My paper “OODIDA: On-Board/Off-Board Distributed Real-Time Data Analytics for Connected Vehicles” was recently published in the Springer journal Data Science and Engineering. The article has been made freely available via Open Access. The abstract is below.

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.