A Fuzzy-Logic Based Adaptive Data Rate Scheme for Energy-Efficient LoRaWAN Communication

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

5 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number65
Journal / PublicationJournal of Sensor and Actuator Networks
Volume11
Issue number4
Online published11 Oct 2022
Publication statusPublished - Dec 2022

Link(s)

Abstract

Long Range Wide Area Network (LoRaWAN) technology is rapidly expanding as a technology with long distance connectivity, low power consumption, low data rates and a large number of end devices (EDs) that connect to the Internet of Things (IoT) network. Due to the heterogeneity of several applications with varying Quality of Service (QoS) requirements, energy is expended as the EDs communicate with applications. The LoRaWAN Adaptive Data Rate (ADR) manages the resource allocation to optimize energy efficiency. The performance of the ADR algorithm gradually deteriorates in dense networks and efforts have been made in various studies to improve the algorithm’s performance. In this paper, we propose a fuzzy-logic based adaptive data rate (FL-ADR) scheme for energy efficient LoRaWAN communication. The scheme is implemented on the network server (NS), which receives sensor data from the EDs via the gateway (GW) node and computes network parameters (such as the spreading factor and transmission power) to optimize the energy consumption of the EDs in the network. The performance of the algorithm is evaluated in ns-3 using a multi-gateway LoRa network with EDs sending data packets at various intervals. Our simulation results are analyzed and compared to the traditional ADR and the ns-3 ADR. The proposed FL-ADR outperforms the traditional ADR algorithm and the ns-3 ADR minimizing the interference rate and energy consumption.

Research Area(s)

  • adaptive data rate, fuzzy logic ADR, internet of things, LoRa, LoRaWAN, LPWAN

Download Statistics

No data available