RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor Networks

Abstract

Applications of wireless sensor networks have significantly increased in the modern era. These networks operate on a limited power supply in the form of batteries, which are normally difficult to replace on a frequent basis. In wireless sensor networks, sensor nodes alternate between sleep and active states to conserve energy through different methods. Duty cycling is among the most commonly used methods. However, it suffers from problems like unnecessary idle listening, extra energy consumption, and packet drop rate. A Deep Reinforcement Learning-based B-MAC protocol called (RL-BMAC) has been proposed to address this issue. The proposed protocol deploys a deep reinforcement learning agent with fixed hyperparameters to optimize the duty cycling of the nodes. The reinforcement learning agent monitors essential parameters such as energy level, packet drop rate, neighboring nodes’ status, and preamble sampling. The agent stores the information as a representative state and adjusts the duty cycling of all nodes. The performance of RL-BMAC is compared to that of conventional B-MAC through extensive simulations. The results obtained from the simulations indicate that RL-BMAC outperforms B-MAC in terms of throughput by 58.5%, packet drop rate by 44.8%, energy efficiency by 35%, and latency by 26.93%

Keywords

• wireless sensor networks
• machine learning
• energy optimization
• deep reinforcement learning
• MAC protocols