On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture

Yuanhao, Sun, Shu, Lei, Nurellari, Edmond , Li, Kailiang, Zhang, Yu, Zhou, Zhangbing and Han, Guangjie (2021) On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture. IEEE Systems Journal . ISSN 1932-8184

Full content URL: https://doi.org/ 10.1109/JSYST.2021.3104107

On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture
Authors' Accepted Manuscript
On Enabling Mobile Crowd Sensing for Data.pdf - Whole Document

Item Type:Article
Item Status:Live Archive


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 that are required to be considered. Mobile Crowd Sensing (MCS) embeds three beneficial characteristics: a) cost-effectiveness, b) scalability, and c) mobility and robustness. With the Internet of Things (IoT) becoming a reality, the smart phones are widely becoming available even in remote areas. Hence, both the MCSs characteristics and the plug and play widely available infrastructure provides huge opportunities for the MCS-enabled smart agriculture.opening up several new opportunities at the application level. In this paper, we extensively evaluate the Agriculture Mobile Crowd Sensing
(AMCS) and provide insights for agricultural data collection schemes. In addition, we provide 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 posses limitations in regard to data integrity and quality to be considered as a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns the MCS-enabled agriculture by putting forward six potential applications of AMCS-enabled agriculture. Finally, we propose future research and focus on agricultural characteristics, e.g., seasonality and regionality.

Keywords:Mobile crowd sensing, smart agriculture, data collection, Internet of Things
Subjects:H Engineering > H620 Electrical Engineering
H Engineering > H650 Systems Engineering
H Engineering > H640 Communications Engineering
D Veterinary Sciences, Agriculture and related subjects > D700 Agricultural Sciences
D Veterinary Sciences, Agriculture and related subjects > D400 Agriculture
Divisions:College of Science > School of Engineering
ID Code:44879
Deposited On:10 Jun 2021 09:22

Repository Staff Only: item control page