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

2026-03-20

AI Summary

This work addresses the inefficiency of existing LoRa network designs that treat above-ground and underground wireless sensors in isolation, leading to poor cross-environment connectivity. The paper proposes the first unified heterogeneous LoRa network framework that integrates terrestrial and subterranean end devices with unmanned aerial vehicle (UAV)-mounted gateways. For the first time, the joint operation of these heterogeneous components is modeled as a partially observable stochastic game (POSG), and a multi-agent proximal policy optimization (MAPPO) algorithm is employed to collaboratively optimize spreading factors, transmission power, and three-dimensional UAV deployment. Compared to conventional isolated deployment strategies, the proposed approach achieves substantial improvements in system energy efficiency—enhancing performance by 55.81% in above-ground scenarios and by 198.49% in underground environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Heterogeneous LoRa Networks
• Energy Efficiency
• 3D Placement
• Resource Allocation
• Wireless Underground Sensor Networks

Innovation

• Heterogeneous LoRa Networks
• Multi-Agent Reinforcement Learning
• UAV 3D Placement
• Energy-Efficient Resource Allocation
• Partially Observable Stochastic Game