OODIDA: On-board/Off-board Distributed Real-Time Data Analytics for Connected Vehicles
arXiv:1902.00319 · doi:10.1007/s41019-021-00152-6
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.
28 pages, 9 figures, 2 algorithms, 2 code listings, 1 table