Unmanned aerial vehicle (UAV)-enabled communication has emerged as an appealing technology for efficient data collection in wireless sensor networks (WSNs). This paper considers a scenario where multiple UAVs collect data from a group of sensor nodes (SNs) on the ground. We study the fundamental tradeoff between the aerial cost, which is defined by the propulsion energy consumption and operation costs of all UAVs, and the ground cost, which is defined as the energy consumption of all SNs. To characterize such a tradeoff, an optimization problem is formulated to minimize the weighted sum of the above two costs, by optimizing the UAV trajectory jointly with wake-up time allocation, as well as the transmit power of all SNs. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory and wake-up time allocation optimization, as well as SN transmit power optimization. We propose an iterative algorithm to solve the two sub-problems by leveraging successive convex approximation and alternating optimization techniques. In addition, a new approach is proposed to design the UAV initial trajectory with multiple travelling salesman problem (MTSP) technique. Simulations are conducted to corroborate our study and show the flexible tradeoff achieved by the proposed design for cost balance between UAVs and SNs.

中文翻译：

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无线传感器网络中多无人机启用数据收集的空地成本权衡

支持无人机 (UAV) 的通信已成为无线传感器网络 (WSN) 中有效数据收集的一项有吸引力的技术。本文考虑了多架无人机从地面上的一组传感器节点 (SN) 收集数据的场景。我们研究了空中成本（由所有无人机的推进能量消耗和运营成本定义）与地面成本（定义为所有 SN 的能量消耗）之间的基本权衡。为了表征这种权衡，通过优化 UAV 轨迹与唤醒时间分配以及所有 SN 的发射功率，优化问题被制定以最小化上述两个成本的加权总和。由于公式化的问题是非凸的，一般很难最优解。为了解决这个问题，我们将其解耦为两个子问题：无人机轨迹和唤醒时间分配优化，以及SN发射功率优化。我们提出了一种迭代算法，通过利用连续凸逼近和交替优化技术来解决两个子问题。此外，提出了一种利用多旅行商问题（MTSP）技术设计无人机初始轨迹的新方法。进行了模拟以证实我们的研究，并展示了通过提议的 UAV 和 SN 之间的成本平衡设计实现的灵活权衡。此外，提出了一种利用多旅行商问题（MTSP）技术设计无人机初始轨迹的新方法。进行了模拟以证实我们的研究，并展示了通过提议的 UAV 和 SN 之间的成本平衡设计实现的灵活权衡。此外，提出了一种利用多旅行商问题（MTSP）技术设计无人机初始轨迹的新方法。进行了模拟以证实我们的研究，并展示了通过提议的 UAV 和 SN 之间的成本平衡设计实现的灵活权衡。