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.
Highlights
Given the rapidly-evolving demands of industrial production/service for safety [1,2], efficiency [3] and environmental friendliness [4], various sensors and wireless devices have been widely deployed to industrial environments [5,6]
Two on-going efforts about developing the framework are introduced and discussed. This collaborative sensing intelligence (CSI) framework aims to achieve the dynamic collaboration between different objects, and such a collaboration is based on massive spatio-temporal data
The intelligence of industrial production/service in the Industrial IoT (IIoT) can be described as: industrial production/service includes a series of complex and dangerous processes, so how to minimize the manual intervention in these processes is an important issue for improving the safety, efficiency and eco-friendliness of production/service
Summary
Given the rapidly-evolving demands of industrial production/service for safety [1,2], efficiency [3] and environmental friendliness [4], various sensors and wireless devices have been widely deployed to industrial environments [5,6]. Analysing based on the massive data that come from different objects and different time points can help to obtain efficient and cost-effective solutions to achieve safe, highly efficient and eco-friendly industrial production/service [10]. In this study, based on the massive spatio-temporal data from different devices and different time points, with developing the potential of big data analytics, we design a collaborative sensing intelligence (CSI) framework. Two on-going efforts about developing the framework are introduced and discussed This CSI framework aims to achieve the dynamic collaboration between different objects, and such a collaboration is based on massive spatio-temporal data. This section displays and describes the key components of CSI framework On this basis, two on-going efforts are introduced and discussed to provide the details about how to achieve CSI in industrial applications.
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