Q-Learning-Based Medium Access Technology for Minimizing AoI in LoRa Wireless Relay Networks

Abstract

LoRaWAN has emerged as a leading communication protocol for Low Power Wide Area Networks (LPWANs), gaining widespread adoption across diverse Internet of Things (IoT) deployments. Our approach integrates LoRa relay devices and Age of Information (AoI) metrics to enhance network performance. The algorithm dynamically adjusts Spreading Factors (SFs) based on network conditions, utilizing reinforcement learning techniques for optimal SF selection. Key innovations in this paper include the strategic use of LoRa relays, which is particularly effective for mitigating signal attenuation in long-distance communication scenarios. Another significant advancement is the utilization of AoI in two crucial aspects: as a component of the reinforcement learning algorithm and as an evaluation metric. This novel approach prioritizes data freshness in transmission decisions, enabling the algorithm to optimize communication based on the timeliness of information. Simulations demonstrate significant performance improvements over baseline algorithms, achieving average AoI reductions of 23% in high-density scenarios and 31% in high data transfer environments. These results highlight the effectiveness of combining AoI metrics and intelligent relay selection in improving LoRaWAN performance for IoT applications.

Keywords

• Age of Information
• IoT system
• LoRa
• medium access control
• relay networks
• reinforcement learning