IoT-UAV Enabled Intelligent Resource Management in Low-Carbon Smart Agriculture Using Federated Reinforcement Learning

Alasbali, Nada and Masood, Fahad and Alnazzawi, Noha and Ghaban, Wad and Alazeb, Abdulwahab and Basurra, Shadi and Saeed, Faisal (2025) IoT-UAV Enabled Intelligent Resource Management in Low-Carbon Smart Agriculture Using Federated Reinforcement Learning. IEEE Transactions on Consumer Electronics. p. 1. ISSN 0098-3063

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Abstract

The Internet of Things (IoT) and unmanned aerial vehicles (UAVs) continue to advance the low-carbon smart agriculture technologies for next-generation consumer electronics and unlock more informed agricultural practices. Reinforcement learning (RL), federated learning (FL), and federated reinforcement learning (FRL) have demonstrated notable achievements in resolving complex problems, including resource allocation, energy efficiency, anomaly detection, and bandwidth utilization for multimodal tasks. This research explores multimodal data analysis and resource optimization using FRL for agricultural consumer electronics. The proposed framework employs IoT devices to monitor temperature, humidity, soil temperature, and soil moisture in real time, while UAVs provide aerial imagery for soil moisture, crop growth, and pest identification across three fields. This framework supports distributed learning, which trains local RL models on each node and combines them into the global model. The proposed FRL model demonstrated significant enhancements, including a 17% reduction in energy consumption for IoT devices and a 15% reduction for UAVs compared to non-FRL methods. This research emphasizes the effectiveness of FRL in integrating IoT and UAV for efficient resource allocation, energy efficiency, and reduced carbon emissions for low-carbon agricultural consumer electronics.

Item Type:	Article
Identification Number:	10.1109/TCE.2025.3572552
Dates:	Date Event 13 May 2025 Accepted 22 May 2025 Published Online
Uncontrolled Keywords:	Federated Reinforcement Learning, IoT, UAV, Energy Efficiency, Agriculture Consumer Electronics.
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Gemma Tonks
Date Deposited:	10 Jun 2025 13:48
Last Modified:	10 Jun 2025 13:48
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16417

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