Air-to-Ground Communications for Internet of Things: UAV-based Coverage Hole Detection and Recovery

TL;DR

This paper introduces a UAV-based system for real-time detection and recovery of coverage holes in IoT networks, improving efficiency and reducing costs compared to traditional methods.

Contribution

It proposes a novel UAV-assisted framework utilizing Delaunay triangulation and multi-agent collision avoidance for scalable, real-time coverage hole detection and restoration.

Findings

High detection accuracy demonstrated in simulations

Effective UAV swarm deployment for coverage restoration

Significant reduction in operational costs and time

Abstract

Uncrewed aerial vehicles (UAVs) play a pivotal role in ensuring seamless connectivity for Internet of Things (IoT) devices, particularly in scenarios where conventional terrestrial networks are constrained or temporarily unavailable. However, traditional coverage-hole detection approaches, such as minimizing drive tests, are costly, time-consuming, and reliant on outdated radio-environment data, making them unsuitable for real-time applications. To address these limitations, this paper proposes a UAV-assisted framework for real-time detection and recovery of coverage holes in IoT networks. In the proposed scheme, a patrol UAV is first dispatched to identify coverage holes in regions where the operational status of terrestrial base stations (BSs) is uncertain. Once a coverage hole is detected, one or more UAVs acting as aerial BSs are deployed by a satellite or nearby operational BSs to restore connectivity. The UAV swarm is organized based on Delaunay triangulation, enabling scalable deployment and tractable analytical characterization using stochastic geometry. Moreover, a collision-avoidance mechanism grounded in multi-agent system theory ensures safe and coordinated motion among multiple UAVs. Simulation results demonstrate that the proposed framework achieves high efficiency in both coverage-hole detection and on-demand connectivity restoration while significantly reducing operational cost and time.