MABs for Adaptive Resource Optimization in LoRa IoT Networks

This paper investigates Multi-Armed Bandit (MAB) algorithms for adapting the transmission parameters of a a 2.4GHz Long Range (LoRa) Internet of Things (IoT) network. The aim is to dynamically select the optimal transmission parameters to reduce energy use while ensuring reliable data transmission under dynamic operating conditions. For this, the most well-known MAB strategies: Upper Confidence Bound (UCB), Exponential weights for exploration and exploitation (EXP3), Epsilon-Greedy, Thompson Sampling (TS), and Tug-of-War (ToW), are thoroughly evaluated using a reward function that balances energy efficiency, data reliability, and data rate. Our simulation results indicate that UCB consistently achieves the best compromise between computational complexity and transmission performance in all considered dynamic LoRa IoT settings, including stochastic and nonstationary ones.

• Publication year

  2025

• Venue

  Cloudification of the Internet of Things

• Publication date

  2025-10-29

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/CIoT67574.2025.11410018
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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