Minimizing the Deployment Cost of UAVs for Delay-Sensitive Data Collection in IoT Networks

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...

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Vydané v:	IEEE/ACM transactions on networking Ročník 30; číslo 2; s. 812 - 825
Hlavní autori:	Xu, Wenzheng, Xiao, Tao, Zhang, Junqi, Liang, Weifa, Xu, Zichuan, Liu, Xuxun, Jia, Xiaohua, Das, Sajal K.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation Approximation algorithms Data collection Dispatching Electronic devices Euclidean geometry Mathematical analysis minimum cycle cover with neighborhoods minimum numbers of UAV deployments Mobile data collection Monitoring multiple UAV scheduling Sensors Unmanned aerial vehicles Wireless communication
ISSN:	1063-6692, 1558-2566
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Abstract	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 <inline-formula> <tex-math notation="LaTeX">B </tex-math></inline-formula>, 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 <inline-formula> <tex-math notation="LaTeX">B </tex-math></inline-formula>. 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 <inline-formula> <tex-math notation="LaTeX">4\frac {4}{7} </tex-math></inline-formula> 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.
AbstractList	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 [Formula Omitted], 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 [Formula Omitted]. 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 [Formula Omitted] 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. 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 <inline-formula> <tex-math notation="LaTeX">B </tex-math></inline-formula>, 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 <inline-formula> <tex-math notation="LaTeX">B </tex-math></inline-formula>. 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 <inline-formula> <tex-math notation="LaTeX">4\frac {4}{7} </tex-math></inline-formula> 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.
Author	Liang, Weifa Jia, Xiaohua Zhang, Junqi Xu, Zichuan Das, Sajal K. Xiao, Tao Liu, Xuxun Xu, Wenzheng
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SubjectTerms	Algorithms Approximation Approximation algorithms Data collection Dispatching Electronic devices Euclidean geometry Mathematical analysis minimum cycle cover with neighborhoods minimum numbers of UAV deployments Mobile data collection Monitoring multiple UAV scheduling Sensors Unmanned aerial vehicles Wireless communication
Title	Minimizing the Deployment Cost of UAVs for Delay-Sensitive Data Collection in IoT Networks
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