Dynamic Resource Discovery and Management for Edge Computing Based on SPF for HADR Operations

The Smart City concept tries to inherit the advantages of Internet-of-Things (IoT) into its realm to function alongside the existing legacy systems. One of the most promising aspects of IoT is Edge Computing, which tries to move the computing, traditionally done via a centralized infrastructure like the cloud to the edge of the network. This allows remote deployment of IoT assets closer to the source and application area of information enabling faster response times of action. Smart Cities of future envision using Edge Computing to their advantage for remote and distributed computing. Sieve, Process and Forward (SPF) is an Edge Computing solution for dynamic IoT applications for Smart City scenarios. The military is looking forward to use, as well as develop the SPF platform for its Edge Computing requirements. But currently, the SPF platform does not have the mechanism for remote discovery of edge resources and their management to leverage its potential completely. This paper tries to propose a resource discovery and management architecture and methodology for SPF to support future Human Assistance and Disaster Recovery (HADR) operations in Smart City environments with the vision of enabling interoperability between civilian and military platforms

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein

Emerging Paradigms and Architectures for Industry 4.0 Applications

[Abstract]: The Fourth Industrial Revolution (4IR), called “Industry 4.0” in Europe, “Industrial Internet of Things” in North America, or “Made in China 2025” in China, blurs the boundaries between the physical, digital and biological worlds, paving the way to the continuous improvement of manufacturing processes. The 4IR-enabling technologies such as Industrial Cyber–Physical Systems (ICPS), Industrial Internet of Things (IIoT) technologies, novel computing paradigms (e.g., fog, mist, and edge computing), Distributed Ledger Technologies (DLTs) (e.g., blockchain), digital twins, and augmented/mixed reality technologies, enable novel cyber-secure, resilient, collaborative and human-centric advanced manufacturing systems. This Special Issue aims to report the latest breakthroughs in architectures, paradigms, and applications in the ever-increasing complex ecosystem of smart manufacturing. A total of eleven research papers were published in this Special Issue, approaching several fields of Industry 4.0 paradigm, such as Low-Power Wide-Area Network (LPWAN) technologies, additive manufacturing, energy harvesting, Industrial Internet of Things (IIoT), Cyber–Physical Systems (CPS), Artificial Intelligence (AI) or cybersecurity.Centro de Investigación de Galicia “CITIC”; ED431G 2019/01Xunta de Galicia; ED431C 2020/15Agencia Estatal de Investigación; PID2020-118857RA-I00 and PID2019-104958RB-C42FEDER Galicia 2014–2020 & AEI/FEDER Programs, U

Sustainable Edge Computing: Challenges and Future Directions

An increasing amount of data is being injected into the network from IoT (Internet of Things) applications. Many of these applications, developed to improve society's quality of life, are latency-critical and inject large amounts of data into the network. These requirements of IoT applications trigger the emergence of Edge computing paradigm. Currently, data centers are responsible for a global energy use between 2% and 3%. However, this trend is difficult to maintain, as bringing computing infrastructures closer to the edge of the network comes with its own set of challenges for energy efficiency. In this paper, we propose our approach for the sustainability of future computing infrastructures to provide (i) an energy-efficient and economically viable deployment, (ii) a fault-tolerant automated operation, and (iii) a collaborative resource management to improve resource efficiency. We identify the main limitations of applying Cloud-based approaches close to the data sources and present the research challenges to Edge sustainability arising from these constraints. We propose two-phase immersion cooling, formal modeling, machine learning, and energy-centric federated management as Edge-enabling technologies. We present our early results towards the sustainability of an Edge infrastructure to demonstrate the benefits of our approach for future computing environments and deployments.Comment: 26 pages, 16 figure