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

TL;DR

This paper tackles IoT connectivity gaps in dense and disaster-affected environments by introducing a UAV-based framework for real-time detection and recovery of terrestrial coverage holes. It combines a novel air-to-ground network model, offline/online UAV scheduling, and collision-avoiding multi-UAV formation control (including single-ABS and 4-UAV tetrahedral swarms) to restore connectivity with minimal backhaul overhead. Key contributions include a Matérn hard-core checkpoint mechanism, circle-covering bounds for ABS deployment, and Lyapunov-based formation control ensuring collision-free operation, validated by extensive simulations showing significant gains in coverage and reduced deployment effort. The work has practical implications for rapid network restoration in IoT-enabled 6G contexts, with future directions addressing energy constraints, endurance planning, and backhaul-aware resource management.

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coverage-hole detection approaches, such as minimizing drive tests, are costly, time-consuming, and reliant on outdated radio-environment data, making them unsuitable for real-time applications.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.

Figures (15)

• Figure 1: Illustration of coverage-hole detection and recovery: (a) a small coverage hole is detected by the patrol UAV and recovered by a single ABS with wireless backhaul provided by nearby terrestrial BSs 1 and 2; and (b) a large coverage hole is detected by the patrol UAV and recovered by an ABS swarm with wireless backhaul provided by terrestrial BS 1 and a remote LEO satellite.
• Figure 2: System workflow under study, where blue links indicate control information flow and red links represent communication data flow. Config. 1 corresponds to a single ABS serving a small coverage hole, while Config. 2 involves an ABS swarm serving a large one.
• Figure 3: The trajectory of the patrol UAV for coverage hole detection. The blue/red squares denote the preset checkpoints of the patrol UAV, and the arcs show the path determined by the greedy algorithm based on the nearest-neighbor criterion. The color of each checkpoint indicates its coverage status: blue denotes being within the coverage area, whereas red denotes being outside it.
• Figure 4: An online scheduling policy determined by the detection information of three consecutive checkpoints.
• Figure 5: A sketch of the circle covering problem, where the blue and red circles refer to the coverage area of terrestrial BSs and ABSs, respectively.

Theorems & Definitions (3)

• Remark 1: Choice of checkpoint sequence length
• Theorem 1: Bounds on the Number of BSs Required for Seamless Coverage
• Theorem 2