Energy Efficient Transmission Parameters Selection Method Using Reinforcement Learning in Distributed LoRa Networks

TL;DR

This paper introduces a reinforcement learning-based method for selecting transmission parameters in LoRa networks to enhance energy efficiency, addressing computational limitations of IoT devices and outperforming existing approaches.

Contribution

A distributed reinforcement learning approach for transmission parameter selection in LoRa networks, suitable for resource-constrained devices, improving energy efficiency and success rates.

Findings

Outperforms fixed assignment and ADR-Lite in success rate

Reduces power consumption compared to baseline methods

Effective in real LoRa network experiments

Abstract

With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multi-armed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and $\epsilon$-greedy-based methods in terms of both transmission success rate and energy efficiency.