| Author: | Wang, Shukang |
| Title: | Multiple unmanned aerial vehicles assisted data collection for heterogeneous time-constrained IoT devices |
| Advisors: | Xu, Gangyan (AAE) |
| Degree: | M.Sc. |
| Year: | 2023 |
| Subject: | Drone aircraft -- Automatic control Airplanes -- Piloting -- Mathematics Airplanes -- Piloting -- Planning Internet of things Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Mechanical Engineering |
| Pages: | vii, 85 pages : color illustrations |
| Language: | English |
| Abstract: | Unmanned Aerial Vehicle (UAV) has been increasingly adopted to collect data from widely distributed Internet of Things (IoT) devices, especially in scenarios with limited network coverage or low quality of services. Efficient UAV route planning is a vital part in such UAV-based data collection process, which is recognized to be complex that need to consider data transmission issues, limit of UAVs, timeliness requirement of use cases, etc. In addition, the data value may vary with the collection time and duration, which make the problem even more challenging. Taken such time-dependent data value into consideration, this paper proposes a new hybrid heuristics-based UAV route planning method for IoT data collection. Specifically, the relationships among UAV service time, data value, and time windows are analyzed first, then an integrated route planning model for multiple UAVs is developed that can well capture the feature of time-dependent data value. After that, an innovative Hybrid Tabu Search-Variable Neighborhood Descent (HTS-VND) algorithm is developed, with six effective operators that could further improve the computing efficiency and solution quality. Based in this, a improved HTS-VND is proposed by combining with CPLEX. Finally, extensive experimental case studies are conducted, which demonstrate our proposed method performs better than other methods in terms of both efficiency and quality. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 7478.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.96 MB | Adobe PDF | View/Open |
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