Machine-to-machine (M2M) communication is a key enabling technology for the future industrial Internet of Things applications. It plays an important role in the connectivity and integration of computerized machines, such as sensors, actuators, controllers, and robots. The requirements in flexibility, efficiency, and cross-platform compatibility of the intermodule communication between the connected machines raise challenges for the M2M messaging mechanism toward ubiquitous data access and events notification. This investigation determines the challenges facing the M2M communication of industrial systems and presents a data-oriented M2M messaging mechanism based on ZeroMQ for the ubiquitous data access in rich sensing pervasive industrial applications. To prove the feasibility of the proposed solution, the EU funded PickNPack production line with a reference industrial network architecture is presented, and the communication between a microwave sensor device and the quality assessment and sensing module controller of the PickNPack line is illustrated as a case study. The evaluation is carried out through qualitative analysis and experimental studies, and the results demonstrate the feasibility of the proposed messaging mechanism. Due to the flexibility in dealing with hierarchical system architecture and cross-platform heterogeneity of industrial applications, this messaging mechanism deserves extensive investigations and further evaluations.

This article describes the implementation of microwave tomography for industrial process applications. Microwave tomography for industrial process imaging has different requirements from that for medical imaging. In addition to spatial resolution, high temporal resolution or real-time imaging is also important for high-speed processes, flows, or rapid reactions. Depending on the specific application, both quantitative imaging and qualitative imaging may be needed. Qualitative imaging would be sufficient to display distributions, patterns, or shapes, which may be adequate for some applications. Quantitative imaging would, however, be more informative, giving images with quantitative dielectric contrast or permittivity values from which other physical parameters, such as density, moisture content, and phase fraction, may be derived. With the microwave tomography approach described, several example applications in industrial processes are demonstrated, and a number of experimental imaging results are presented.

Microwave sensor technology is widely accepted as a non-destructive and hygienic means for food evaluation and analysis. However, its applications concentrate on in-lab investigations, which are not widely applied for on-line measurement in food industry. Motivated by the rapid progress of microwave technologies and the lack of on-line measurement systems in industry, this paper aims to provide a comprehensive overview of microwave sensors for food measurement, define the technological gap, and suggest the potential solutions. With a brief introduction to the fundamentals, classification and analysis of the traditional methods and technologies are presented, followed by a discussion of calibration and decision-making methods. Based on the analysis of the cutting-edge microwave sensing technologies, the limitations and challenges facing the present studies are identified. Then, focusing on some new emerging technologies including Monolithic Microwave Integrated Circuits, antenna array and System on Chip Ultra-Wide Band pulse-based time domain systems, the feasibility and prospective of potential solutions in this particular area are suggested. In addition, integration of emerging Information and Communication Technologies (ICT) and new design concepts of the sensor system concerning the practical use for smart manufacturing are also illustrated. The potentiality of the suggested new emerging technologies and integration of ICT to satisfy future digitised industry will be inspirational and of interest to researchers of both microwave engineering and food sectors.

The development of the Internet of Things (IoTs) has empowered revolution in almost all walks of life. Although substantial effort has been made to bring IoT into manufacturing, there are still technical challenges to provide solutions of real-time pervasive multisensing, quality evaluation, and boundaryless information sharing, which put people at risk of deteriorated food products and food adulteration. In this article, we present an industrial IoT-based system for food product quality assessment and prediction. This research completes the real-time food quality assessment via radiofrequency identification (RFID) based multisensor fusion for the first time. Also, a novel concept of shelf-life prediction is proposed. The RFID-powered sensors provide a new idea for nondestructive food product and environment sensing. A five-layer architecture, considering sensing, controlling, communication, interfaces, and data analysis, is highlighted. A novel RFID metadata structure is first proposed to achieve multidimensional information traceability and global data sharing. Machine-learning-based multisensor fusion is proposed to provide the accurate quality assessment and prediction. The proposed system is implemented as a smart-shelf system to demonstrate its feasibility and advantages. The system is of great significance in improving food safety, reducing food waste, and providing powerful information support for food manufacturing line and supply chain management.

The Internet-of-Things (IoT) empowered technical revolution in manufacturing industry allows pervasive sensing and ubiquitous data access through the lifecycle of products. Although great efforts have been devoted to bridge the gap between industrial operations and information technologies, it still faces technical challenges to unobtrusively monitor the entire lifecycle of products via a boundary-less information flow for Product Lifecycle Management (PLM) in highly adaptive manufacturing. This investigation presents an IoT sensing and networking framework for seamless data integration and ubiquitous access in smart manufacturing, focusing on product identification, data modeling, inter-phase data integration and ubiquitous data access. The highlights of this investigation are: (1) Radio Frequency Identification (RFID) and virtual Universal Unique Identifier (UUID) dual identifier online item-specific data integration, (2) RFID based online product object localization and unique identification, (3) object-centric manufacturing process modeling for inter-phase data integration, and (4) RFID/QR code encoding method for ubiquitous data sharing between product trading lifecycle phases. Finally, the implementation of presented methods in EU PickNPack food manufacturing production line is reported, and the practice has proved the feasibility and advantages.