Dynamic Parameter Selection of LoRa Edge Nodes Using Reinforcement Learning With Link Prior Knowledge

LoRa is widely used in low-power Internet of Things (IoT) applications due to its low-power consumption, high-connection density, and wide coverage. How to optimize the physical layer parameter configuration of LoRa edge nodes is a bottleneck issue that restricts network performance. To reduce the energy consumption of the distributed LoRa networks, a reinforcement learning strategy based on link prior knowledge is adopted, and the resource allocation of the edge node of the LoRa networks is transformed into a multiarmed bandit problem. On this basis, a dynamic parameter selection algorithm suitable for LoRa edge nodes is proposed, namely, the link-weight-EXP3 (LI-WEX) algorithm. By defining the weight factors of parameters and rewards for nodes, the energy consumption factor in decision-making is enhanced, and the policy space at the node is processed based on link knowledge, thereby improving energy efficiency by selecting the optimal combination of parameters. The simulation results show that the LI-WEX algorithm not only converges faster and is robust in different scenarios, but also can effectively reduce network energy consumption.