Next Article in Journal
Application of 2D Non-Graphene Materials and 2D Oxide Nanostructures for Biosensing Technology
Next Article in Special Issue
Leasing-Based Performance Analysis in Energy Harvesting Cognitive Radio Networks
Previous Article in Journal
Quartz-Enhanced Photoacoustic Spectroscopy with Right-Angle Prism
Open AccessBrief Report

Industrial Internet of Things-Based Collaborative Sensing Intelligence: Framework and Research Challenges

1
Institut Mines-Télécom, Télécom SudParis, Evry 91011, France
2
Liverpool John Moores University, Liverpool L3 3AF, UK
3
Guangdong University of Petrochemical Technology, Maoming 525000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Sensors 2016, 16(2), 215; https://doi.org/10.3390/s16020215
Received: 13 December 2015 / Accepted: 3 February 2016 / Published: 6 February 2016
(This article belongs to the Special Issue Intelligent Internet of Things (IoT) Networks)
The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well. View Full-Text
Keywords: big data analytics; collaborative intelligence; industrial sensing intelligence; Internet of Things big data analytics; collaborative intelligence; industrial sensing intelligence; Internet of Things
Show Figures

Figure 1

MDPI and ACS Style

Chen, Y.; Lee, G.M.; Shu, L.; Crespi, N. Industrial Internet of Things-Based Collaborative Sensing Intelligence: Framework and Research Challenges. Sensors 2016, 16, 215.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop