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Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply‐chain supervision and collaboration, Internet of Vehicles, real‐time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token‐based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man‐in‐the‐middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. [ABSTRACT FROM AUTHOR]

Edge computing has been considered as a leading paradigm to satisfy the low latency demand for some computation-intensive or data-intensive applications, especially for IIoT applications such as automatic line scheduling of the Internet of Vehicles, time-sensitive supply-chain supervision, and smart control of complex industrial processes. In the edge computing environment, app vendors prefer to cache their app data on edge servers to ensure low latency service. However, it is frequently a challenge in practice, because cache spaces on edge servers are limited and expensive. In view of this challenge, a d eep l earning-based e dge c aching o ptimization method, named DLECO, is proposed to reduce the cost during the cache planning process. In this paper, the edge app data caching problem is formulated as a constrained optimization problem. Then, the specific design of DLECO with a deep learning model is shown, which aims to minimize the overall system cost with lower service latency. The performance of DLECO is analyzed theoretically and experimentally with a collection of data from the real world. The experimental results show its superior performance through comparison with three representative methods. • The edge app data caching (EADC) problem is modeled formally, and the problem is proved to be NP-hard. • The deep learning-based edge caching optimization method, named DLECO, is proposed to solve the EADC problem. • DLECO is analyzed theoretically and evaluated against three representative methods through experiments to show its superior performance. [ABSTRACT FROM AUTHOR]

Metadata service of geo-distributed file systems plays an important role in many web-based applications, such as supply-chain management, process mining from log files, intelligent aggregation and collaboration among business processes deployed over Industrial Internet, etc. The edge computing paradigm provides an ideal infrastructure for some value-added services by using geo-distributed file systems. However, web-based applications among the geo-distributed file systems often need an intelligent metadata service mechanism for low-latency data visiting and processing. Unfortunately, it is often a challenge in practice, as the edge computing paradigm often lacks an edge data center that is indispensable for metadata storage and metadata operation. In view of this challenge, an architecture of a virtual edge data center with intelligent metadata service is investigated, in this paper, for taking full advantage of the geo-distributed file systems. Concretely, a prototype of the virtual edge data center is proposed by dynamically gathering the idle storage capability of edge servers into a virtual resource pool. Then, its intelligent metadata service mechanism is investigated The experimental results demonstrate the feasibility of the architecture and show the superior performances with comparison analysis. [ABSTRACT FROM AUTHOR]