Energy-Efficient Flying LoRa Gateways: A Multi-Agent Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2502.03377v2
• Date: Sun, 13 Apr 2025 17:52:57 GMT
• Title: Energy-Efficient Flying LoRa Gateways: A Multi-Agent Reinforcement Learning Approach
• Authors: Abdullahi Isa Ahmed, Jamal Bentahar, El Mehdi Amhoud,
• Abstract summary: We deploy flying LoRa gateways mounted on unmanned aerial vehicles (UAVs) to collect data from LoRa end devices and transmit it to a central server.<n>Our primary objective is to maximize the global system energy efficiency of wireless LoRa networks by joint optimization of transmission power, spreading factor, bandwidth, and user association.
• Score: 8.65375886947338
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As next-generation Internet of Things (NG-IoT) networks continue to grow, the number of connected devices is rapidly increasing, along with their energy demands. This creates challenges for resource management and sustainability. Energy-efficient communication, particularly for power-limited IoT devices, is therefore a key research focus. In this paper, we deployed flying LoRa gateways mounted on unmanned aerial vehicles (UAVs) to collect data from LoRa end devices and transmit it to a central server. Our primary objective is to maximize the global system energy efficiency of wireless LoRa networks by joint optimization of transmission power, spreading factor, bandwidth, and user association. To solve this challenging problem, we model the problem as a partially observable Markov decision process (POMDP), where each flying LoRa GW acts as a learning agent using a cooperative multi-agent reinforcement learning (MARL). Simulation results demonstrate that our proposed method, based on the multi-agent proximal policy optimization (MAPPO) algorithm, significantly improves the global system EE and surpasses the conventional MARL schemes.

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