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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Veröffentlicht in:IEEE transactions on intelligent transportation systems Jg. 23; H. 11; S. 21492 - 21504
Hauptverfasser: Han, Shoufei, Zhu, Kun, Zhou, MengChu, Liu, Xiaojing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
ISSN:1524-9050, 1558-0016
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3180288