title: Industrial Internet of Things-Based Collaborative Sensing Intelligence: Framework and Research Challenges.
abstract: 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.
authors: Institut Mines-Télécom, Télécom SudParis, Evry 91011, France. yuanfang_chen@ieee.org.|Guangdong University of Petrochemical Technology, Maoming 525000, China. yuanfang_chen@ieee.org.
authors: Yuanfang
authors: Y
authors: Chen
authors: Liverpool John Moores University, Liverpool L3 3AF, UK. g.m.lee@ljmu.ac.uk.
authors: Gyu Myoung
authors: GM
authors: Lee
authors: Guangdong University of Petrochemical Technology, Maoming 525000, China. lei.shu@ieee.org.
authors: Lei
authors: L
authors: Shu
authors: Institut Mines-Télécom, Télécom SudParis, Evry 91011, France. noel.crespi@mines-telecom.fr.
authors: Noel
authors: N
authors: Crespi
keywords: Internet of Things; big data analytics; collaborative intelligence; industrial sensing intelligence