吞吐量最大化为无人机启用的数据收集

Junchao Gong, Xiaojun Zhu, Lijie Xu
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引用次数: 2

摘要

在本文中,我们考虑了一个场景,其中无人驾驶飞行器(UAV)从放置在二维(2-D)平面上的一组节点收集数据。无人机沿着给定的路线飞行以收集数据。为防止传输冲突,任何时候只允许有一个节点向无人机传输数据,并且可以向无人机传输数据的节点数量不能超过规定的数量,避免频繁切换。问题是选择节点子集并调度其传输时间以使无人机的吞吐量最大化。在将问题公式化后,我们发现由于实变量和整数变量的混合,问题很难求解。然后,我们提出将问题分解为子问题,并给出一个多项式时间算法来求解每个子问题的最优性。然后通过求解所有子问题得到一个精确的算法。不幸的是,由于子问题的数量呈指数级增长,总体运行时间与输入大小呈指数级增长。然后,我们提出了两个多项式时间次优算法来解决问题,这两个算法都探索多项式个数的子问题。仿真结果表明,次优算法的性能与指数时间精确算法相当,但运行时间要短得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Throughput Maximization for UAV-Enabled Data Collection
In this paper, we consider a scenario where an unmanned aerial vehicle (UAV) collects data from a set of nodes placed on a two-dimensional (2-D) plane. The UAV flies along a given line to collect data. To prevent transmission collisions, only one node is allowed to transmit data to the UAV at any time, and the number of nodes that can transmit data to the UAV cannot exceed a specified number to avoid frequent switches. The problem is to select a subset of nodes and schedule their transmission time to maximize the UAV’s throughput. After formulating the problem, we find that it is difficult to solve due to mixture of real variables and integer variables. We then propose to decompose the problem into subproblems, and give a polynomial time algorithm to solve each subproblem to optimality. We then get an exact algorithm by solving all subproblems. Unfortunately, due to an exponential number of subproblems, the overall running time is exponential with respect to the input size. We then propose two polynomial-time suboptimal algorithms to solve the problem, both of which explore a polynomial number of subproblems. Simulations show that the suboptimal algorithms perform comparably with the exponential-time exact algorithm, while the running time is much smaller.
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