DLSense: Distributed Learning-Based Smart Virtual Sensing for Precision Agriculture


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.


Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research


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).”

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


File Type





© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

15 Aug 2021