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.

翻译：大范围物联网传感器网络在传感器分布广阔且通信基础设施有限的场景下，需要高效的数据收集机制。搭载于无人机上的移动基站提供了一种灵活的解决方案；然而，其有限的机载能量以及传感器严格的能量预算，要求对路径规划进行精细优化。本文提出了移动基站最优路径规划问题，该问题旨在寻找一组候选停靠点的最小成本、非重复访问路径，使得这些停靠点覆盖区域的并集能在全局传感器能量约束下确保完整的传感器数据收集，同时路径需避开受限区域。我们将MOT问题形式化建模为一个组合优化问题，并证明其为NP完全问题。鉴于其计算难解性，我们提出了一种多项式时间的贪心启发式算法，该算法在避开受限区域的同时，综合考虑行程成本与增量覆盖增益。通过仿真实验，我们获得了低成本、完整传感器覆盖且执行更快的路径。我们提出的贪心算法在定义为路径长度与算法执行时间乘积的性能指标上，优于现有先进方法，实现了39.15%的性能提升。所提出的框架既为MBS辅助数据收集的结构复杂性提供了理论见解，也为大规模物联网部署提供了实用的算法解决方案。