Citation

Kabir, Fardin and Roslee, Mardeni and Abas, Anas and Ali, Farman and Ullah, Yasir and Khan, Irfan Ullah and Kabir, Fahmid (2026) Performance Evaluation Of Lora-Based Data Transmission For UAV Environmental Monitoring Systems. International Journal of Artificial Intelligence and Machine Learning, 6 (7s). pp. 467-474. ISSN 2789-2557

Text IJAIML+-+Vol.6,+No.7s,+2026+-+37.pdf - Published Version Restricted to Repository staff only Download (480kB)

Abstract

This paper presents the design and evaluation of a low-cost LoRa-based communication system for environmental data transmission using an unmanned aerial vehicle (UAV). The proposed system enables direct, long-range communication between a drone-mounted sensor unit and a ground receiver without depending on internet or cellular networks. Both nodes are built using ESP32 microcontrollers and SX1278 LoRa transceivers, while onboard sensors measure CO, NO₂, VOC concentration, temperature, and humidity. Real-time data are displayed through a lightweight HTML dashboard accessible within the same Wi-Fi network. Field experiments were performed under three weather conditions sunny, rainy, and windy at multiple UAV altitudes to analyze link stability, signal quality, and packet delivery. Results showed that LoRa maintained over 94% packet delivery success up to 2 km range, with the best performance observed in sunny conditions. The findings confirm that LoRa technology provides a reliable, energy-efficient, and scalable solution for UAV-based environmental monitoring in remote areas. Future improvements will include GPS integration, adaptive transmission control, and multi-drone coordination for larger network coverage.

Item Type:	Article
Uncontrolled Keywords:	LoRa communication, UAV monitoring, Wireless data transmission, Environmental sensing, Weather impact, ESP32, Long-range IoT
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Sep 2026 06:57
Last Modified:	04 Sep 2026 06:57
URII:	http://shdl.mmu.edu.my/id/eprint/16729

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