Blockchain-based Collaborative Edge Intelligence for Trustworthy and Real-Time Video Surveillance

Mingjin Zhang, Jiannong Cao, Yuvraj Sahni, Qianyi Chen, Shan Jiang, Lei Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

29 Citations (Scopus)

Abstract

Trustworthy and real-time video surveillance aims to analyze the live camera streams in a privacy-preserving manner for the decision-making of various advanced services, such as pedestrian reidentification and traffic monitoring. In recent years, edge computing has been identified as a promising technology for trustworthy and real-time video surveillance because it keeps confidential video data locally and reduces the latency caused by massive data transmission. Generally, a single edge device can hardly afford the computation-intensive video analytics tasks. Most existing solutions incorporate cloud servers to handle the overloaded tasks. However, such an edge-cloud collaboration approach still suffers from unpredictable latency and privacy concerns because the remote cloud is centralized and distant from the cameras. In this work, we designed a blockchain-based collaborative edge intelligence (BCEI) approach for trustworthy and real-time video surveillance. In BCEI, geo-distributed edge devices form a peer-to-peer network to maintain a permissioned blockchain and share data and computation resources to perform computation-intensive video analytics tasks. The video analytics results are written on the blockchain in an immutable manner to guarantee trustworthiness. To reduce task execution time, we formulate and solve a joint stream mapping and task scheduling problem to schedule video streams and machine learning models among edge devices. A pedestrian reidentification prototype is implemented and deployed based on BCEI with the extensive performance evaluation, indicating the superiority of BCEI in latency reduction and system throughput improvement by leveraging collaboration among edge devices.

Original languageEnglish
Pages (from-to)1623-1633
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume19
Issue number2
DOIs
Publication statusPublished - 1 Feb 2023

Keywords

  • Collaborative edge computing
  • edge blockchain
  • edge intelligence
  • trustworthiness
  • video surveillance

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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