A LoRaWAN-Based Smart Monitoring System for Animal and Traffic Safety on Highways

This paper proposes a Long Range Wireless Area Network (LoRaWAN)-based system to enhance road safety by preventing animal-vehicle collisions on highways, a critical issue in Saudi Arabia where camels frequently cross roads, endan-gering animals and humans. The system employs infrared (IR) cameras along highways to detect animals near the road and vehicles on the road. Captured images are wirelessly transmitted to a regional station via LoRaWAN. Based on data analysis, a three indicator alarm system (red, yellow, green) alerts drivers: red indicates animal presence, yellow indicates road congestion, and green signals normal conditions. The system achieved 99.8 % accuracy in detecting both camels and vehicles, effectively reducing collision risks. LoRaWAN enables efficient data transmission across long distances with minimal infrastructure, making this solution particularly suited for remote areas.

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Publication Details

Published
2024-12-11
DOI
https://doi.org/10.1109/ieeeconf63577.2024.10881957
Primary Topic
IoT Networks and Protocols
Type
article
Field-Weighted Citation Impact
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article

A LoRaWAN-Based Smart Monitoring System for Animal and Traffic Safety on Highways

Waleed Saad
IoT Networks and Protocols
article

A LoRaWAN-Based Smart Monitoring System for Animal and Traffic Safety on Highways

Waleed Saad
article en

Abstract

This paper proposes a Long Range Wireless Area Network (LoRaWAN)-based system to enhance road safety by preventing animal-vehicle collisions on highways, a critical issue in Saudi Arabia where camels frequently cross roads, endan-gering animals and humans. The system employs infrared (IR) cameras along highways to detect animals near the road and vehicles on the road. Captured images are wirelessly transmitted to a regional station via LoRaWAN. Based on data analysis, a three indicator alarm system (red, yellow, green) alerts drivers: red indicates animal presence, yellow indicates road congestion, and green signals normal conditions. The system achieved 99.8 % accuracy in detecting both camels and vehicles, effectively reducing collision risks. LoRaWAN enables efficient data transmission across long distances with minimal infrastructure, making this solution particularly suited for remote areas.

Shaqra University (SA)
Openalex Percentile: Top 74%
IoT Networks and Protocols
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