Mobile Computing, Applications, and Services. First International ICST Conference, MobiCASE 2009, San Diego, CA, USA, October 26-29, 2009, Revised Selected Papers

Research Article

Intelligent Telemetry for Freight Trains

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  • @INPROCEEDINGS{10.1007/978-3-642-12607-9_6,
        author={Johnathan Reason and Han Chen and Riccardo Crepaldi and Sastry Duri},
        title={Intelligent Telemetry for Freight Trains},
        proceedings={Mobile Computing, Applications, and Services. First International ICST Conference, MobiCASE 2009, San Diego, CA, USA, October 26-29, 2009, Revised Selected Papers},
        proceedings_a={MOBICASE},
        year={2012},
        month={10},
        keywords={freight trains sensor networks on-board telemetry outlier detection},
        doi={10.1007/978-3-642-12607-9_6}
    }
    
  • Johnathan Reason
    Han Chen
    Riccardo Crepaldi
    Sastry Duri
    Year: 2012
    Intelligent Telemetry for Freight Trains
    MOBICASE
    Springer
    DOI: 10.1007/978-3-642-12607-9_6
Johnathan Reason1,*, Han Chen1,*, Riccardo Crepaldi2,*, Sastry Duri1,*
  • 1: IBM T. J. Watson Research Center
  • 2: University of Illinois
*Contact email: reason@us.ibm.com, chenhan@us.ibm.com, rcrepal2@illinois.edu, sastry@us.ibm.com

Abstract

Within the North American freight railroad industry, there is currently an effort to enable more intelligent telemetry for freight trains. By enabling greater visibility of their rolling stock, including locomotives and railroad cars, railroad companies hope to improve their asset utilization, operational safety, and business profitability. Different communication and sensing technologies are being explored and one candidate technology is wireless sensor networks (WSN). In this article, we present Sensor-Enabled Ambient-Intelligent Telemetry for Trains (SEAIT), which is a WSN-based approach to supporting sensing and communications for advanced freight transportation scenarios. As part of a proof-of-technology exploration, SEAIT was designed to address key requirements of industry proposed applications. We introduce several of these applications and highlight the challenges, which include high end-to-end reliability over many hops, low-latency delivery of emergency alerts, and accurate identification of train composition. We present the architecture of SEAIT and evaluate it against these requirements using an experimental deployment.