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Time- and Computation-Efficient Data Localization at Vehicular Networks' Edge

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Title: Time- and Computation-Efficient Data Localization at Vehicular Networks' Edge
Authors: Duvignau, Romaric, 1989, Havers, Bastian, 1991, Gulisano, Vincenzo Massimiliano, 1984, Papatriantafilou, Marina, 1966
Source: AutoSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2 Molnbaserade produkter och produktion (FiC) HAREN: Självdistribuerad och anpassningsbar dataströmningsanalys i dimman BADA - On-board Off-board Distributed Data Analytics IEEE Access Time- and Computation-Efficient Data Localization in Vehicular Networks’ Edge - Public code repository. 9:137714-137732
Subject Terms: Connected vehicles, Query processing, Edge computing, Data Analysis
Description: As Vehicular Networks rely increasingly on sensed data to enhance functionality and safety, efficient and distributed data analysis is needed to effectively leverage new technologies in real-world applications. Considering the tens of GBs per hour sensed by modern connected vehicles, traditional analysis, based on global data accumulation, can rapidly exhaust the capacity of the underlying network, becoming increasingly costly, slow, or even infeasible. Employing the edge processing paradigm, which aims at alleviating this drawback by leveraging vehicles' computational power, we are the first to study how to localize, efficiently and distributively, relevant data in a vehicular fleet for analysis applications. This is achieved by appropriate methods to spread requests across the fleet, while efficiently balancing the time needed to identify relevant vehicles, and the computational overhead induced on the Vehicular Network. We evaluate our techniques using two large sets of real-world data in a realistic environment where vehicles join or leave the fleet during the distributed data localization process. As we show, our algorithms are both efficient and configurable, outperforming the baseline algorithms by up to a 40× speedup while reducing computational overhead by up to 3× , while providing good estimates for the fraction of vehicles with relevant data and fairly spreading the workload over the fleet. All code as well as detailed instructions are available at https://github.com/dcs-chalmers/dataloc_vn.
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Access URL: https://research.chalmers.se/publication/526437
https://research.chalmers.se/publication/526686
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  Data: Time- and Computation-Efficient Data Localization at Vehicular Networks' Edge
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  Data: <searchLink fieldCode="AR" term="%22Duvignau%2C+Romaric%22">Duvignau, Romaric</searchLink>, 1989<br /><searchLink fieldCode="AR" term="%22Havers%2C+Bastian%22">Havers, Bastian</searchLink>, 1991<br /><searchLink fieldCode="AR" term="%22Gulisano%2C+Vincenzo+Massimiliano%22">Gulisano, Vincenzo Massimiliano</searchLink>, 1984<br /><searchLink fieldCode="AR" term="%22Papatriantafilou%2C+Marina%22">Papatriantafilou, Marina</searchLink>, 1966
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  Data: <i>AutoSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2 Molnbaserade produkter och produktion (FiC) HAREN: Självdistribuerad och anpassningsbar dataströmningsanalys i dimman BADA - On-board Off-board Distributed Data Analytics IEEE Access Time- and Computation-Efficient Data Localization in Vehicular Networks’ Edge - Public code repository</i>. 9:137714-137732
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  Data: <searchLink fieldCode="DE" term="%22Connected+vehicles%22">Connected vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Query+processing%22">Query processing</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink>
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  Data: As Vehicular Networks rely increasingly on sensed data to enhance functionality and safety, efficient and distributed data analysis is needed to effectively leverage new technologies in real-world applications. Considering the tens of GBs per hour sensed by modern connected vehicles, traditional analysis, based on global data accumulation, can rapidly exhaust the capacity of the underlying network, becoming increasingly costly, slow, or even infeasible. Employing the edge processing paradigm, which aims at alleviating this drawback by leveraging vehicles' computational power, we are the first to study how to localize, efficiently and distributively, relevant data in a vehicular fleet for analysis applications. This is achieved by appropriate methods to spread requests across the fleet, while efficiently balancing the time needed to identify relevant vehicles, and the computational overhead induced on the Vehicular Network. We evaluate our techniques using two large sets of real-world data in a realistic environment where vehicles join or leave the fleet during the distributed data localization process. As we show, our algorithms are both efficient and configurable, outperforming the baseline algorithms by up to a 40× speedup while reducing computational overhead by up to 3× , while providing good estimates for the fraction of vehicles with relevant data and fairly spreading the workload over the fleet. All code as well as detailed instructions are available at https://github.com/dcs-chalmers/dataloc_vn.
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