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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]

Watching the live video on a bus/train/tram has become an important pattern for people to enjoy their travel time. Due to the complex environment changes and obstacles caused by the rapid movement, the network state of the user device is often unstable. A large number of interruptions and resolution reductions seriously affect the user's watching experience. Through direct transmission between devices, Device‐to‐Device (D2D) can effectively improve video quality. Accurate decision for helper device selection is the key to optimize video streaming services based on D2D technology. However, the existing methods through D2D rarely consider the states and the intention of the helper devices when selecting cooperative devices, such as the watching status, the state of charge, the state of cache, and the connection quality. This is highly probable to reduce the efficiency of D2D transmission and the cooperation enthusiasm of helper devices. In addition, the existing methods seldom consider the multiple network interfaces in the device. In view of these challenges, we propose a dynamic smart decision method for device selection in D2D‐assisted multi‐path video transmission network (named DS‐DAMP). Specifically, this method takes the current states and the resource constraints into consideration to select the appropriate helper devices for video cooperative transmission, so as to improve the cooperation satisfaction of helper devices and optimize the video quality of the requesting device. Besides, we make full use of multiple network interfaces to realize multi‐path parallel transmission between devices. A large number of experiments have proved the good performance of our method in terms of video quality and network utility. [ABSTRACT FROM AUTHOR]

Generally, a location‐based service (LBS) often contains the location attribute, content attribute, time‐stamp, and range. From the perspective of privacy protection, the location attribute and the content attribute are the key attributes and need to be protected. However, existing privacy protection methods focus excessively on the location attribute and ignore the content attribute contained in the LBS, which discloses the user's private information. In view of this challenge, a content‐aware privacy protection method, called the CPP method that considers the content attribute is proposed. Specifically, the CPP method is based on using k‐anonymity to generate dummy content attributes to protect the private content. As is shown in an experiment constructed on real‐world data, the CPP method can indeed improve the effect of privacy protection. [ABSTRACT FROM AUTHOR]