Deep reinforcement learning-based mechanism to improve the throughput of EH-WSNs

Energy Harvesting Wireless Sensor Networks (EH-WSNs) are widely adopted for their ability to harvest ambient energy. However, these networks face significant challenges due to the limited and continuously varying energy availability at individual nodes, which depends on unpredictable environmental sources. To operate effectively in such conditions, energy fluctuations need to be regulated. This requires continuous monitoring of each node's energy level over time and adaptively adjusting operations. State-of-the-art mechanisms often categorize nodes or discretize energy levels, leading to issues such as the inability to select appropriate actions based on the actual energy states of the nodes. This discretization simplifies the representation of energy states and reduces complexity, making it easier to design and implement. However, it overlooks subtle variations in energy levels, leading to inaccurate assessments and suboptimal performance. To overcome this limitation, this paper proposes an energy-aware transmission method based on the Deep Reinforcement Learning (DRL) algorithm that integrates Q-learning with Deep Neural Networks (DNNs). This method enables each node to adaptively select transmission actions based on its real-time energy state, improving responsiveness to dynamic network conditions. Simulation results show that the proposed method improves throughput by 11.79% compared to traditional methods. These findings demonstrate the effectiveness of DRL-based control in enhancing performance and energy efficiency in EH-WSNs.