Nader Mohamed

The rapid expansion of Internet of Things (IoT) devices in healthcare has increased data volumes, creating challenges for the efficiency and latency of real-time monitoring systems. Traditional cloud computing often encounters high latency and network congestion, making it unsuitable for time-sensitive healthcare applications. Although fog computing introduces intermediary nodes to mitigate these issues, existing approaches frequently lack efficient workload distribution, leading to performance bottlenecks. To address these limitations, an Optimised Load Balancing (OLB) algorithm is proposed, designed to allocate workloads effectively across fog nodes and reduce communication and computational delays. The system follows a three-tier architecture: (1) Sensor data collection, where patient-worn sensors transmit vital signs; (2) Fog layer for real-time analysis near base stations; and (3) Cloud storage and user access via mobile devices. Simulations conducted using the iFogSim toolkit demonstrate that OLB achieves a 28% reduction in latency, a 15% improvement in network usage, a 20% reduction in execution time, a 25% decrease in energy consumption, and a 22% reduction in execution cost compared to existing methods, including the Load Balancing Scheme (LBS), Fog Node Placement Algorithm (FNPA), Load-Aware Balancing (LAB) scheme, and Mobile Edge Computing (MEC). By dynamically adjusting workload distribution based on real-time traffic and computational capacity, the proposed fog-based solution provides a responsive, energy-efficient, and cost-effective approach to healthcare data management, surpassing MEC and other state-of-the-art algorithms in adaptability and resource efficiency.

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.

Cloud computing is a virtualized, scalable, ubiquitous, and distributed computing paradigm that provides resources and services dynamically in a subscription based environment. Cloud computing provides services through Cloud Service Providers (CSPs). Cloud computing is mainly used for delivering solutions to a large number of business and scientific applications. Large-scale scientific applications are evaluated through cloud computing in the form of scientific workflows. Scientific workflows are data-intensive applications, and a single scientific workflow may be comprised of thousands of tasks. Deadline constraints, task failures, budget constraints, improper organization and management of tasks can cause inconvenience in executing scientific workflows. Therefore, we proposed a fault-tolerant and data-oriented scientific workflow management and scheduling system (FD-SWMS) in cloud computing. The proposed strategy applies a multi-criteria-based approach to schedule and manage the tasks of scientific workflows. The proposed strategy considers the special characteristics of tasks in scientific workflows, i.e., the scientific workflow tasks are executed simultaneously in parallel, in pipelined, aggregated to form a single task, and distributed to create multiple tasks. The proposed strategy schedules the tasks based on the data-intensiveness, provides a fault tolerant technique through a cluster-based approach, and makes it energy efficient through a load sharing mechanism. In order to find the effectiveness of the proposed strategy, the simulations are carried out on WorkflowSim for Montage and CyberShake workflows. The proposed FD-SWMS strategy performs better as compared with the existing state-of-the-art strategies. The proposed strategy on average reduced execution time by 25%, 17%, 22%, and 16%, minimized the execution cost by 24%, 17%, 21%, and 16%, and decreased the energy consumption by 21%, 17%, 20%, and 16%, as compared with existing QFWMS, EDS-DC, CFD, and BDCWS strategies, respectively for Montage scientific workflow. Similarly, the proposed strategy on average reduced execution time by 48%, 17%, 25%, and 42%, minimized the execution cost by 45%, 11%, 16%, and 38%, and decreased the energy consumption by 27%, 25%, 32%, and 20%, as compared with existing QFWMS, EDS-DC, CFD, and BDCWS strategies, respectively for CyberShake scientific workflow.

Several cities have recently moved towards becoming smart cities for better services and quality of life for residents and visitors, with: optimized resource utilization; increased environmental protection; enhanced infrastructure operations and maintenance; and strong safety and security measures. Smart cities depend on deploying current and new technologies and different optimization methods to enhance services and performance in their different sectors. Some of the technologies assisting smart city applications are the Internet of Things (IoT), fog computing, and cloud computing. Integrating these three to serve one system (we will refer to it as integrated IoT-fog-cloud system (iIFC)) creates an advanced platform to develop and operate various types of smart city applications. This platform will allow applications to use the best features from the IoT devices, fog nodes, and cloud services to deliver best capabilities and performance. Utilizing this powerful platform will provide many opportunities for enhancing and optimizing applications in energy, transportation, healthcare, and other areas. In this paper we survey various applications of iIFCs for smart cities. We identify different common issues associated with utilizing iIFCs for smart city applications. These issues arise due to the characteristics of iIFCs on the one side and the requirements of different smart city applications on the other. In addition, we outline the main requirements to effectively utilize iIFCs for smart city applications. These requirements are related to optimization, networking, and security.

Continued advancements in microprocessors, electronics, and communication technology have led to the design and development of sensing devices with increased functionalities, smaller sizes, larger processing, storage, and communication capabilities, and decreased cost. A large number of these sensor nodes are used in many environmental, infrastructure, commercial, and military monitoring applications. Due to the linearity of a good number of the monitored structures such as oil, gas, and water pipelines, borders, rivers, and roads, the wireless sensor networks (WSNs) that are used to monitor them have a linear topology. This type of WSN is called a linear sensor network (LSN). In this paper, two distributed algorithms for topology discovery in thick LSNs are presented: the linear backbone discovery algorithm (LBD) and the linear backbone discovery algorithm with x backbone paths (LBDx). Both of them try to construct a linear backbone for efficient routing in LSNs. However, the LBD algorithm has the objective of minimizing the number of messages used during the backbone discovery process. On the other hand, the LBDx algorithm focuses on reducing the number of hops of the data messages transmitted from the nodes to the sink. LBD and LBDx exhibit good properties and efficient performance, which are confirmed by extensive simulations.

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.

Smart cities rely heavily on collecting and using data. Smart systems are implemented and deployed to provide intelligent features that help improve efficiency and quality of life. This creates a huge repository of data representing many aspects of smart city operations. Many data-driven applications can take advantage of this data to further improve the “smartness” of a smart city. At the same time, smart city systems, being very large-scale distributed systems and highly integrated with the physical infrastructure and residents of the city, pose immense security challenges as well. So why don’t we take advantage of this data to improve security measures? In this paper we propose the use of data-driven security approaches to secure smart city systems. To illustrate the significance of this approach we first identify the different challenges for securing smart city systems given the unique characteristics of these systems. Then we discuss the benefits of using data-driven security. Furthermore, we categorize the different types of security applications (features) needed to help capitalize on the data needs and benefits. We also discuss the how these categories of applications can alleviate some of the challenges. In addition, we highlight possible future research directions to incorporate effective data-driven security in smart city systems.

Cyber–Physical Systems (CPS) connect the physical world (systems, environments, and humans) with the cyber world (software, data, etc.) to intelligently enhance the operational environment they serve. CPS are distributed software and hardware components embedded in the physical world and possibly attached to humans. They offer smart features, such as enhancing and optimizing the reliability, quality, safety, health, security, efficiency, operational costs, sustainability, and maintainability of physical systems. CPS are also very vulnerable to security attacks and criminal activities. In addition, they are very complex and have a direct impact on their environment. Therefore, it is hard to detect and investigate security attacks, while such attacks may have a catastrophic impact on the physical world. As a result, CPS must incorporate security measures in addition to suitable and effective forensics capabilities. When the security measures fail and an attack occurs, it becomes imperative to perform thorough forensics analysis. Adding effective forensics tools and capabilities will support the investigations of incidents. This paper defines the field of CPS forensics and its dimensions: Technical, Organizational, and Legal. Then, it reviews examples of current research efforts in the field and the types of tools and methods they propose for CPS forensics. In addition, it discusses the issues and challenges in the field that need to be addressed by researchers and developers of CPS. The paper then uses the review outcomes to discuss future research directions to address challenges and create a more effective, efficient, and safe forensics tools and for CPS. This discussion aims to create a starting point for researchers where they can identify the gaps and challenges and create suitable solutions through their research in CPS forensics.

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.

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.