Industrial smart product-service system (ISPSS), as an emerging industrial digital servitisation paradigm, has attracted ever-increasing attentions from both industries and academics recently. The prevailing adoption of cutting-edge information and communication technologies, digital technologies and artificial intelligence has enabled the engineering lifecycle management in an ever-smarter manner with context awareness. Nevertheless, little study has provided any systematic process to prescribe its sustainable development throughout the engineering lifecycle, let alone any to consider sustainability in the cyber space other than the physical components. Aiming to fill the gap, this work outlines the definition, key features and social, economic and environmental concerns of the proposed sustainable ISPSS, and further proposes a systematic four-phase cyber-physical development framework by referring to the ISO14001:2015 standards. As a short communication, it is hoped this research can attract more open discussion and in-depth investigation towards sustainable ISPSS development in the near future.

Industry 4.0 encompasses various technologies but centres on highly auto-mated, digitalised manufacturing processes and advanced information communication technology. The digitalisation of manufacturing processes enabled different devices and sub-processes within manufacturing facilities to be interconnected into a large digital platform. The modern SM challenge for complex decision-making corresponds to four main aspects: (1) The ability to realistically model the actual manufacturing systems for information and data gathering; (2) the ability to integrate consistent, reliable and valid manufacturing plant data; (3) the ability to process the gathered data to obtain required information within reasonable computational efforts; and (4) the ability to incorporate feedback systems into the manufacturing process for continuous improvement and to facilitate continuous decision-making procedures over time.

A new revolution called Industry 4.0 (I4.0) is emerging and trending, in which industrial systems comprised of numerous sensors, actuators, and intelligent elements are interfaced and integrated into the smart factories with Internet communication technologies. I4.0 is currently driven by disruptive innovations that promise to provide opportunities for new value creations in all major market sectors. Cybersecurity is a common requirement in any Internet technology, thus it remains a major challenge to adopters of I4.0. This chapter provides a brief overview of a number of key components, principles, and paradigms of I4.0 technologies pertaining to cybersecurity. In addition, this chapter introduces industry-relevant cybersecurity vulnerabilities, risks, threats, and countermeasures with high-profile attack examples (e.g. BlackEnergy, Stuxnet) to help readers to appreciate and understand the state of the art. Finally, the chapter attempts to highlight the open issues and future directions of the system components in the context of cybersecurity for I4.0.

Industries also evolved much since 1700. There have been four industrial revolutions since the 1700s. The first industrial revolution in 1780 was about steam engines, textile industries, and mechanical engineering. The second in 1840 was about steel industries. The third in 1900 was about electricity and automobiles, whereas, the fourth industrial revolution was about the IT industry, and it is generally accepted that the fourth industrial revolution has just begun [1]. As such, the term “Industry 4.0” was pinned by the German government in 2011 [2]. Industry 4.0 or Fourth Industrial Revolution is all about the Internet of Things and services (IoTS), cyber-physical systems (CPSs), and interaction and exchange of data through the Internet or cloud computing. During 1960, one system can perform only one task at a time. Multiple systems needed to run multiple tasks simultaneously [3]. Now moving forward to the present 2020, the single system can perform multiple tasks within a few seconds. Such technology is achieved by scientific advancements like the Internet, web services, Internet of things, and cloud computing. Like the industrial revolutions, there have been several improvements and developments in computing, processing, and accessing the stored data. The evolution of computing is provided in Figure 13.1.

System integration is a process commonly implemented in the fields of engineering and information technology. It involves the combination of various computing systems and software packages in order to create a larger system, and this is what drives Industry 4.0 to work at its optimum. System integration increases the value of a system by creating new functionalities through the combination of subsystems and software applications. The world is currently experiencing a fourth iteration of the Industrial Revolution, Industry 4.0, which merges computers and automation to enhance efficiency in the manufacturing industry and also includes cyber-physical systems, the Internet of Things, and cloud computing. Industry 4.0 takes into account all kinds of technologies and machines, from smartphones and tablets to cars, whitegoods, web-enabled televisions, and more. Also, software development is not left out of his process for the effective and efficient development of software products. Software development and applications are increasingly spreading in all areas of human endeavors. It therefore means that, to meet the needs of the world population, Industry 4.0 principles must be applied.

The Fourth Industrial Revolution or Industry 4.0 encompasses production/manu-facturing-based industries, marrying advanced manufacturing techniques with digital transformation, driven by connected technologies to create intelligent manufacturing systems that not only are interconnected but also have the ability to communicate, analyse, forecast and use this information to drive further intelligent actions. New business models and technologies such as the internet of things (IoT), big data, artificial intelligence (Al) and additive manufacturing are driving the change of current business models and shifting the global economics and market structures. Industry 4.0 involves global economic transformation. Hence, national standardisation activities need to be harmonised with the international level to focus on stipulating the international collaboration and cooperation mechanisms and exchange of information.

Project Dragonfly is a two-in-one industrial wastewater and air toxicity monitoring solution that is environmentally friendly, noninvasive, and cost-effective. The project presents remarkable significance when regulation of industrial emissions becomes crucial.The project utilizes Microsoft Azure platform along with Microsoft's proprietary cloud products and services, and Android mobile application for its software components and database: Lolin D32 Pro, Neffos Y5i, multiple sensors and a quadrotor helicopter (quadcopter) are among its main hardware components. The sensors are packed into two functional units: air monitoring unit (AMU) and water monitoring unit (WMU).

Industry 4.0 has recently become an important topic in the software development context. This standard-based strategy integrates physical systems, the Internet of Things, and the Internet of Services with the aim of extending the capacity of software development process. Although many software development experts have presented the advantages of different software development models and approach, software development refers to an architecture that allows the correct implementation of Industry 4.0 applications using the load-balancing approach model (LBAM 4.0). This study exposes the essential characteristics that allow software to be retrofitted to become Industry 4.0 applications. Specifically, an intelligent software system based on a load-balancing approach was developed and implemented using equal and unequal clustering processing capabilities. To evaluate the performance of LBAM 4.0, implementation was carried out on a cluster with equal and unequal nodes using round-robin algorithm. It was discovered that the performance of the algorithm is quite good when the nodes are of equal capacities, but very poor when the capacities of the nodes are not equal. Load adjusted-load informed algorithm was used to improve the worst case of the round-robin algorithm to prevent the worst situations of using nodes of different capacities of which the results showed remarkable improvement.

The human and machine work interference in industry should be flexible and adaptive. Because of this, several industries started to adopt using AR and VR to train their workers. With this training, they can speed up the work or reconfigure the work, support operators, execute augmented virtuality (AV) training for compiling or constructing parts, administer depository or stockroom effectively, support diagnostics in the assembly, and minimize the risk in the work setting. The key technologies on AV used in the industries are display interaction, tracking positioning and registration, human-computer interaction, object detection and recognition, calibration, model rendering, analysis on 3D space, and collision detection.

Cone pyrolysis liquefaction reactor is the key core device of biomass energy production equipment. The study of the dynamics of the cone is of great significance to the bioenergy conversion technology. Proper control of the movement time of the heat carrier in the cone can prevent it. Biomass superheated carbonisation can increase the ratio of gas and liquid formation. It is a difficult and key technology in the design of the cone pyrolysis liquefaction reactor. The kinetic differential equation of the relative motion of heat carrier particles can be obtained by kinetic analysis and calculation. It can play a key role in the in-depth study of the structural size design of the cone and the optimisation of the structural size parameters. This study conducts a dynamic simulation of the physical simplification model of biomass energy production equipment. The computer simulation software was used to simulate the running process of the simplified cone mechanism model. A reasonable physical prototype that meets the process parameters is designed. In view of the above complex dynamic phenomena, it is necessary to comprehensively analyse various methods of dynamics, and use appropriate numerical analysis and computer-aided design methods to carry out system equipment research and design.