Schwarz Information Criterion Aided Multi-Armed Bandit for Decentralized Resource Allocation in Dynamic LoRa Networks

2025-12-26

AI Summary

To address the sluggish adaptation of existing UCB1-tuned distributed learning methods in dynamic LoRa networks—caused by excessive reliance on stale historical data—this paper proposes a lightweight, fully decentralized parameter optimization framework without centralized coordination. Our approach innovatively integrates the Schwarz Information Criterion (SIC) with UCB1-tuned to enable low-overhead environmental change detection and adaptive reset of learning histories. Coupled with an ACK-feedback-driven online learning mechanism, it empowers end devices to autonomously and collaboratively optimize channel selection, transmission power, and bandwidth—balancing transmission success rate and energy efficiency. Experimental results demonstrate that, compared to baseline methods, our solution improves transmission success rate by 19.3%, reduces energy consumption per successful transmission by 27.6%, and accelerates convergence after environmental changes by a factor of 3.2.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper proposes a lightweight distributed learning method for transmission parameter selection in Long Range (LoRa) networks that can adapt to dynamic communication environments. In the proposed method, each LoRa End Device (ED) employs the Upper Confidence Bound (UCB)1-tuned algorithm to select transmission parameters including channel, transmission power, and bandwidth. The transmission parameters are selected based on the acknowledgment (ACK) feedback returned from the gateway after each transmission and the corresponding transmission energy consumption. Hence, it enables devices to simultaneously optimize transmission success rate and energy efficiency in a fully distributed manner. However, although UCB1-tuned based method is effective under stationary conditions, it suffers from slow adaptation in dynamic environments due to its strong reliance on historical observations. To address this limitation, we integrate the Schwarz Information Criterion (SIC) to our proposed method. SIC is adopted because it enables low-cost detection of changes in the communication environment, making it suitable for implementation on resource-constrained LoRa EDs. When a change is detected by SIC, the learning history of UCB1-tuned is reset, allowing rapid re-learning under the new conditions. Experimental results using real LoRa devices demonstrate that the proposed method achieves superior transmission success rate, energy efficiency, and adaptability compared with the conventional UCB1-tuned algorithm without SIC.

Problem

• Optimizes LoRa transmission parameters for success rate and energy efficiency
• Enhances adaptation to dynamic communication environments using SIC
• Enables lightweight distributed learning on resource-constrained LoRa devices

Innovation

• Integrates Schwarz Information Criterion for change detection in dynamic environments
• Resets UCB1-tuned learning history upon change detection for rapid adaptation
• Enables distributed optimization of transmission success rate and energy efficiency