Joint Deployment Optimization and Flight Trajectory Planning for UAV Assisted IoT Data Collection: A Bilevel Optimization Approach

This work investigates an unmanned aerial vehicle (UAV) assisted IoT system, where a UAV flies to each foothold to collect data from IoT devices, and then return to its start point. For such a system, we aim to minimize the energy consumption by jointly optimizing the deployment and flight trajector...

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Published inIEEE transactions on intelligent transportation systems Vol. 23; no. 11; pp. 21492 - 21504
Main Authors Han, Shoufei, Zhu, Kun, Zhou, MengChu, Liu, Xiaojing
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Autonomous aerial vehicles
Bilevel optimization
dandelion algorithm
Data collection
deployment optimization
Electronic devices
Energy consumption
flight trajectory planning
Greedy algorithms
Internet of Things
iterated greedy algorithm
machine learning
Mixed integer
Mutation
Optimization
Trajectory
Trajectory planning
Traveling salesman problem
UAV
Unmanned aerial vehicles
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Abstract This work investigates an unmanned aerial vehicle (UAV) assisted IoT system, where a UAV flies to each foothold to collect data from IoT devices, and then return to its start point. For such a system, we aim to minimize the energy consumption by jointly optimizing the deployment and flight trajectory of UAV. It is a mixed-integer non-convex and NP-hard problem. In order to address it, a bilevel optimization approach is proposed, where an upper-level method aims to optimize the deployment of UAV and a lower-level one aims to plan UAV flight trajectory. Specifically, the former optimizes the number and locations of footholds of UAV. This work proposes an improved dandelion algorithm with a novel encoding strategy, in which each dandelion represents a foothold of UAV and the entire dandelion population is seen as an entire deployment. Then, two mutation strategies are designed to adjust the number and locations of footholds. Based on the footholds of the UAV provided by the former, the latter transforms flight trajectory planning into a traveling salesman problem (TSP). This work proposes an iterated greedy algorithm to solve it efficiently. The effectiveness of the proposed bilevel optimization approach is verified on ten instances, and the experimental results show that it significantly outperforms other benchmark approaches.
AbstractList This work investigates an unmanned aerial vehicle (UAV) assisted IoT system, where a UAV flies to each foothold to collect data from IoT devices, and then return to its start point. For such a system, we aim to minimize the energy consumption by jointly optimizing the deployment and flight trajectory of UAV. It is a mixed-integer non-convex and NP-hard problem. In order to address it, a bilevel optimization approach is proposed, where an upper-level method aims to optimize the deployment of UAV and a lower-level one aims to plan UAV flight trajectory. Specifically, the former optimizes the number and locations of footholds of UAV. This work proposes an improved dandelion algorithm with a novel encoding strategy, in which each dandelion represents a foothold of UAV and the entire dandelion population is seen as an entire deployment. Then, two mutation strategies are designed to adjust the number and locations of footholds. Based on the footholds of the UAV provided by the former, the latter transforms flight trajectory planning into a traveling salesman problem (TSP). This work proposes an iterated greedy algorithm to solve it efficiently. The effectiveness of the proposed bilevel optimization approach is verified on ten instances, and the experimental results show that it significantly outperforms other benchmark approaches.
Author Zhou, MengChu
Han, Shoufei
Liu, Xiaojing
Zhu, Kun
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Cites_doi 10.1109/TVT.2018.2867021
10.1109/TSMC.2016.2560128
10.1109/TCOMM.2019.2900630
10.1109/JIOT.2020.3013647
10.1109/TNSE.2020.3017556
10.1109/ACCESS.2019.2960314
10.1109/TSMC.2019.2907649
10.1080/0952813X.2017.1413142
10.1109/GLOCOM.2017.8254949
10.1109/ACCESS.2016.2529723
10.1109/TWC.2022.3150429
10.1016/j.eswa.2020.113803
10.1109/JAS.2021.1004171
10.1109/TSMC.2020.3042898
10.1109/TCOMM.2020.2982152
10.1109/ACCESS.2019.2890862
10.1109/TETCI.2019.2939373
10.1109/TSMC.2019.2956121
10.1109/ACCESS.2019.2935462
10.1109/JSYST.2018.2820085
10.1109/MWC.010.2000528
10.1186/s13638-018-1313-0
10.1109/TVT.2020.3001403
10.1109/ACCESS.2019.2927846
10.1109/TVT.2018.2816244
10.1109/ACCESS.2019.2945570
10.1109/TCOMM.2020.3044599
10.1109/TCE.2010.5505976
10.1155/2017/4523754
10.1109/LCOMM.2017.2772254
10.1016/j.knosys.2020.106282
10.1016/j.asoc.2016.10.022
10.1109/TCOMM.2020.3025910
10.1109/MWC.001.1900085
10.1007/s00500-020-05376-8
10.1109/TWC.2019.2926279
10.1016/j.neucom.2020.04.078
10.1109/JAS.2020.1003120
10.1186/s13638-019-1490-5
10.1109/LWC.2017.2700840
10.1109/TCC.2020.3001051
10.1109/TVT.2020.2972133
10.1109/TITS.2019.2892377
10.1109/TWC.2020.3007648
10.1145/2908961.2931634
10.1016/j.engappai.2020.103922
10.1109/TWC.2017.2751045
10.1109/TVT.2020.2968343
10.1109/TVT.2020.3018265
10.1109/TITS.2020.2972389
10.1109/JAS.2021.1004284
10.1109/JAS.2020.1003291
10.1007/s10462-018-9648-9
10.1109/TETCI.2019.2899604
10.1109/TII.2020.3004816
10.3390/s19224952
10.1109/TCOMM.2018.2792014
10.1109/TITS.2018.2835145
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References ref57
ref13
ref56
ref12
ref59
ref15
ref58
ref14
ref52
ref55
ref11
ref54
ref10
ref17
ref16
ref19
ref18
ref51
ref50
ref46
ref45
ref48
ref47
ref42
ref41
ref44
ref43
ref49
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref40
ref35
ref34
ref37
ref36
ref31
ref30
ref33
ref32
ref2
ref1
ref39
ref38
al-hilo (ref26) 2020; 22
du (ref24) 2021; 23
ref23
ref25
ref20
ref22
han (ref53) 2020; 8
ref21
ref28
ref27
ref29
ref60
ref61
References_xml – ident: ref35
  doi: 10.1109/TVT.2018.2867021
– ident: ref39
  doi: 10.1109/TSMC.2016.2560128
– ident: ref27
  doi: 10.1109/TCOMM.2019.2900630
– ident: ref21
  doi: 10.1109/JIOT.2020.3013647
– ident: ref32
  doi: 10.1109/TNSE.2020.3017556
– ident: ref14
  doi: 10.1109/ACCESS.2019.2960314
– ident: ref48
  doi: 10.1109/TSMC.2019.2907649
– ident: ref50
  doi: 10.1080/0952813X.2017.1413142
– ident: ref22
  doi: 10.1109/GLOCOM.2017.8254949
– ident: ref31
  doi: 10.1109/ACCESS.2016.2529723
– ident: ref42
  doi: 10.1109/TWC.2022.3150429
– ident: ref45
  doi: 10.1016/j.eswa.2020.113803
– ident: ref10
  doi: 10.1109/JAS.2021.1004171
– ident: ref1
  doi: 10.1109/TSMC.2020.3042898
– ident: ref41
  doi: 10.1109/TCOMM.2020.2982152
– ident: ref36
  doi: 10.1109/ACCESS.2019.2890862
– ident: ref20
  doi: 10.1109/TETCI.2019.2939373
– ident: ref38
  doi: 10.1109/TSMC.2019.2956121
– ident: ref37
  doi: 10.1109/ACCESS.2019.2935462
– volume: 23
  start-page: 1
  year: 2021
  ident: ref24
  article-title: UAV trajectory planning with probabilistic geo-fence via iterative chance-constrained optimization
  publication-title: IEEE Trans Intell Transp Syst
– ident: ref61
  doi: 10.1109/JSYST.2018.2820085
– ident: ref11
  doi: 10.1109/MWC.010.2000528
– ident: ref33
  doi: 10.1186/s13638-018-1313-0
– ident: ref25
  doi: 10.1109/TVT.2020.3001403
– ident: ref47
  doi: 10.1109/ACCESS.2019.2927846
– ident: ref23
  doi: 10.1109/TVT.2018.2816244
– ident: ref56
  doi: 10.1109/ACCESS.2019.2945570
– ident: ref29
  doi: 10.1109/TCOMM.2020.3044599
– ident: ref6
  doi: 10.1109/TCE.2010.5505976
– ident: ref44
  doi: 10.1155/2017/4523754
– ident: ref15
  doi: 10.1109/LCOMM.2017.2772254
– ident: ref46
  doi: 10.1016/j.knosys.2020.106282
– ident: ref52
  doi: 10.1016/j.asoc.2016.10.022
– ident: ref7
  doi: 10.1109/TCOMM.2020.3025910
– ident: ref2
  doi: 10.1109/MWC.001.1900085
– volume: 22
  start-page: 1
  year: 2020
  ident: ref26
  article-title: UAV-assisted content delivery in intelligent transportation systems-joint trajectory planning and cache management
  publication-title: IEEE Trans Intell Transp Syst
– ident: ref55
  doi: 10.1007/s00500-020-05376-8
– ident: ref13
  doi: 10.1109/TWC.2019.2926279
– ident: ref51
  doi: 10.1016/j.neucom.2020.04.078
– ident: ref4
  doi: 10.1109/JAS.2020.1003120
– ident: ref34
  doi: 10.1186/s13638-019-1490-5
– ident: ref16
  doi: 10.1109/LWC.2017.2700840
– ident: ref60
  doi: 10.1109/TCC.2020.3001051
– ident: ref30
  doi: 10.1109/TVT.2020.2972133
– ident: ref58
  doi: 10.1109/TITS.2019.2892377
– ident: ref28
  doi: 10.1109/TWC.2020.3007648
– ident: ref54
  doi: 10.1145/2908961.2931634
– volume: 8
  start-page: 1
  year: 2020
  ident: ref53
  article-title: Information-utilization-method-assisted multimodal multiobjective optimization and application to credit card fraud detection
  publication-title: IEEE Transactions on Computational Social Systems
– ident: ref49
  doi: 10.1016/j.engappai.2020.103922
– ident: ref43
  doi: 10.1109/TWC.2017.2751045
– ident: ref8
  doi: 10.1109/TVT.2020.2968343
– ident: ref17
  doi: 10.1109/TVT.2020.3018265
– ident: ref57
  doi: 10.1109/TITS.2020.2972389
– ident: ref40
  doi: 10.1109/JAS.2021.1004284
– ident: ref12
  doi: 10.1109/JAS.2020.1003291
– ident: ref3
  doi: 10.1007/s10462-018-9648-9
– ident: ref19
  doi: 10.1109/TETCI.2019.2899604
– ident: ref18
  doi: 10.1109/TII.2020.3004816
– ident: ref5
  doi: 10.3390/s19224952
– ident: ref9
  doi: 10.1109/TCOMM.2018.2792014
– ident: ref59
  doi: 10.1109/TITS.2018.2835145
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Snippet This work investigates an unmanned aerial vehicle (UAV) assisted IoT system, where a UAV flies to each foothold to collect data from IoT devices, and then...
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SubjectTerms Autonomous aerial vehicles
Bilevel optimization
dandelion algorithm
Data collection
deployment optimization
Electronic devices
Energy consumption
flight trajectory planning
Greedy algorithms
Internet of Things
iterated greedy algorithm
machine learning
Mixed integer
Mutation
Optimization
Trajectory
Trajectory planning
Traveling salesman problem
UAV
Unmanned aerial vehicles
Title Joint Deployment Optimization and Flight Trajectory Planning for UAV Assisted IoT Data Collection: A Bilevel Optimization Approach
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