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Intelligent Telemetry for Freight Trains

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 35)

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.

Keywords

  • freight trains
  • sensor networks
  • on-board telemetry
  • outlier detection

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© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

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Reason, J.M., Chen, H., Crepaldi, R., Duri, S. (2010). Intelligent Telemetry for Freight Trains. In: Phan, T., Montanari, R., Zerfos, P. (eds) Mobile Computing, Applications, and Services. MobiCASE 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12607-9_6

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  • DOI: https://doi.org/10.1007/978-3-642-12607-9_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12606-2

  • Online ISBN: 978-3-642-12607-9

  • eBook Packages: Computer ScienceComputer Science (R0)