Work

Collision Detection in LoRaWAN Using Machine Learning

Year: 2024

Type: conference-paper

Abstract: In this paper, we investigate the application of machine learning techniques as a productive solution for carrying out LoRaWAN packet collision detection. Our focus is on a common channel that is shared by several LoRa end-devices via the asynchronous ALOHA protocol, with the aim of delivering data packets in IoT scenarios. We evaluate the effectiveness of two algorithms in this setup: Random Forest and Decision Tree. Higher efficiency is exhibited for identification and likelihood of False Positive Rate, even when packets transmissions collide.

Source: IEEE International Symposium on Intelligent Systems and Informatics (SISY)

DOI: 10.1109/sisy62279.2024.10737620

Authors: Nabil Abdoun, Samira Abboud, Haya Altaleb, Zoltán Rajnai

Institutions: Lebanese American University, École Supérieure d'Électronique de l'Ouest, Obuda University

Language: English

Topic: IoT Networks and Protocols

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W4403382473

FWCI: 0.781

Cites: 13

Cited by: 1

Related to: 10