JLR study group problem: Community detection in car electronics
networks
A modern car is a system with complex architecture that can be represented
by a hierarchy of layers of subsystems (typically 9), ECUs (electronic control
units, typically around 100), functions (around 20000). At the user end, this
network links (on the function level) to a list of features, which is basically the
functionality of the car documented in the driver manual. Every feature is
therefore determined by several functions, and the main question of this
project is how to 'optimally' group these functions together in ECUs and
subsystems.
The current approach is to basically represent all functions in a network, write
down an adjacency matrix A with non-negative integer entries. Each function
has a fixed number of outputs and a_{ij} =2 means that two of the outputs of
function i feed into j. One then has to identify community structures for that
network, which is mostly done by trying to identify blocks in the adjacency
matrix (called the N^2 method) using optimization methods that break down
for matrices with more than about 30 functions. The goal of the MSc project is
to identify methods for community detection that are well suited to the
particular context and test them on realistic function networks, which can
contain up to several hundred functions.
This is of major importance for JLR since they plan to remodel their car design
on a quite fundamental level within the next 10 years.
A reference to start with:
S. Treviño III, A. Nyberg, C. I. Del Genio and K. E. Bassler
, J. Stat. Mech. Theory E. (2015) P02003
http://www2.warwick.ac.uk/fac/cross_fac/complexity/people/staff/delgenio/publ
ist/zscore
JLR contact: Alexandros Mouzakitis
Academic leader: Sam Johnson