Performance and efficiency optimization of multi-layer IoT edge architecture

Abstract. Internet of Things (IoT) has become a backbone technology that connects together various devices with diverse capabilities. It is a technology, which enables ubiquitously available digital services for end-users. IoT applications for mission-critical scenarios need strict performance indicators such as of latency, scalability, security and privacy. To fulfil these requirements, IoT also requires support from relevant enabling technologies, such as cloud, edge, virtualization and fifth generation mobile communication (5G) technologies. For Latency-critical applications and services, long routes between the traditional cloud server and end-devices (sensors /actuators) is not a feasible approach for computing at these data centres, although these traditional clouds provide very high computational and storage for current IoT system. MEC model can be used to overcome this challenge, which brings the CC computational capacity within or next on the access network base stations. However, the capacity to perform the most critical processes at the local network layer is often necessary to cope with the access network issues. Therefore, this thesis compares the two existing IoT models such as traditional cloud-IoT model, a MEC-based edge-cloud-IoT model, with proposed local edge-cloud-IoT model with respect to their performance and efficiency, using iFogSim simulator. The results consolidate our research team’s previous findings that utilizing the three-tier edge-IoT architecture, capable of optimally utilizing the computational capacity of each of the three tiers, is an effective measure to reduce energy consumption, improve end-to-end latency and minimize operational costs in latency-critical It applications

Interpretation of creep crack growth data for ½ CMV steel weldments

Abstract½Cr½ Mo¼ V (½ CMV) steel has been used in high temperature power plant piping due to its enhanced weld properties. Creep crack growth testing has been performed on compact tension C(T) specimens of ½CMV (low alloy ferritic) steel at 540◦C on both parent metal specimens as well as fine and coarse grained heat affected zone (HAZ) specimens, where the initial crack is located within the HAZ. The data has been interpreted using the fracture mechanics parameter C* against the crack growth rate. The creep toughness parameter, Kcmat, is also evaluated for the material. It was seen that, for a given C* value, the fine grained HAZ material generally exhibits higher crack growth rates than the post weld heat treated coarse grained HAZ

Performance and efficiency optimization of multi-layer IoT edge architecture

Abstract The recent IoT applications set strict requirements in terms of latency, scalability, security and privacy. The current IoT systems, where computation is done at data centers, provide typically very high computational and storage capacity but long routes between computational capacity and sensors/actuators make them unsuitable for latency-critical applications and services. Mobile Edge Computing (MEC) can address these problems by bringing computational capacity within or next to the base stations of access networks. Furthermore, to cope with access network problems, the capability of providing the most critical processes at the local network layer is also important. Therefore, in this paper, we compare the traditional cloud-IoT model, a MEC-based edge-cloud-IoT model, and a local edge-cloud-IoT model with respect to their performance and efficiency, using iFogSim simulator. The results complement our previous findings that utilizing the three-tier edge-IoT architecture, capable of optimally utilizing the computational capacity of each of the three tiers, is an effective measure to reduce energy consumption, improve end-to-end latency and minimize operational costs in latency-critical IoT applications

Health-BlockEdge:blockchain-edge framework for reliable low-latency digital healthcare applications

Abstract The rapid evolution of technology allows the healthcare sector to adopt intelligent, context-aware, secure, and ubiquitous healthcare services. Together with the global trend of an aging population, it has become highly important to propose value-creating, yet cost-efficient digital solutions for healthcare systems. These solutions should provide effective means of healthcare services in both the hospital and home care scenarios. In this paper, we focused on the latter case, where the goal was to provide easy-to-use, reliable, and secure remote monitoring and aid for elderly persons at their home. We proposed a framework to integrate the capabilities of edge computing and blockchain technology to address some of the key requirements of smart remote healthcare systems, such as long operating times, low cost, resilience to network problems, security, and trust in highly dynamic network conditions. In order to assess the feasibility of our approach, we evaluated the performance of our framework in terms of latency, power consumption, network utilization, and computational load, compared to a scenario where no blockchain was used

Distributed network and service architecture for future digital healthcare

Abstract According to World Health Organization (WHO), the worldwide prevalence of chronic diseases increases fast and new threats, such as Covid-19 pandemic, continue to emerge, while the aging population continues decaying the dependency ratio. These challenges will cause a huge pressure on the efficacy and cost-efficiency of healthcare systems worldwide. Thanks to the emerging technologies, such as novel medical imaging and monitoring instrumentation, and Internet of Medical Things (IoMT), more accurate and versatile patient data than ever is available for medical use. To transform the technology advancements into better outcome and improved efficiency of healthcare, seamless interoperation of the underlying key technologies needs to be ensured. Novel IoT and communication technologies, edge computing and virtualization have a major role in this transformation. In this article, we explore the combined use of these technologies for managing complex tasks of connecting patients, personnel, hospital systems, electronic health records and medical instrumentation. We summarize our joint effort of four recent scientific articles that together demonstrate the potential of the edge-cloud continuum as the base approach for providing efficient and secure distributed e-health and e-welfare services. Finally, we provide an outlook for future research needs

Distributed network and service architecture for future digital healthcare

According to World Health Organization (WHO), the worldwide prevalence of chronic diseases increases fast and new threats, such as Covid-19 pandemic, continue to emerge, while the aging population continues decaying the dependency ratio. These challenges will cause a huge pressure on the efficacy and cost-efficiency of healthcare systems worldwide. Thanks to the emerging technologies, such as novel medical imaging and monitoring instrumentation, and Internet of Medical Things (IoMT), more accurate and versatile patient data than ever is available for medical use. To transform the technology advancements into better outcome and improved efficiency of healthcare, seamless interoperation of the underlying key technologies needs to be ensured. Novel IoT and communication technologies, edge computing and virtualization have a major role in this transformation. In this article, we explore the combined use of these technologies for managing complex tasks of connecting patients, personnel, hospital systems, electronic health records and medical instrumentation. We summarize our joint effort of four recent scientific articles that together demonstrate the potential of the edge-cloud continuum as the base approach for providing efficient and secure distributed e-health and e-welfare services. Finally, we provide an outlook for future research needs

BlockEdge:blockchain-edge framework for industrial IoT networks

Abstract Industry 4.0 encompasses a promise of a new industrial revolution in terms of providing secure, intelligent, autonomous and self-adaptive industrial IoT (IIoT) networks. Key industrial applications and systems will be significantly more complex due to the involvement of the vast number of different devices and diverse nature of various stakeholders and service providers. These complex industrial processes, services and applications also have strict requirements in terms of performance — latency in particular — and resource-efficiency, together with high standards for security and trust. In this context, Blockchain and Edge Computing emerge as prominent technologies to address the mentioned essential requirements and to further strengthen the rise of the new era of digitization. The Edge computing paradigm ensures low latency services for IIoT applications while optimizing the network usage, whereas Blockchain provides a decentralized way for ensuring data integrity, trust and security. In this paper, we propose a “BlockEdge” framework that combines these two enabling technologies to address some of the critical issues faced by the current IIoT networks. We verify the feasibility of our approach by evaluating the performance and resource-efficiency of BlockEdge in terms of latency, power consumption and network usage, through simulations against non-Blockchain solution