Blockchain technologies have recently come to the forefront of the research and industrial communities as they bring potential benefits for many industries. This is due to their practical capabilities in solving many issues currently inhibiting further advances in various industrial domains. Securely recording and sharing transactional data, establishing automated and efficient supply chain processes, and enhancing transparency across the whole value chain are some examples of these issues. Blockchain offers an effective way to tackle these issues using distributed, shared, secure, and permissioned transactional ledgers. The employment of blockchain technologies and the possibility of applying them in different situations enables many industrial applications through increased efficiency and security; enhanced traceability and transparency and reduced costs. In this paper, different industrial application domains where the use of blockchain technologies has been proposed are reviewed. The paper explores the opportunities, benefits, and challenges of incorporating blockchain in different industry applications. Furthermore, the paper attempts to identify the requirements that support the implementation of blockchain for different industry applications. The review reveals that several opportunities are available for utilizing blockchain in various industry sectors; however, there are still some challenges to be addressed to achieve better utilization of this technology.

Health 4.0 establishes a new promising vision for the healthcare industry. It creatively integrates and employ innovative technologies such as the Internet of Health Things (IoHT), medical Cyber-Physical Systems (medical CPS), health cloud, health fog, big data analytics, machine learning, blockchain, and smart algorithms. The goal is to deliver improved, value-added and cost-effective healthcare services to patients and enhance the effectiveness and efficiency or the healthcare industry. Health 4.0 (adapted from the Industry 4.0 principles) changes the healthcare business model to enhance the interactions across the healthcare clients (the patients), stakeholders, infrastructure, and value chain. This effectively will improve the quality, flexibility, productivity, cost-effectiveness, and reliability of healthcare services in addition to increasing patients’ satisfaction. However, building and utilizing healthcare applications that follow the Health 4.0 concept is a non-trivial and complex endeavor. In addition, advanced potential applications based on Health 4.0 capabilities are not yet being investigated. In this paper we define the main objectives of Health 4.0 and discuss advanced potential Health 4.0 applications. To have a clear understanding of these applications, we categorize them in 4 groups based on the primary beneficiary of these applications. Thus we have patient targeted applications, applications supporting healthcare professionals, resource management applications and high-level healthcare systems management applications. In addition, as we studied the different applications, we realized that these is a certain collection of services that these most of them need regardless of their goals or business context. Services supporting data collection and transfer, security and privacy, reliable operations are some examples. As a result we propose creating a service-oriented middleware framework to offers the common services to the applications developers and facilitate the integration of different services to build applications under the Health 4.0 umbrella.

In this paper, we focus on the use of linear sensor networks (LSNs) to monitor underwater pipelines. We propose the use of an autonomous underwater vehicle (AUV) to move along the pipeline and collect data from the sensor nodes (SNs). It then delivers the collected data to the surface sink at the end of the LSN or LSN segment. In turn, the surface sink transmits the collected data to the network control center (NCC) using any of the communication technologies that are available in the area such as WiMAX, cellular, and satellite. The use of the AUV for data collection allows for considerable advantages including flexibility in SN placement, considerable energy savings, and reduced interference, hidden terminal problems, and collisions. We also provide, different AUV movement strategies and analyze the effect of various network design parameters on network performance metrics such as packet delivery ratio and end-to-end delay.

Factories use many manufacturing processes that consume a lot of energy and highly contribute in greenhouse gas emissions. The introduction of the concept of Industrial Internet in USA and Industry 4.0 in Europe offers many opportunities to reduce energy consumption in these factories. Introducing and utilizing smart techniques for the applications pertinent to manufacturing processes within the Industry 4.0 domain can offer many benefits for reducing energy consumption in smart factories. This paper investigates and discusses these opportunities and benefits. The paper also discusses the roles of Industry 4.0 technologies in enabling these opportunities. Consequently, introducing these capabilities will help significantly reduce both production costs and greenhouse gas emissions. The paper then provides a benefit analysis that shows the advantages of such leverage. In addition, the paper offers an enabling architecture and its components that includes cyber-physical system (CPS) manufacturing services layer, fog manufacturing services layer, cloud manufacturing services layer, and blockchain-based service-oriented middleware to support such opportunities.

Healthcare systems are complex systems that need effective and efficient operations, optimizations, management, and control to offer reliable, high-quality, and cost-effective healthcare services. There are different approaches to improve the management of healthcare systems including utilizing the healthcare systems engineering principles. Healthcare systems engineering views a healthcare organization as a system and applies the engineering analysis and design principles to improve different aspects of healthcare services provided in that system. While this approach can provide many advantages for healthcare organizations, there are also many challenges hindering the ability of healthcare systems engineers from effectively accomplishing their mission. The initiation of the digital twin technology formed several potential methods for various industrial sectors to enhance their operations. Accordingly, they can help improve productivity, cost-effectiveness, reliability, quality, and flexibility. This paper studies how digital twins can be utilized for improving healthcare systems engineering processes and outcomes to enhance different aspects of healthcare systems. The paper discusses some of the challenges of healthcare systems engineering and how these challenges can be relaxed by utilizing digital twins. The paper also develops a conceptual framework to utilize digital twins for improving healthcare systems engineering processes and outcomes and discusses the prospects of such utilization on achieving the goals of healthcare systems engineering. In addition, the paper provides some discussions on the impact of this utilization and the future research and development projections of the employment of digital twins for healthcare systems engineering.

Recently, most healthcare organizations focus their attention on reducing the cost of their supply chain management (SCM) by improving the decision making pertaining processes’ efficiencies. The availability of products through healthcare SCM is often a matter of life or death to the patient; therefore, trial and error approaches are not an option in this environment. Simulation and modeling (SM) has been presented as an alternative approach for supply chain managers in healthcare organizations to test solutions and to support decision making processes associated with various SCM problems. This paper presents and analyzes past SM efforts to support decision making in healthcare SCM and identifies the key challenges associated with healthcare SCM modeling. We also present and discuss emerging technologies to meet these challenges.