iSFA: Intelligent SF Allocation Approach for LoRa-Based Mobile and Static End Devices

LoRaWAN (Long Range Wide Area Network) is a low-power, wide-area wireless communication protocol designed specifically for the Internet of Things (IoT) and machine-to-machine (M2M) applications that enable long-range, bidirectional communication between low-power devices. Lo-RaWAN employs Adaptive D...

Full description

Saved in:
Bibliographic Details
Main Authors: Hazarika, Anakhi, Choudhury, Nikumani
Format: Conference Proceeding
Language:English
Subjects:
Adaptive Data Rate
K-means clustering
Lo-RaWAN
Logic gates
Machine Learning
Machine learning algorithms
Performance evaluation
Reinforcement learning
Throughput
Wide area networks
Wireless communication
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:LoRaWAN (Long Range Wide Area Network) is a low-power, wide-area wireless communication protocol designed specifically for the Internet of Things (IoT) and machine-to-machine (M2M) applications that enable long-range, bidirectional communication between low-power devices. Lo-RaWAN employs Adaptive Data Rate (ADR) technology to dynamically adjust the data rate for each device based on its signal quality and distance from the gateway. ADR enables improved network performance, extends device battery life, and simplifies network management, making LoRaWAN suitable for various IoT deployments. However, the end devices' inefficient utilization of radio resources (e.g., spreading factor and transmission power) significantly degrades network performance, device battery life, and adaptability to changing network conditions. Machine Learning (ML) algorithms analyze and optimize the real-time network conditions to enhance network performance. This work aims to develop an ML-based approach that adaptively selects the most suitable Spreading Factor (SF) for end devices (ED). Two independent ML algorithms such as K-means and Reinforcement Learning (RL) have been applied to EDs and Gateways, respectively, to dynamically allocate SF for both static and mobile EDs. Through simulations, the performance of the proposed mechanism is analyzed in terms of packet success rate, convergence time, energy consumption, latency, and throughput.
ISSN:1558-2612
DOI:10.1109/WCNC57260.2024.10570655