Mobile Base Station Optimal Tour in Wide Area IoT Sensor Networks

TL;DR

This paper introduces the MOT problem for optimizing UAV-mounted mobile base station tours in large IoT sensor networks, balancing coverage, energy constraints, and restricted areas, with a practical greedy solution outperforming existing methods.

Contribution

It formally defines the NP-complete MOT problem, models it as a combinatorial optimization, and proposes a polynomial-time greedy heuristic for efficient tour planning in IoT networks.

Findings

The greedy algorithm achieves 39.15% improvement over state-of-the-art methods.

Simulations show low-cost tours with complete coverage and faster execution.

The framework offers both theoretical insights and practical solutions for large-scale IoT deployments.

Abstract

Wide-area IoT sensor networks require efficient data collection mechanisms when sensors are dispersed over large regions with limited communication infrastructure. Unmanned aerial vehicle (UAV)-mounted Mobile Base Stations (MBSs) provide a flexible solution; however, their limited onboard energy and the strict energy budgets of sensors necessitate carefully optimized tour planning. In this paper, we introduce the Mobile Base Station Optimal Tour (MOT) problem, which seeks a minimum-cost, non-revisiting tour over a subset of candidate stops such that the union of their coverage regions ensures complete sensor data collection under a global sensor energy constraint. The tour also avoids restricted areas. We formally model the MOT problem as a combinatorial optimization problem, which is NP-complete. Owing to its computational intractability, we develop a polynomial-time greedy heuristic that jointly considers travel cost and incremental coverage gain while avoiding restricted areas. Using simulations, we obtain tours with low cost, complete sensor coverage, and faster execution. Our proposed greedy algorithm outperforms state-of-the-art approaches in terms of a performance indicator defined as the product of tour length and algorithm execution time, achieving an improvement of 39.15%. The proposed framework provides both theoretical insight into the structural complexity of MBS-assisted data collection and a practical algorithmic solution for large-scale IoT deployments.