In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the 'freshness' of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay B, e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour of any UAV is no greater than B. Specifically, 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 is that a UAV is able to collect the data of an IoT device if the Euclidean distance between them is no greater than the wireless transmission range of the IoT device. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio 4 4/7 for it so far. For the second variant, we devise the very first constant factor approximation algorithm. We also evaluate the performance of the proposed algorithms via extensive experiment simulations. Experimental results show that the numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms on average.
W. Xu et al., "Minimizing the Deployment Cost of UAVs for Delay-Sensitive Data Collection in IoT Networks," IEEE/ACM Transactions on Networking, vol. 30, no. 2, pp. 812 - 825, Institute of Electrical and Electronics Engineers, Apr 2022.
The definitive version is available at https://doi.org/10.1109/TNET.2021.3123606
Keywords and Phrases
Approximation algorithms; Minimum cycle cover with neighborhoods; Minimum numbers of UAV deployments; Mobile data collection; Multiple UAV scheduling
International Standard Serial Number (ISSN)
Article - Journal
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01 Apr 2022