A Distributed Message Delivery Infrastructure for Connected Vehicle Technology Applications

A complex and vast amount of data will be collected from on-board sensors of operational connected vehicles (CVs), infrastructure data sources such as roadway sensors and traffic signals, mobile data sources such as cell phones, social media sources such as Twitter, and news and weather data services. Unfortunately, these data will create a bottleneck at data centers for processing and retrievals of collected data, and will require the deployment of additional message transfer infrastructure between data producers and consumers to support diverse CV applications. In this paper, the authors present a strategy for creating an efficient and low-latency distributed message delivery system for CV applications using a distributed message delivery platform. This strategy enables large-scale ingestion, curation, and transformation of unstructured data (roadway traffic-related and roadway non-traffic-related data) into labeled and customized topics for a large number of subscribers or consumers, such as CVs, mobile devices, and data centers. The authors evaluate the performance of this strategy by developing a prototype infrastructure using Apache Kafka, an open source message delivery system, and compared its performance with the latency requirements of CV applications. They present experimental results of the message delivery infrastructure on two different distributed computing testbeds at Clemson University: the Holocron cluster and the Palmetto cluster. Experiments were performed to measure the latency of the message delivery system for a variety of testing scenarios. These experiments reveal that measured latencies are less than the U.S. Department of Transportation recommended latency requirements for CV applications, which prove the efficacy of the system for CV related data distribution and management tasks.

• Record URL:
  https://doi.org/10.1109/TITS.2017.2701799
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Du, Yuheng
  • Chowdhury, Mashrur
  • Rahman, Mizanur
  • Dey, Kakan
  • Apon, Amy
  • Luckow, Andre
  • Ngo, Linh Bao
• Publication Date: 2018-3

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 787-801
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 3
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Cloud computing; Connected vehicles; Data communications; Data management; Distributed processing; Messages (Communications); Metadata; System architecture; Telematics
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01664584
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Mar 28 2018 10:53AM