The scope of the Industrial Internet of Things (IIoT) has stretched beyond manufacturing to include energy, healthcare, transportation, and all that tomorrow’s smart cities will entail. The realm of IIoT includes smart sensors, actuators, programmable logic controllers, distributed control systems (DCS), embedded devices, supervisory control, and data acquisition systems—all produced by manufacturers for different purposes and with different data structures and formats; designed according to different standards and made to follow different protocols. In this sea of incompatibility, how can we flexibly acquire these heterogeneous data, and how can we uniformly structure them to suit thousands of different applications? In this article, we survey the four pillars of information science that enable collaborative data access in an IIoT—standardization, data acquisition, data fusion, and scalable architecture—to provide an up-to-date audit of current research in the field. Here, standardization in IIoT relies on standards and technologies to make things communicative; data acquisition attempts to transparently collect data through plug-and-play architectures, reconfigurable schemes, or hardware expansion; data fusion refers to the techniques and strategies for overcoming heterogeneity in data formats and sources; and scalable architecture provides basic techniques to support heterogeneous requirements. The article also concludes with an overview of the frontier researches and emerging technologies for supporting or challenging data access from the aspects of 5G, machine learning, blockchain, and semantic web.

Cities are becoming smarter because of recent advances in artificial intelligence and the Internet of Things. However, heterogeneous data source in smart cities are continuously producing low-quality data, and ever-growing applications have greater real-time requirements. Therefore, this article proposes a data stream cleaning system (named DSCS) using edge intelligence to utilize the advantages of cloud servers and edge devices. The DSCS in edge nodes consists of a dynamic protocol interpreter, a structure parser, and a cleaning model activator. Meanwhile, a cloud server, which has pools of protocol and structured programs and cleaning models, supports the edge nodes to adapt massive heterogeneous data sources. To validate the proposed data cleaning system, we applied it to two scenarios: monitoring the injection molding machines, and base stations. The DSCS can have a stable processing time when the number of accessed edge devices is increased, as well as a good cleaning effect.