Paper Info
Title
Intelligent Resource Allocation for Video Analytics in Blockchain-Enabled Internet of Autonomous Vehicles With Edge Computing
Abstract
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.
Year
DOI
Venue
2022
10.1109/JIOT.2020.3026354
IEEE Internet of Things Journal
Keywords
DocType
Volume
Blockchain,deep reinforcement learning (DRL),Internet of Autonomous Vehicles (IoAV),multiaccess edge computing (MEC),video analytics
Journal
9
Issue
ISSN
Citations 
16
2327-4662
5
PageRank 
References 
Authors
0.42
42
4
Name
Order
Citations
PageRank
Xiantao Jiang1508.24
Fei Yu25116335.58
Tian Song3368.93
Victor C. M. Leung49717759.02