{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/76987"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/76987","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Data collection framework for vehicular On-Board-Diagnostic systems","abstract":"Most modern vehicles contain an On-Board-Diagnostic (OBD) system that can collect a wide range of system data from the vehicle. In aggregation, such data could be applied towards solving the problems of accident prevention, vehicular safety, and driving efficiency. In this paper, we describe our design and implementation of a distributed vehicular data collection framework for running applications that aggregate customized OBD data on demand. Our framework is low-capital, low-maintenance, and flexible enough to handle several types of data requests. In our evaluation, the framework achieves within 2% accuracy for data collection at 1-second interval when compared to an externally calculated benchmark. We also simulate data collection under non-ideal conditions and methodically characterize the drift of accuracy as total percentage of packet loss rises for different patterns of data loss; in our experiment, we conclude that for our sampled data, dropped blocks have the greatest impact on accuracy..","abstract_html":"Most modern vehicles contain an On-Board-Diagnostic (OBD) system that can collect a wide range of system data from the vehicle. In aggregation, such data could be applied towards solving the problems of accident prevention, vehicular safety, and driving efficiency. In this paper, we describe our design and implementation of a distributed vehicular data collection framework for running applications that aggregate customized OBD data on demand. Our framework is low-capital, low-maintenance, and flexible enough to handle several types of data requests. In our evaluation, the framework achieves within 2% accuracy for data collection at 1-second interval when compared to an externally calculated benchmark. We also simulate data collection under non-ideal conditions and methodically characterize the drift of accuracy as total percentage of packet loss rises for different patterns of data loss; in our experiment, we conclude that for our sampled data, dropped blocks have the greatest impact on accuracy..","abstract_has_math":false,"creators":["Liu, Chenxia"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Hari Balakrishnan and Lewis Girod."],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-22T22:20:47Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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In aggregation, such data could be applied towards solving the problems of accident prevention, vehicular safety, and driving efficiency. In this paper, we describe our design and implementation of a distributed vehicular data collection framework for running applications that aggregate customized OBD data on demand. Our framework is low-capital, low-maintenance, and flexible enough to handle several types of data requests. In our evaluation, the framework achieves within 2% accuracy for data collection at 1-second interval when compared to an externally calculated benchmark. We also simulate data collection under non-ideal conditions and methodically characterize the drift of accuracy as total percentage of packet loss rises for different patterns of data loss; in our experiment, we conclude that for our sampled data, dropped blocks have the greatest impact on accuracy.."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Data collection framework for vehicular On-Board-Diagnostic systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Hari Balakrishnan and Lewis Girod."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. 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Our framework is low-capital, low-maintenance, and flexible enough to handle several types of data requests. In our evaluation, the framework achieves within 2% accuracy for data collection at 1-second interval when compared to an externally calculated benchmark. We also simulate data collection under non-ideal conditions and methodically characterize the drift of accuracy as total percentage of packet loss rises for different patterns of data loss; in our experiment, we conclude that for our sampled data, dropped blocks have the greatest impact on accuracy.."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/76987"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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