Attack-Resistant, Energy-Adaptive Monitoring for Smart Farms: Uncertainty-Aware Deep Reinforcement Learning Approach

This work proposes an energy-adaptive monitoring system for a smart farm using solar sensors attached to cows. The proposed system aims to achieve a high monitoring quality in the smart farm under fluctuating energy and cyber attacks disrupting the collection of sensed data from solar sensors, such as protocol noncompliance, false data injection, denial-of-service, and state manipulation. We adopt Subjective Logic, a belief model, to consider multidimensional uncertainty in sensed data. We employ deep reinforcement learning (DRL) for agents on gateways to collect high-quality sensed data from the solar sensors. The DRL agents aim to collect high-quality sensed data with low uncertainty and high freshness under fluctuating energy levels in solar sensors. We analyze the performance of the proposed energy-adaptive smart farm system in accumulated reward, monitoring error rate, and system overload. We conduct a comparative performance analysis of the uncertainty-aware DRL algorithms against their counterparts in choosing the number of sensed data to be updated to collect high-quality sensed data to achieve high resilience against attacks. Our results prove that multiagent proximal policy optimization (MAPPO) using the uncertainty maximization technique outperforms other counterparts, showing about 4% lower monitoring error rate and the system overload.