Authors:
Fedor Smirnov
1
;
Behnaz Pourmohseni
1
;
Michael Glaß
2
and
Jürgen Teich
1
Affiliations:
1
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen and Germany
;
2
Ulm University, Ulm and Germany
Keyword(s):
DSE, Network Optimization, Automotive Networks.
Related
Ontology
Subjects/Areas/Topics:
Applications and Uses
;
Sensor Networks
;
Sensor, Mesh and Ad Hoc Communications and Networks
;
Telecommunications
;
Vehicular Networks
;
Wireless Information Networks and Systems
Abstract:
The introduction of sophisticated ADAS has given rise to larger and more complex automotive communication
networks whose efficient (in effort) and optimal (in quality) design necessarily depends on automated
network design techniques. Typically, these techniques either (a) optimize communication routes based on
topology-independent constraint systems that encode the inclusion of each network component in the route of
a message or (b) depend on a time- and memory-expensive enumeration of all possible transmission routes
to identify the optimal route. In this paper, we propose a novel approach which combines the advantages of
these two strategies to enable an efficient exploration of the routing search space: First, the given network is
preprocessed to identify so-called proxy areas in which each pair of nodes can be connected by exactly one
route. Contrary to network areas with a variety of different routing possibilities, proxy areas do not offer any
room for optimization. We propose
two approaches—both integrable into existing constraint systems—which
exploit the knowledge gathered on proxy areas to improve the exploration efficiency during the routing optimization
process. Experimental results for two mainstream topologies of automotive networks give evidence
that, compared to state-of-the-art routing optimization approaches, the proposed approaches (a) offer an exploration
speedup of up to x 185, (b) deliver network designs of equal or higher quality, and (c) enable an
automated design of significantly larger automotive systems.
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