Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT), where a UAV can use its limited service coverage to harvest and disseminate data from IoT devices with low transmission abilities. The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs. To reduce both energy and time costs, a key way is to enhance the transmission performance of IoT and UAVs. In this work, we introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination from multiple IoT clusters to remote base stations (BSs). Except for reducing these costs, another non-ignorable threat lies in the existence of the potential eavesdroppers, whereas the handling of eavesdroppers often increases the energy and time costs, resulting in a conflict with the minimization of the costs. Moreover, the importance of these goals may vary relatively in different applications. Thus, we formulate a multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs. We prove that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem. Thus, we propose a swarm intelligence-based algorithm to find a set of candidate solutions with different trade-offs which can meet various requirements in a low computational complexity. We also show that swarm intelligence methods need to enhance solution initialization, solution update, and algorithm parameter update phases when dealing with mixed-variable optimization and large-scale problems. Simulation results demonstrate the proposed algorithm outperforms state-of-the-art swarm intelligence algorithms.

翻译：无人机网络是辅助物联网的一项有前景的技术，其中无人机可利用其有限的服务覆盖范围，从传输能力较低的物联网设备中采集和分发数据。现有的无人机辅助数据采集与分发方案大多要求无人机在物联网与接入点之间频繁飞行，导致额外的能量与时间成本。为了同时降低能量和时间成本，关键在于提升物联网与无人机的传输性能。在本研究中，我们同时将协作波束成形引入物联网和无人机，以实现从多个物联网簇到远程基站的高能效与省时的数据采集与分发。除了降低成本，另一个不可忽视的威胁在于潜在窃听者的存在，而应对窃听者往往会增加能量与时间成本，这与最小化这些成本的目标相冲突。此外，在不同应用中，这些目标的重要性可能相对有所变化。因此，我们构建了一个多目标优化问题，旨在同时最小化任务完成时间、朝向窃听者的信号强度以及无人机的总能量成本。我们证明所构建的多目标优化问题是一个NP难、混合变量优化及大规模优化问题。因此，我们提出了一种基于群体智能的算法，以低计算复杂度找到一组具有不同权衡的候选解，从而满足各种需求。我们还表明，在处理混合变量优化和大规模问题时，群体智能方法需增强解初始化、解更新及算法参数更新阶段。仿真结果表明，所提算法优于最先进的群体智能算法。