Experimental Analysis of LoRaWAN for Optimizing Water Quality Monitoring with Reinforcement Learning-Driven Scheduling

Long Range Wide Area Network (LoRaWAN) is a promising communication technology for environmental monitoring due to its low power consumption and long-range capabilities. Despite its advantages, several challenges are associated with LoRaWAN due to technical limitations, environmental factors, and operational complexities. Continued advancements in adaptive algorithms and AI-based optimization are essential for overcoming these challenges and fully realizing the potential of LoRaWAN in diverse IoT applications. Transmission parameter allocation is one of the most studied aspects of LoRaWAN, typically required to reduce energy consumption and improve the signal quality in dense LoRaWAN. Evaluations of the optimization algorithms for parameter allocation are usually done using simulators. However, they do not imitate the dynamic nature of the network environment and other signal characteristics. Thus, it becomes difficult to understand the performance of these algorithms when deployed on real devices. This paper introduces transmission parameter allocation strategies using a State–Action–Reward–State–Action (SARSA) and Deep Q-Learning Network (DQN) based Reinforcement Learning (RL)-based scheduling algorithm for allocating transmission parameters in LoRaWAN communication. We experimentally evaluate this algorithm in a water quality monitoring system using actual LoRaWAN devices to assess Signal to Noise ratio (SNR), Received Signal Strength Indicator (RSSI), Time on Air (ToA), and power consumption of our RL-based algorithms with the default and Adaptive Data Rate (ADR) in LoRaWAN communication.