OODIDA: On-board/Off-board Distributed Real-Time Data Analytics for Connected Vehicles

Gregor Ulm; Simon Smith; Adrian Nilsson; Emil Gustavsson; Mats Jirstrand

doi:10.1007/s41019-021-00152-6

OODIDA: On-board/Off-board Distributed Real-Time Data Analytics for Connected Vehicles

Programming Languages 2021-02-02 v3 Distributed, Parallel, and Cluster Computing

Authors: Gregor Ulm , Simon Smith , Adrian Nilsson , Emil Gustavsson , Mats Jirstrand

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Abstract

A fleet of connected vehicles easily produces many gigabytes of data per hour, making centralized (off-board) data processing impractical. In addition, there is the issue of distributing tasks to on-board units in vehicles and processing them efficiently. Our solution to this problem is OODIDA (On-board/Off-board Distributed Data Analytics), which is a platform that tackles both task distribution to connected vehicles as well as concurrent execution of tasks on arbitrary subsets of edge clients. Its message-passing infrastructure has been implemented in Erlang/OTP, while the end points use a language-independent JSON interface. Computations can be carried out in arbitrary programming languages. The message-passing infrastructure of OODIDA is highly scalable, facilitating the execution of large numbers of concurrent tasks.

Keywords

vehicular networks intelligent transportation system distributed computing

Cite

@article{arxiv.1902.00319,
  title  = {OODIDA: On-board/Off-board Distributed Real-Time Data Analytics for Connected Vehicles},
  author = {Gregor Ulm and Simon Smith and Adrian Nilsson and Emil Gustavsson and Mats Jirstrand},
  journal= {arXiv preprint arXiv:1902.00319},
  year   = {2021}
}

Comments

28 pages, 9 figures, 2 algorithms, 2 code listings, 1 table

OODIDA: On-board/Off-board Distributed Real-Time Data Analytics for Connected Vehicles

Abstract

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