A Novel Approach to Improve the Adaptive-Data-Rate Scheme for IoT LoRaWAN

Al-Gumaei, Yousef Ali Mohammed, Aslam, Nauman, Aljaidi, Mohammad, Al-Saman, Ahmed, Alsarhan, Ayoub and Ashyap, Adel Y. (2022) A Novel Approach to Improve the Adaptive-Data-Rate Scheme for IoT LoRaWAN. Electronics, 11 (21). p. 3521. ISSN 2079-9292

[img]
Preview
Text
electronics-11-03521-v2.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (463kB) | Preview
Official URL: https://doi.org/10.3390/electronics11213521

Abstract

The long-range wide-area network (LoRaWAN) uses the adaptive-data-rate (ADR) algorithm to control the data rate and transmission power. The LoRaWAN ADR algorithm adjusts the spreading factor (SF) to allocate the appropriate transmission rate and transmission power to reduce power consumption.The updating SF and transmission power of the standard ADR algorithm are based on the channel state, but it does not guarantee efficient energy consumption among all the nodes in complex environments with high-varying channel conditions. Therefore, this article proposes a new enhancement approach to the ADR+ algorithm at the network server, which only depends on the average signal-to-noise ratio (SNR). The enhancement ADR algorithm ADR++ introduces an energy-efficiency controller α related to the total energy consumption of all nodes, to use it for adjusting the average SNR of the last records. We implement our new enhanced ADR at the network server (NS) using the FLoRa module in OMNET++. The simulation results demonstrate that our proposed ADR++ algorithm leads to a significant improvement in terms of the network delivery ratio and energy efficiency that reduces the network energy consumption up to 17.5% and improves the packet success rate up to 31.55% over the existing ADR+ algorithm.

Item Type: Article
Uncontrolled Keywords: LoRaWAN, energy efficiency, adaptive data rate, SNR
Subjects: G400 Computer Science
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 17 Nov 2022 12:25
Last Modified: 17 Nov 2022 12:30
URI: https://nrl.northumbria.ac.uk/id/eprint/50682

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics