In the IoT paradigm, many of the entities and objects that surround us will be connected to the network. RFID and sensor network technologies have been raised to meet this new challenge, in which information and communication systems were invisibly embedded in the environment around us [12]. The earliest case of an industrial IoT application was the supply chain and logistics management. RFIDs can be attached to (or embedded in) objects and used to identify materials and goods [13]. By using the IoT technology during the process of product life cycle management (PLM), the entire lifecycle status of products can be tracked by RFIDs [14]. For example, RFID readers can be installed along the production plant to monitor the production process, while the label can be traced throughout the entire supply chain (e.g., packaging, transportation, warehousing, sale, service, maintenance, and disposal). Advanced IoT systems, consisted of RFID-equipped items and smart shelves, where the objects and products can be tracked in real time. This may help to reduce material waste, thus lowering costs and improving profit margins for both retailers and manufacturers [15]. IoT can be used to offer advanced solutions in the automotive industry [16]. Each parameter of the automobile can be monitored by specific sensors, such as tire pressure, motor data, fuel consumption, location, speed, and so on. The sensed data was then reported to the center system for optimization of product design, improvement of service, and energy efficiency. IoT may help to increase the environmental sustainability of our cities and the people’s quality of life [17]. For example, smart parking systems may guide drivers to the nearest available parking slot according to the real-time data of drivers’ location or real-time recommendation of the smart parking systems, hence saving time and fuel, and thus reducing the carbon footprint. Additional applications include smart services for entertainment and tourism [18]. For example, by taking pictures of monuments and other tourist landmarks, the users may obtain pertinent information on the personal smartphone from the recommendation system of the city tourism services center, and be guided to discover the heritage of the city. IoT can be used for monitoring and exchanging information about energy flows in the smart grid [19]. In the application of grid, smart meters, automatic control devices, smart switches, and smart appliances were used to monitor the real-time data of electric consumption. By intelligent data analysis, the grid was able to know in advance the expected demands and to adapt the production and consumption of electricity. Consequently, the peak loads of power consumption can be avoided, the possibility of a power outage can be eliminated, and the promptness of action in case of failure and fault can be enhanced. Other types of applications were integrated IoT with the smart grid to optimize the domestic consumption [20]. For example, the home area network (HAN) allows appliances to interact with smart meters in order to reduce costs while guaranteeing the demanded performance. Emergency management assists the society in preparing for, and coping with manmade or natural disasters such as chemical leaks, floods, fire, earthquakes, tornadoes, and epidemics [21]. IoT offers feasible solutions for monitoring and tackling these emergency scenarios in real time (or near real time). The medical and healthcare sector will be strongly affected by IoT [22]. For instance, body area networks (BANs) which is formed by smart and wearable devices (e.g., watches, shoes, and glasses), allow doctors to conduct the remote patient’s monitoring and remote patient’s diagnosing of the hospital.

A new service-oriented manufacturing paradigm, namely cloud manufacturing (CMfg) has been proposed to transform from production-oriented manufacturing to service-oriented manufacturing, and to construct complete manufacturing enterprise systems (ES) with high intelligence [23]. This new manufacturing paradigm was proposed with combination of advanced computing models and technologies such as CC [24], high performance computing, service-oriented technologies [25], and IoT. To fully implement CMfg, the concept, architecture, core enabling technologies, and characteristics of CMfg were widely studied [26–29]. A cloud-based design and manufacture (CBDM) model composed of a cloud consumer, cloud provider, cloud broker, and cloud carriers was studied [30,31]. Product configurators of CMfg for enterprises were studied by Yip et al. [32] to achieve product customization in order to address individual customers’ requirements. Wang [33] studied and developed an Internet and web-based service-oriented system for machine availability monitoring and process planning toward CMfg. The problem of manufacturing resource and service management were studied by Tao et al. [23,34], including the utility modeling, equilibrium, and collaboration of manufacturing resource service transaction, and resource service scheduling based on utility evaluation, service composition and its optimal-selection algorithms, service composition network in CMfg system or service-oriented manufacturing systems. Lu et al. [35] used ontology within a cloud management engine to manage user-defined clouds. The ontology is used to instantiate companies on which the user executes a customized rule to create a cloud and define users authorized to access this cloud. With the emerging theory of “Industry 4.0,” the integration of cloud technologies and industrial cyber-physical systems (ICPS) was first studied [36]. In this paper, the development and character of ICPS were described. With the support of the cloud, ICPS development will impact value creation, business models, downstream services, and work organization. A cloud-based production planning and control system for discrete manufacturing environments were developed to meet the shift of traditional mass producing industries toward mass customization practices [37]. The proposed approach takes into consideration capacity constraints, lot sizing, and priority control in a “bucket-less” manufacturing environment. Gao et al. [38] review the historical development of prognosis theories and techniques and project their future growth enabled by the emerging cloud infrastructure. Techniques for CC were highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Virtualization was critical for resource sharing and dynamic allocation in CMfg. An effective method for manufacturing resources and capabilities virtualization was proposed by Liu and Li [39]. This method contains manufacturing resources modeling and manufacturing cloud services encapsulation. A manufacturing resource virtual description model was built, which includes both nonfunctional and functional features of manufacturing resources. The model can provide a comprehensive manufacturing resource view and information for various manufacturing applications.

Servitization plays an increasingly important role in modern manufacturing environment. In respect of service-oriented manufacturing paradigm, intangible services and physical products were integrated into one system, which was called product-service system (PSS) to provide a comprehensive solution for customers [40]. The ultimate PSS objective was to increase a company’s competitiveness and profitability [41], and another of the PSS objectives was to reduce the consumption of products through alternative scenarios of product use instead of acquiring it. For example, customers who drive rather infrequently may not need to buy cars but would use a car-sharing system [42]. When defining the PSS, Goedkoop et al. [43] define it as a combination of products and services in a system that provides functionality for consumers and reduces environmental impact. Mont [44] highlights how the PSS offers a product and system of integrated products and services that are intended to reduce the environmental impact through alternative scenarios of product use. Thus, the PSS was a competitive opportunity, which was important for how it was able to alter consumption standards. In other words, this new manufacturing paradigm aims to improve both competitiveness and the pursuit of balance between social, economic, and environmental issues [45]. A RFID-enabled PSS for automotive part and accessory manufacturing alliances were proposed by Huang et al. [46] to alleviate the manufacturing systems of automotive part and accessory manufacturers, and to address the “three high problems,” namely high cost, high risk, and high level of technical skills. To have a competitive PSS, a methodology of PSS engineering design was proposed to support engineering designers during the development process [47]. The context of PSS development and the current methods used to develop such systems were described in this paper. Then, the tools and formalism used in the proposed methodology based on a function-oriented description and an activities-related description were explained. An approach for PSS configuration was proposed to achieve desired benefits and customer satisfaction [48]. In this research, the customer needs were first divided as functional needs and perception needs which were generally expressed by customers in their own words. Based on it, a multiclass support vector machine model was built for configuring a specific PSS that meets customer needs. Since methods to evaluate the feasibility of new businesses vary with the characteristics of businesses, the evaluation methods might need to be modified to reflect unique nature in the design of a new PSS. To address this problem, a new framework was proposed to improve the applicability of evaluation methods of newly designed PSS [49]. Environmental constraints lead to important changes in the innovation strategies of manufacturing firms. The development of PSS in 10 manufacturing firms were studied by Laperche and Picard [50] to investigate the reasons and the forms of PSS development. Their impact on innovation management as well as the prerequisites and limits of their implementation were discussed in detail.

An interface to achieve seamless connections between enterprise resource planning (ERP) and process control systems was developed by Siemens Energy & Automation, Inc. [52]. Panetto and Molina [53] described the challenges, trends, and issues for the enterprise information interoperability. They pointed out that enterprise knowledge sharing, common best practices use, and open source/web-based applications were helping to achieve the concept of integrated enterprise and hence the implementation and interoperability of networked enterprises. Rodriguez et al. [54] presented a novel classification method for eight web service discoverability antipatterns, which was good for ranking more relevant services. Kong et al. [55] modeled the uncertain workload and the vague availability of virtualized server nodes with a fuzzy prediction method. The ISA95 standard [56] was developed with the objective to reduce the cost, risk, and errors associated with implementing interfaces between enterprise and production control systems. The business-to-manufacturing mark-up language (B2MML) [57] standard developed by World Batch Forum (WBF) specifies accepted definitions and data formats for information exchange between different management systems. Anaya et al. [58] described a unified enterprise modeling language (UEML), which aims at supporting integrated use of enterprise information models expressed using different languages. Zhang et al. [59] adopt extensible mark-up language (XML)-based schemas to implement the information integration of the heterogeneous information systems. A comprehensive machine tool resource model was proposed by Vichare et al. [60]. The proposed model considers the majority of standard machine elements including specification of the NC controller. Shi et al. [61] employed XML schema to encapsulate manufacturing resource information and adopted web service description language (WSDL) to model the accessing operations to manufacturing resources. Liu et al. [62] proposed a multigranularity resource virtualization and sharing strategies for bridging the gap between complex manufacturing tasks and underlying resources. The proposed approach considers three factors, including workflow, activity, and resource that significantly influence stepwise decompositions of a complex manufacturing task. Tao et al. [63] investigated the formulation of service composition optimal-selection in CMfg with multiple objectives and constraints. An extended product data model that can specify technical services, taking into account the product design and manufacturing process supported as integrated product life cycle data was proposed to maximize product performance over its life cycle [64]. Being an information modeling language to support the Standard for Exchange of Product Model Data (STEP), EXPRESS has been developed to share and exchange product design, manufacturing, and production data in product life cycle. In order to explicitly represent and handle fuzzy engineering information, an extended EXPRESS-G data model for different kinds of fuzziness modeling was developed [65]. The formal transformation from the fuzzy EXPRESS-G data model to the fuzzy XML model was investigated in this paper. The formal transformation approaches proposed in the paper were demonstrated with engineering application examples. The STEP standard makes it easier to integrate systems that process various product life cycle functions, such as design, engineering, manufacturing, logistics support, and will help to facilitate concurrent engineering. To facilitate the computer-readable exchange of the product bill of materials (BOM) information for product data management (PDM), the STEP ISO 10303-21 was implemented by Shih [66] in order to share the product data information in a manufacturing environment.

To deal with these problems, a real-time manufacturing information sharing and integration framework (RMISIF) is presented as seen in Fig. 2.2. It aims to build up a bridge to share and exchange real-time information between heterogeneous enterprise information management systems and real-time manufacturing data captured by IoT devices. As shown in Fig. 2.2, the bottom shows the real-time manufacturing data database. The top shows the real-time manufacturing data processing, sharing, and exchanging service, and the B2MML standards are adopted to create standard schemas for sharing and exchanging among the heterogeneous enterprise information management systems (shown in the right of the Fig. 2.2).

PSES follows the service-oriented architecture and is represented as a web service which can be easily published, searched, and invoked through Internet. It includes two main modules, namely data processing module and sharing and exchanging module, which are described as follows.