Design and Implementation of Schwarz Information Criterion-Aided Intelligent Decentralized Resource Allocation in Dynamic LoRa Networks

2026-07-31

AI Summary

This study addresses the challenge of achieving low overhead, high energy efficiency, and rapid adaptation to environmental dynamics in LoRa networks using conventional resource allocation approaches. To this end, the authors propose a lightweight distributed learning framework that, for the first time, integrates the Schwarz Information Criterion (SIC) for detecting abrupt environmental changes in LoRa settings and combines it with Thompson Sampling to enable acknowledgment (ACK)-feedback-driven, decentralized selection of transmission parameters. The proposed method operates without centralized coordination and is designed to run efficiently on resource-constrained devices. Experimental results demonstrate that, in dense and dynamic scenarios, the approach improves transmission success rate from 64.0% to 71.1% and enhances energy efficiency from 293.9 bit/J to 328.3 bit/J.

Abstract

This paper proposes a lightweight distributed learning method for selecting transmission parameters in Long-Range (LoRa) networks that adapts to dynamically changing communication environments. In the proposed method, the Thompson Sampling (TS) is adopted for transmission parameter selection, whereas the Schwarz Information Criterion (SIC) is employed for environmental change detection. TS is a reinforcement learning approach that effectively balances exploration and exploitation by updating parameters based on probability distributions. Additionally, it demonstrates stable performance even with a small number of trials, thereby making it well-suited for LoRa end devices (EDs) with limited memory capacity and computational resources. Furthermore, to address the issue that TS-based methods strongly depend on past learning histories and therefore adapt slowly to abrupt changes in communication environments, a statistical change detection mechanism based on the SIC is integrated into our proposed method. SIC is adopted because it can detect environmental changes with low computational cost and is suitable for implementation on resource-constrained LoRa EDs. When a change in the communication environment is detected by SIC, the learning history of TS is reset, thereby enabling rapid re-learning under new environmental conditions. Moreover, to achieve fully distributed communication parameter selection while enhancing transmission reliability and energy efficiency, the proposed method relies solely on Acknowledgment (ACK) feedback and the selected transmission parameters. Experimental results demonstrate that the proposed method improves the transmission success rate from 64.0% to 71.1% and increases energy efficiency from 293.9 bit/J to 328.3 bit/J compared with the conventional Upper Confidence Bound (UCB)1-tuned scheme under high-density dynamic LoRa networks.

Problem

• LoRa networks
• dynamic environment
• resource allocation
• environmental change detection
• energy efficiency

Innovation

• Thompson Sampling
• Schwarz Information Criterion
• Decentralized Resource Allocation
• Dynamic LoRa Networks
• Environmental Change Detection