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Wireless sensor nodes (WSNs) are useful to monitor animals remotely and continuously. The proposed WSN aims to monitor pig activities, and it consists of a 3-axis accelerometer, a 3-axis gyroscope, and a microcontroller with embedded BLE (Bluetooth Low Energy) radio. The WSN was designed and prototyped with a custom PCB and used to collect data from pigs in field for about 131 hours, and the collected data was processed to classify pig behaviors with machine learning models. The sampling rate of the sensors is 10 samples per second. The proposed WSN dissipates 6.29 mW, on average, and the peak power dissipation is 41.01 mW during transmission of the sensed data. The WSN is estimated to operate for about three weeks with a coin cell battery CR2477. 

In this work, we propose an energy-adaptive moni-toring system for a solar sensor-based smart animal farm (e.g., cattle). The proposed smart farm system aims to maintain high-quality monitoring services by solar sensors with limited and fluctuating energy against a full set of cyberattack behaviors including false data injection, message dropping, or protocol non-compliance. We leverage Subjective Logic (SL) as the belief model to consider different types of uncertainties in opinions about sensed data. We develop two Deep Reinforcement Learning (D RL) schemes leveraging the design concept of uncertainty maximization in SL for DRL agents running on gateways to collect high-quality sensed data with low uncertainty and high freshness. We assess the performance of the proposed energy-adaptive smart farm system in terms of accumulated reward, monitoring error, system overload, and battery maintenance level. We compare the performance of the two DRL schemes developed (i.e., multi-agent deep Q-Iearning, MADQN, and multi-agent proximal policy optimization, MAPPO) with greedy and random baseline schemes in choosing the set of sensed data to be updated to collect high-quality sensed data to achieve resilience against attacks. Our experiments demonstrate that MAPPO with the uncertainty maximization technique outperforms its counterparts.