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

: Ulm, G.; Smith, S.; Nilsson, A.; Gustavsson, E.; Jirstrand, M.

Volltext ()

Data science and engineering : DSE 6 (2021), Nr.1, S.102-117
ISSN: 2364-1185 (Print)
ISSN: 2364-1541 (Online)
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer FCC ()

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 On-board/Off-board Distributed Data Analytics (OODIDA), 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.