Optimizing Sleep Schedules for Energy-Efficient Agricultural Wireless Sensor Networks Using Deep Reinforcement Learning

In recent years, energy-efficient wireless sensor networks (WSNs) have gained significant attention, particularly in agriculture, where activating numerous sensors across a farm can be highly energy-intensive. Energy-efficient WSNs ensure normal operations while extending their lifetime through either energy-harvesting hardware units or optimized sleep schedule algorithms. This paper proposes an innovative deep reinforcement learning algorithm in order to optimize sleep schedules across entire WSNs. By utilizing historical sensor data, the algorithm predicts the optimal times for different sensor types to wake up, allowing them to capture valuable variations in data. The sensors then return to sleep mode after recording the necessary data, significantly extending the WSN’s lifetime and reducing battery replacement frequency. The results in the simulation show the Deep Reinforcement Learning accomplished the best performance in algorithm in optimizing sleep schedules for energy-efficient WSNs.