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Minimizing the Number of Deployed UAVs for Delay-bounded Data Collection of IoT Devices

机译:最大限度地减少IOT设备的延迟有限数据收集的已部署的UVS的数量

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In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding the data collection tour of each UAV. To ensure the ‘freshness’ of the collected data, a strict requirement is that the total time spent in the tour of each UAV, which consists of UAV flying time and data collection time, must be no greater than a given maximum data collection delay B, e.g., 20 minutes. In this paper, we consider a problem of using the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour is no greater than B. We study two variants of the problem, one is that a UAV needs to fly to the location of each IoT device to collect its data; the other variant is that a UAV is able to collect the data of the IoT device as long as their Euclidean distance is no greater than a given wireless transmission range. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio $4rac{4}{7}$ so far. For the second variant, we design the first constant factor approximation algorithm. In addition, we evaluate the performance of the proposed algorithms via extensive experiments, and experimental results show that the average numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms.
机译:在本文中,我们研究了无人驾驶飞行器(UAV)的部署,通过查找每个UAV的数据收集巡视来收集IoT设备的数据。为了确保收集的数据的“新鲜度”,严格要求是在每个无人机之旅中花费的总时间,包括UAV飞行时间和数据收集时间,必须不大于给定的最大数据收集延迟b ,例如20分钟。在本文中,我们考虑使用最小无人机数量并找到他们的数据收集之旅的问题,而是受到每次游览中所花费的总时间不大于B的限制。我们研究了问题的两个变体,一个是UAV需要飞到每个IOT设备的位置,以收集其数据;另一个变体是,只要其欧几里德距离不大于给定的无线传输范围,UAV就能够收集物联网设备的数据。对于问题的第一个变体,我们提出了一种新颖的4近似算法,其提高了到目前为止的最佳逼近比率4 FRAC {4} {7} $。对于第二种变体,我们设计第一恒因子近似算法。此外,我们通过广泛的实验评估所提出的算法的性能,实验结果表明,所提出的算法部署的UAV的平均数为11%至19%,而不是现有算法的11%至19%。

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