Hetero-Net: An Energy-Efficient Resource Allocation and 3D Placement in Heterogeneous LoRa Networks via Multi-Agent Optimization

TL;DR

Hetero-Net introduces a unified framework for heterogeneous LoRa networks, optimizing energy efficiency through joint resource allocation and 3D UAV placement using multi-agent reinforcement learning.

Contribution

The paper presents a novel integrated LoRa network model with multi-agent optimization for energy-efficient resource management across diverse environments.

Findings

Achieves 55.81% energy efficiency improvement over isolated WSNs.

Achieves 198.49% energy efficiency improvement over isolated WUSNs.

Demonstrates the effectiveness of multi-agent PPO in dynamic network optimization.

Abstract

The evolution of Internet of Things (IoT) into multi-layered environments has positioned Low-Power Wide Area Networks (LPWANs), particularly Long Range (LoRa), as the backbone for connectivity across both surface and subterranean landscapes. However, existing LoRa-based network designs often treat ground-based wireless sensor networks (WSNs) and wireless underground sensor networks (WUSNs) as separate systems, resulting in inefficient and non-integrated connectivity across diverse environments. To address this, we propose Hetero-Net, a unified heterogeneous LoRa framework that integrates diverse LoRa end devices with multiple unmanned aerial vehicle (UAV)-mounted LoRa gateways. Our objective is to maximize system energy efficiency through the joint optimization of the spreading factor, transmission power, and three-dimensional (3D) placement of the UAVs. To manage the dynamic and partially observable nature of this system, we model the problem as a partially observable stochastic game (POSG) and address it using a multi-agent proximal policy optimization (MAPPO) framework. An ablation study shows that our proposed MAPPO Hetero-Net significantly outperforms traditional, isolated network designs, achieving energy efficiency improvements of 55.81\% and 198.49\% over isolated WSN-only and WUSN-only deployments, respectively.