Intelligent Resource Allocation for Video Analytics in Blockchain-Enabled Internet of Autonomous Vehicles with Edge Computing


Victor Leung, Xiantao Jiang, Richard Yu, Tian Song

Date of Publication


Video surveillance in intelligent transportation systems (ITS) is in the rapid growth stage, where video analytics is a potential technology to improve the safety of the Internet of Autonomous Vehicles (IoAV). However, massive video data transmission and computation-intensive video analytics bring an overwhelming burden for vehicular networks. Moreover, owing to the unstable network connection, the video data are not always reliable, which makes data sharing lack of security and scalability in IoAV. In this work, we first propose a video analytics framework, where the multi-access edge computing (MEC) and blockchain technologies are integrated into IoAV to optimize the transaction throughput of the blockchain system as well as reducing the latency of the MEC system. Furthermore, based on deep reinforcement learning, the joint optimization problem is modeled as a Markov decision process (MDP), and the asynchronous advantage actor-critic (A3C) algorithm is adopted to solve this problem. Simulation results demonstrate that our approach can fast converge and significantly improve the performance of blockchain-enabled IoAV with MEC.

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First Nations land acknowledegement

We acknowledge that the UBC Point Grey campus is situated on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm.

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