Cloud and Edge Computing

This document specifies functional requirements and architecture about the following items for resource interoperability among heterogeneous IoT platforms (e.g., oneM2M, GS1 Oliot, IBM Watson IoT, OCF IoTivity, and FIWARE, etc.) through the conversion of resource identifiers (IDs) and paths (e.g., uniform resource identifier (URI)): Requirements for interoperability of resource IDs in the heterogeneous IoT platforms; Functional architecture for converting IDs and paths of resources on heterogeneous platforms; and, Functional architecture for mapping and managing resource IDs among heterogeneous platforms.

IEC 62541-13:2020 contains both the official IEC International Standard and its Redline version. The Redline version is available in English only and provides you with a quick and easy way to compare all the changes between the official IEC Standard and its previous edition. IEC 62541-13:2020 is part of the overall OPC Unified Architecture specification series and defines the information model associated with Aggregates. This second edition cancels and replaces the first edition of IEC 62541-13, published in 2015. No technical changes but numerous clarifications. Also some corrections to the examples.

In recent years, there a+E139:I139re more extra-large data centres worldwide. Although these kind of data centres are in strong capability of data processing, it is a little bit difficult to meet the need of data centre application from users. The Edge data centre is established to perfectly solve this problem and let users get better experience of data access. With the huge quantity application and establishment of Edge data centres, the specific requirement of data centre infrastructure, especially in Edge data centre, will be stricter in the future. This Recommendation proposes to establish clear requirements on infrastructure including ICT system, powering system, cooling system, monitoring system etc. to get green, safe, reliable, smart, energy-saving Edge data centre.

Cloud gaming requires high real-time performance, so it needs special hardwares to match the needs of real-time game rendering. This recommendation will describe the detailed infrastructure requirements of cloud gaming and provide reference for infrastructure selection of mobile edge computing in different scenarios. Among the factors that restrict the development of cloud gaming, the long end-to-end delay is the most important one. Mobile Edge computing is an effective way to reduce the end-to-end delay. This recommendation will describe the architecture and interaction process of cloud gaming system optimized for edge computing.

This draft Recommendation provides security guidelines for vehicular edge computing. Vehicular edge computing (VEC) is a model that supports the core cloud's capacity for decentralizing the concentration of computing resources in data centers. VEC also provides more localized storage and application services to road users, thereby making it possible to achieve lower latency delays, faster response times providing mobility support, location awareness, high availability, and Quality of Service for streaming real-time applications since the data processing is conducted closer to the vehicle. Vehicular edge computing faces many security challenges and issues since it requires providing faster service response time to end-users. This Recommendation provides security guidelines for vehicular edge computing based on an analysis of the threats and vulnerabilities identified within VEC. Further, it also provides use cases for a security system and relevant security requirements for use in for vehicular edge computing scenarios.

Edge computing is strongly related to 5G and UPF is the data connection point between the MEC system and 5G Network architecture. The sinking of 5G network user plane brings threats such as illegal eavesdropping, which may seriously threaten the data security in edge computing. The edge node has unique features itself and whether in the physical environment or the network, the data security will be an important problem that the edge nodes need to face. Therefore, the recommendation analyses the edge computing data security mainly from these two aspects and provides relative data security challenges and threats and data security guidelines for Edge Computing. This recommendation could help to address the data security issues in Edge Computing implementation

5G EC would play a key role on low latency services and traffic off-load services in 5G era. Several prominent factors would enlarge and complex the security risks to the network layer that supports 5G EC and even bring new security challenges to the network security operation. These factors would be the flexible network architectures of 5G, the variable deployment positions of EC, the various application scenarios, different types of users' private networks and access networks, etc. The boundaries among the telecommunication networks and the private networks would be more ambiguous, and the exposure surface would be expanded. Therefore, the security requirements and measures of the network layer including both of the telecommunication networks and the private networks would be recommended as telecommunication operators enjoying the benefit of EC.

This draft Recommendation analyses the potential deployment scheme and typical application scenarios of edge computing services, specifies the security threats and requirements specific to the edge computing services and thus establishes the security framework for the operator to safeguard its applications.

ISO/PAS 19450:2015 specifies Object-Process Methodology (OPM) with detail sufficient for enabling practitioners to utilise the concepts, semantics, and syntax of Object-Process Methodology as a modelling paradigm and language for producing conceptual models at various extents of detail, and for enabling tool vendors to provide application modelling products to aid those practitioners.

This document provides a collection of representative use cases of Digital Twin applications in a variety of domains.

This document defines a reference architecture of Knowledge Engineering (KE) in Artificial Intelligence (AI). The reference architecture describes KE roles, activities, constructional layers, components and their relationships among themselves and other systems from systemic user and functional views. This document also provides a common KE vocabulary by defining KE terms

The document describes the common concepts, terminologies, characteristics, use cases and technologies (including data management, coordination, processing, network functionality, heterogeneous computing, security, hardware/software optimization) of edge computing for IoT systems applications. This document is also meant to assist in the identification of potential areas for standardization in edge computing for IoT. The document describes several use cases from different domains: Smart elevator, Smart video monitoring, Intelligent transport systems, Process control in smart factory, Virtual power plant, Automated crop monitoring and management system, Smart lightning system.