Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review.

The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

An Efficient Mobile-Healthcare Emergency Framework.

Mobile technologies are capable of offering individual level health care services to users. Mobile Healthcare (m-Healthcare) frameworks, which feature smartphone (SP) utilizations of ubiquitous computing made possible by applying wireless Body Sensor Networks (BSNs), have been introduced recently to provide SP clients with health condition monitoring and access to medical attention when necessary. However, in a vulnerable m-Healthcare framework, clients' personal info and sensitive data can easily be poached by intruders or any malicious party, causing serious security problems and confidentiality issues. In 2013, Lu et al. proposed a mobile-Healthcare emergency framework based on privacy-preserving opportunistic computing (SPOC), claiming that their splendid SPOC construction can opportunistically gather SP resources such as computing power and energy to handle computing-intensive Personal Health Information (PHI) with minimal privacy disclosure during an emergency. To balance between the risk of personal health information exposure and the essential PHI processing and transmission, Lu et al. presented a patient-centric privacy ingress control framework based on an attribute-based ingress control mechanism and a Privacy-Preserving Scalar Product Computation (PPSPC) technique. In spite of the ingenious design, however, Lu et al.'s framework still has some security flaws in such aspects as client anonymity and mutual authentication. In this article, we shall offer an improved version of Lu et al.'s framework with the security weaknesses mended and the computation efficiency further boosted. In addition, we shall also present an enhanced mobile-Healthcare emergency framework using Partial Discrete Logarithm (PDL) which does not only achieve flawless mutual authentication as well as client anonymity but also reduce the computation cost.