Dynamic Parameter Allocation with Reinforcement Learning for LoRaWAN

Document Type

Article

Source of Publication

IEEE Internet of Things Journal

Publication Date

6-15-2023

Abstract

LoRaWAN attracted lots of attention with its capacity for large device numbers, long-range, and low-power consumption. In order to simplify the transmission procedure, a pure Aloha protocol is implemented into its MAC layer. However, as the number of connected devices to the base station increases, the devices' transmission parameters allocation becomes a vital issue related to network performance. This research contributes to the decentralized dynamic spreading factor (SF) allocation strategies during transmission by proposing a score table-based evaluation and parameters surfing (STEPS) approach. STEPS is a reinforcement learning-based method that evaluates and changes the parameters based on probability and score tables. It provides a nondeterministic parameter selection method by updating the table while transmitting. Some variants of STEPS with different algorithms are proposed. Moreover, an estimation-based initialization is proposed to improve learning performance. Simulations and statistical tests are carried out with MULANE, a lightweight LoRaWAN Simulator developed in our previous work. The results show that the estimation has a high confidence level. Compared with the baseline methods, the proposed methods reduce energy consumption by 24%-27% in different numbers of nodes. For bi-directional transmission, the proposed methods increase the 18% network throughput in a small number of nodes and 33% in a large number of nodes. Moreover, the proposed methods provide a framework of decentralized parameter allocation, which gives the extendability of this work.

ISSN

2327-4662

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

10

Issue

12

First Page

10250

Last Page

10265

Disciplines

Computer Sciences

Keywords

Decentralized spreading factor (SF) allocation, energy consumption, LoRaWAN, reinforcement learning

Scopus ID

85147289009

Indexed in Scopus

yes

Open Access

no

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