Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.

Internet of Things in Industries: A Survey

Rapid advances in industrialisation and informatisation methods have spurred tremendous progress in developing the next generation of manufacturing technology. Today, we are on the cusp of the Fourth Industrial Revolution. In 2013, amongst one of 10 ‘Future Projects’ identified by the German government as part of its High-Tech Strategy 2020 Action Plan, the Industry 4.0 project is considered to be a major endeavour for Germany to establish itself as a leader of integrated industry. In 2014, China’s State Council unveiled their ten-year national plan, Made-in-China 2025, which was designed to transform China from the world’s workshop into a world manufacturing power. Made-in-China 2025 is an initiative to comprehensively upgrade China’s industry including the manufacturing sector. In Industry 4.0 and Made-in-China 2025, many applications require a combination of recently emerging new technologies, which is giving rise to the emergence of Industry 4.0. Such technologies originate from different disciplines ...

Industry 4.0: state of the art and future trends

A prevalent and pervasive view of designing is that it can be modeled using variables and decisions made about what values should be taken by these variables. The activity of designing is carried out with the expectation that the designed artifact will operate in the natural world and the social world. These worlds impose constraints on the variables and their values; so, design could be described as a goal-oriented, constrained, decision- making activity. However, design distinguish- es itself from other similarly described activities not only by its domain but also by additional necessary features. Designing involves exploration, exploring what variables might be appropriate. The process of explo- ration involves both goal variables and deci- sion variables. In addition, designing involves learning: Part of the exploration activity is learning about emerging features as a design proceeds. Finally, design activity occurs within two contexts: the context within which the designer operates and the context produced by the developing design itself. The designer’s perception of what the context is affects the implication of the context on the design. The context shifts as the designer’s perceptions change. Design activity can be now characterized as a goal-oriented, con- strained, decision-making, exploration, and learning activity that operates within a con- text that depends on the designer’s percep- tion of the context.

Design Prototypes: A Knowledge Representation Schema for Design

Combining with the emerged technologies such as cloud computing, the Internet of things, service-oriented technologies and high performance computing, a new manufacturing paradigm – cloud manufacturing CMfg – for solving the bottlenecks in the informatisation development and manufacturing applications is introduced. The concept of CMfg, including its architecture, typical characteristics and the key technologies for implementing a CMfg service platform, is discussed. Three core components for constructing a CMfg system, i.e. CMfg resources, manufacturing cloud service and manufacturing cloud are studied, and the constructing method for manufacturing cloud is investigated. Finally, a prototype of CMfg and the existing related works conducted by the authors' group on CMfg are briefly presented.

Cloud manufacturing: a new manufacturing paradigm

Rapid advances in industrial information integration methods have spurred tremendous growth in the use of enterprise systems. Consequently, a variety of techniques have been used for probing enterprise systems. These techniques include business process management, workflow management, Enterprise Application Integration (EAI), Service-Oriented Architecture (SOA), grid computing, and others. Many applications require a combination of these techniques, which is giving rise to the emergence of enterprise systems. Development of the techniques has originated from different disciplines and has the potential to significantly improve the performance of enterprise systems. However, the lack of powerful tools still poses a major hindrance to exploiting the full potential of enterprise systems. In particular, formal methods and systems methods are crucial for modeling complex enterprise systems, which poses unique challenges. In this paper, we briefly survey the state of the art in the area of enterprise systems as they relate to industrial informatics.

Enterprise Systems: State-of-the-Art and Future Trends

Today's manufacturing environments are very dynamic and turbulent. Traditional enterprise information systems (EISs) have mostly been implemented upon hierarchical architectures, which are inflexible to adapt changes and uncertainties promptly. Next-generation EISs must be agile and adaptable to accommodate changes without significant time delays. It is essential for an EIS to obtain real-time data from the distributed and dynamic manufacturing environment for decision making. Wireless sensor networks (WSNs) and radio-frequency identification (RFID) systems provide an excellent infrastructure for data acquisition, distribution, and processing. In this paper, some key challenges related to the integration of WSN and RFID technologies are discussed. A five-layer system architecture has been proposed to achieve synergistic performance. For the integration of WSN and RFID, one of the critical issues is the low efficiency of communication due to redundant data as redundant data increases energy consumption and causes time delay. To address it, an improved data cleaning algorithm has been proposed; its feasibility and effectiveness have been verified via simulation and a comparison with a published algorithm. To illustrate the capacity of the developed architecture and new data cleaning algorithm, their application in relief supplies storage management has been discussed.

Data Cleaning for RFID and WSN Integration

Virtual enterprises (VEs) have become one of main types of organisations in the manufacturing sector through which the consortium companies organise their manufacturing activities. To be competitive, a VE relies on the complementary core competences among members through resource sharing and agile manufacturing capacity. Manufacturing grid (M-Grid) is a platform in which the production resources can be shared. In this article, an M-Grid-based VE operation platform (MGVEOP) is presented as it enables the sharing of production resources among geographically distributed enterprises. The performance management system of the MGVEOP is based on the balanced scorecard and has the capacity of self-learning. The study shows that a MGVEOP can make a semi-automated process possible for a VE, and the proposed MGVEOP is efficient and agile.

A methodology toward manufacturing grid-based virtual enterprise operation platform

Autoregressive integrated moving average (ARIMA) model has been successfully applied as a popular linear model for economic time series forecasting. In addition, during the recent years, artificial neural networks (ANNs) have been used to capture the complex economic relationships with a variety of patterns as they serve as a powerful and flexible computational tool. However, most of these studies have been characterized by mixed results in terms of the effectiveness of the ANNs model compared with the ARIMA model. In this paper, we propose a hybrid model, which is distinctive in integrating the advantages of ARIMA and ANNs in modeling the linear and nonlinear behaviors in the data set. The hybrid model was tested on three sets of actual data, namely, the Wolf's sunspot data, the Canadian lynx data and the IBM stock price data. Our computational experience indicates the effectiveness of the new combinatorial model in obtaining more accurate forecasting as compared to existing models. Copyright © 2013 John Wiley & Sons, Ltd.

An ARIMA‐ANN Hybrid Model for Time Series Forecasting

Over the last few years, the area of electronic government (e-Government) has received increasing prominence and attention; people are interacting with e-Government systems to an ever greater extent. It is therefore important to measure the development of e-Government. Adopting principal component analysis (PCA), this study presents, validates and updates an evaluation model with 5 dimensions based on Socio-Technical model and Stakeholder Theory, which captures the multidimensional and interdependent nature of e-Government system. The validity of the model is empirically investigated using a sample of local e-Government of 18 cities in China, all of which have high Internet penetration and mature ICT use. The five dimensions of the evaluation model include project construction, information security management, special construction, transparency of government affairs and informationized ability. K-means clustering is applied in the subspace created by PCA to evaluate the local e-Government stages of growth of these 18 cities. The findings provide several important implications for e-Government research and practice.

Research on e-Government evaluation model based on the principal component analysis

Enterprise systems consist of numerous integrated applications including manufacturing, logistics, distribution, accounting, marketing, finance, human resources, and others. Enterprise system is intended to enable automation as it offers the availability of real-time data, improved process visibility, and an increased level of automation. In this paper, we propose to use cloud computing technologies to share computing resources and support advanced features such as on demand payments in human resource management (HRM) sub-system, and we focus on the applications in small and medium enterprises (SMEs). The presented work consists of the following four parts. First, we discuss the cloud computing concept extensively in terms of the definitions, the classifications, and main characteristics, as well as its applications. Second, we perform the game analysis on the cloud computing of HRM in SMEs. Third, we propose new infrastructure of cloud computing to implement HRM in SMEs. Fourth, we illustrate the applications of the proposed infrastructure in HRM; in particular, we discuss some cloud-computing-based enterprise systems application systems including human resources management alliance, human resources management consulting services, and human resources shared services for process reengineering. Our innovation is the primary attempt of applying cloud computing technologies to elevate HRM sub-system in enterprise systems for SMEs.

Li Wang

Papers

Data Cleaning for RFID and WSN Integration

A methodology toward manufacturing grid-based virtual enterprise operation platform

An ARIMA‐ANN Hybrid Model for Time Series Forecasting

Research on e-Government evaluation model based on the principal component analysis

Cloud computing in human resource management (HRM) system for small and medium enterprises (SMEs)