DLSense: Distributed Learning-Based Smart Virtual Sensing for Precision Agriculture
Abstract
This work presents the design of an efficient edge-empowered sensor-cloud architecture equipped with a smart virtual sensing scheme for precision agriculture. Traditionally, in agricultural sensor-cloud, sensor nodes send raw sensed data periodically to the cloud, resulting in higher latency and higher energy and bandwidth consumption. The environment-dependent nature of agricultural parameters also limits the serviceability of sensor-cloud in regions with damaged or unemployed sensors. Moreover, the agricultural sensor-cloud suffers from privacy issues due to the sharing of sensitive farming data across third-party service providers. To address these drawbacks, we first propose a modified sensor-cloud architecture using edge devices as the middleware layer for sensor virtualization, thereby reducing service provisioning latency and resource consumption. Next, we propose DLSense, a novel intelligent virtualization scheme to aid in the design of virtual sensors in the absence of working sensor nodes in a region. DLSense utilizes correlation theory and distributed learning in the edge devices to predict sensor data and enables sharing of information of the trained models instead of raw sensed data, thus imparting privacy. Finally, we evaluate the performance of the DLSense scheme through extensive simulations and an experimental case study of an agricultural application. Results demonstrate that our proposed scheme reduces latency and service cost by 81% and 66%, respectively, and increases service availability by 39% compared to the state-of-the-art methods.
Recommended Citation
R. Saha et al., "DLSense: Distributed Learning-Based Smart Virtual Sensing for Precision Agriculture," IEEE Sensors Journal, vol. 21, no. 16, pp. 17556 - 17563, Institute of Electrical and Electronics Engineers (IEEE), Aug 2021.
The definitive version is available at https://doi.org/10.1109/JSEN.2020.3048593
Department(s)
Computer Science
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
Cloud computing; Computer architecture; Correlation; Distributed Learning; Edge intelligence; Intelligent sensors; Sensor Cloud; Sensor Virtualization; Sensors; Temperature measurement; Virtualization; Wireless sensor networks
International Standard Serial Number (ISSN)
1530-437X; 1558-1748
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
15 Aug 2021
Comments
Published online: 31 Dec 2020
This research is supported by the scheme of IMPacting Research, INnovation and Technology (IMPRINT) (Project no. 5682) sponsored by the Ministry of Human Resources Development, Government of India, New Delhi and by the Ministry of Agriculture and Farmers’ Welfare, Government of India, New Delhi (Coordinated by Director, Mahalanobis National Crop Forecast Centre, New Delhi). The work of S. K. Das is supported by the VAJRA Faculty Scheme under the Science and Engineering Research Board (SERB), India and the US National Science Foundation grant SCC-1952045 on “Smart Integrated Farm Network for Rural Agricultural Communities (SIRAC).”