold start problem. 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 in our experiments based on real
datasets, we demonstrate the superior performance of TL-based DRL
strategies over other competitive counterparts regarding system
lifetime, monitoring quality, learning convergence time, and energy
consumption.