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On enabling mobile crowd sensing for data collection in smart agriculture: a vision

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journal contribution
posted on 28.10.2021, 08:02 by Yuanhao Sun, Weimin Ding, Lei Shu, Kailiang Li, Eve ZhangEve Zhang, Zhangbing Zhou, Guangjie Han
Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges required to be considered. Mobile crowd sensing (MCS) embeds three beneficial characteristics: 1) cost-effectiveness; 2) scalability; and 3) mobility and robustness. With the Internet of Things becoming a reality, smartphones are widely becoming available even in remote areas. Hence, both the MCS characteristics and the plug-and-play widely available infrastructure provide huge opportunities for MCS-enabled smart agriculture, opening up several new opportunities at the application level. In this article, we extensively evaluate agriculture mobile crowd sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we offer a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low-cost requirements. However, we note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns MCS-enabled agriculture by putting forward seven potential applications of AMCS-enabled agriculture. Finally, we propose general research based on agricultural characteristics and discuss a special case based on the solar insecticidal lamp maintenance problem.

Funding

National Natural Science Foundation of China under Grant 62072248

China Scholarship Council (CSC) under Grant 202006850075

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Systems Journal

Publisher

Institute of Electrical and Electronics Engineers

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2021 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

02/08/2021

Publication date

2021-08-27

Copyright date

2021

ISSN

1932-8184

eISSN

1937-9234

Language

en

Depositor

Dr Eve Zhang. Deposit date: 27 October 2021