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Energy-optimal data collection for unmanned aerial vehicle-aided industrial wireless sensor network-based agricultural monitoring system: a clustering compressed sampling approach

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journal contribution
posted on 13.09.2021, 11:24 by Chuan Lin, Guangjie Han, Xingyue Qi, Jiaxin Du, Tiantian Xu, Miguel Martinez-GarciaMiguel Martinez-Garcia
In this article, we propose a hierarchical data collection scheme, toward the realization of unmanned aerial vehicle (UAV)-aided industrial wireless sensor networks. The particular application is that of agricultural monitoring. For that, we propose the use of hybrid compressed sampling through exact and greedy approaches. With the exact approach - to model the energy-optimal formulation - an improved linear programming formulation of the minimum cost flow problem was utilized. The greedy approach is based on a proposed balance factor parameter, consisting of data sparsity, and distance from cluster head to normal nodes. To improve node clustering efficiency, a hierarchical data collection scheme is implemented, by which nodes in different layers are adaptively clustered, and the UAV can be scheduled to perform energy-efficient data collection. Simulation results show that our method can effectively collect the data and plan the path for the UAV at a low energy cost.

Funding

National Key Research and Development Program under Grant 2017YFE0125300

Jiangsu Key Research and Development Program under Grant BE2019648

Shenzhen Science and Technology Innovation Committee under Grant JCYJ20190809145407809

National Natural Science Foundation of China under Grant 62002045

Project of Fujian University of Technology, under Grant GY-Z19066. Paper no. TII-20-3899

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Industrial Informatics

Volume

17

Issue

6

Pages

4411 - 4420

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

18/09/2020

Publication date

2020-09-30

Copyright date

2020

ISSN

1551-3203

eISSN

1941-0050

Language

en

Depositor

Dr Miguel Martinez Garcia. Deposit date: 7 September 2021