bodrum escortbodrum escort betpas CarTel: A Distributed Mobile Sensor Computing System

CarTel: A Distributed Mobile Sensor Computing System

Bret Hull, Vladimir Bychkovsky, Yang Zhang, Kevin Chen, Michel Goraczko, Allen K. Miu, Eugene Shih, Hari Balakrishnan, Samuel Madden
4th ACM SenSys, Boulder, CO, November 2006

CarTel is a mobile sensor computing system designed to collect, process, deliver, and visualize data from sensors located on mobile units such as automobiles. A CarTel node is a mobile embedded computer coupled to a set of sensors. Each node gathers and processes sensor readings locally before delivering them to a central portal, where the data is stored in a database for further analysis and visualization. In the automotive context, a variety of on-board and external sensors collect data as users drive.

CarTel provides a simple query-oriented programming interface, handles large amounts of heterogeneous data from sensors, and handles intermittent and variable network connectivity. CarTel nodes rely primarily on opportunistic wireless (e.g., Wi-Fi, Bluetooth) connectivity—to the Internet, or to “data mules” such as other CarTel nodes, mobile phone flash memories, or USB keys—to communicate with the portal. CarTel applications run on the portal, using a delay tolerant continuous query processor, ICEDB, to specify how the mobile nodes should summarize, filter, and dynamically prioritize data. The portal and the mobile nodes use a delay tolerant network stack, CafNet, to communicate.

CarTel has been deployed on six cars, running on a small scale in Boston and Seattle for over a year. It has been used to analyze commute times, analyze metropolitan Wi-Fi deployments, and for automotive diagnostics.

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Bibtex Entry:

   author =       "Bret Hull and Vladimir Bychkovsky and Yang Zhang and Kevin Chen and Michel Goraczko and Allen K. Miu and Eugene Shih and Hari Balakrishnan and Samuel Madden",
   title =        "{CarTel: A Distributed Mobile Sensor Computing System}",
   booktitle =    {4th ACM SenSys},
   year =         {2006},
   month =        {November},
   address =      {Boulder, CO}