SPEED: a deep learning assisted privacy-preserved framework for intelligent transportation systems

Muhammad Usman, Mian Ahmad Jan*, Alireza Jolfaei*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Roadside cameras in an Intelligent Transportation System (ITS) are used for various purposes, e.g., monitoring the speed of vehicles, violations of laws, and detection of suspicious activities in parking lots, streets, and side roads. These cameras generate big multimedia data, and as a result, the ITS faces challenges like data management, redundancy, and privacy breaching in end-to-end communication. To solve these challenges, we propose a framework, called SPEED, based on a multi-level edge computing architecture and machine learning algorithms. In this framework, data captured by end-devices, e.g., smart cameras, is distributed among multiple Level-One Edge Devices (LOEDs) to deal with data management issue and minimize packet drop due to buffer overflowing on end-devices and LOEDs. The data is forwarded from LOEDs to Level-Two Edge Devices (LTEDs) in a compressed sensed format. The LTEDs use an online Least-Squares Support-Vector Machines (LS-SVMs) model to determine distribution characteristics and index values of compressed sensed data to preserve its privacy during transmission between LTEDs and High-Level Edge Devices (HLEDs). The HLEDs estimate the redundancy in forwarded data using a deep learning architecture, i.e., a Convolutional Neural Network (CNN). The CNN is used to detect the presence of moving objects in the forwarded data. If a movement is detected, the data is forwarded to cloud servers for further analysis otherwise discarded. Experimental results show that the use of a multi-level edge computing architecture helps in managing the generated data. The machine learning algorithms help in addressing issues like data redundancy and privacy-preserving in end-to-end communication.

Original languageEnglish
Pages (from-to)4376-4384
Number of pages9
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number7
DOIs
Publication statusPublished - Jul 2021

Keywords

  • ITS
  • data management
  • redundancy
  • privacy
  • LS-SVMs
  • CNN

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