Intelligent Resource Allocation for Video Analytics in Blockchain-Enabled Internet of Autonomous Vehicles With Edge Computing
Intelligent Resource Allocation for Video Analytics in Blockchain-Enabled Internet of Autonomous Vehicles With Edge Computing
复制标题
具有边缘计算的区块链自动驾驶汽车互联网中视频分析的智能资源分配
DOI：
10.1109/jiot.2020.3026354
复制
发表时间：
2022-08-15
影响因子：
10.6
通讯作者：
Leung, Victor C. M.
中科院分区：
文献类型：
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作者：
Jiang, Xiantao;Yu, F. Richard;Leung, Victor C. M.
智能交通系统（ITS）中的视频监控正处于快速增长阶段，其中视频分析是提高自动驾驶汽车互联网（IoAV）安全性的潜在技术。然而，海量视频数据传输和计算密集型视频分析给车载网络带来了巨大的负担。此外，由于网络连接不稳定，视频数据并不总是可靠，这使得车联网中的数据共享缺乏安全性和可扩展性。在这项工作中，我们首先提出了一个视频分析框架，其中多路访问边缘计算（MEC）和区块链技术集成到IoAV中，以优化区块链系统的交易吞吐量并减少MEC系统的延迟。此外，基于深度强化学习，将联合优化问题建模为马尔可夫决策过程（MDP），并采用异步优势行动者批评家（A3C）算法来解决该问题。仿真结果表明，我们的方法可以快速收敛并显着提高基于 MEC 的区块链 IoAV 的性能。
Video surveillance in intelligent transportation systems (ITSs) 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 a lack of security and scalability in IoAV. In this work, we first propose a video analytics framework, where the multiaccess 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.