Blockchain-Based Privacy-Aware Content Caching in Cognitive Internet of Vehicles

摘要

The Cognitive Internet of Vehicles (CIoV) introduces a cognitive engine in the traditional Internet of Vehicles, which can realize more intelligent functions such as vehicle deployment and resource allocation. Especially in terms of content caching, the cognitive engine can perceive the content requirements of users and match the content providers and content requesters to improve the caching hit rate. However, during this process, users may worry that their privacy data may be leaked. Content requesters need to submit data about points of interest in contents, which is part of their sensitive information. In addition, in terms of rapid speed of vehicles, the connection time is limited, such as vehicle-to-vehicle and vehicle-to-roadside unit (RSU), which leads to limited transaction time during obtaining contents. In order to solve these problems, in this article, we propose a blockchain-based privacy-aware content caching architecture in CIoV. In general, when vehicles need contents, in order to protect the privacy of vehicles, it is no longer necessary to submit a request to an RSU, but through broadcasting contents from an RSU or surrounding vehicles, where vehicles can selectively obtain contents. To improve the cache hit rate, the cognitive engine will perceive content requirements and recommend relevant content requirements to an RSU or content-providing vehicles based on machine learning or deep learning methods. However, this method of content acquisition will connect different vehicles, which brings the untrusted problem. Both content requesters and providers may worry about untrusted users connected with them. To this end, we adopt the blockchain technology to record the completed content transactions, which are written into the block after the consensus mechanism is completed, thus solving the problem of distrust between vehicles. Experiments demonstrate the privacy-aware content caching architecture based on blockchains effectiveness.