TU BRAUNSCHWEIG
| Carl Friedrich Gauß Faculty | Department of Computer Science
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Misbehavior Detection vs. Redundancy Mitigation in Collective Perception

SupervisorKeno Garlichs
Alexander Willecke
ProfessorProf. Dr.-Ing. Lars Wolf
Projectcollective-perception
IBR GroupCM (Prof. Wolf)
TypeMaster Thesis
Statusopen

Introduction

When generating Collective Perception Messages (CPMs) it is important to keep a look on the channel load caused by their transmission. Therefore after applying some general filtering using generation rules [0], there are some recent works proposing to further reduce the number of shared objects by applying so called Redundancy Mitigation Techniques. They are summarized in the current draft of the CP Standard [1]. However, reducing the redundancy also has some downsides because it limits the abilities of Misbehaviour Detection (MBD) approaches leveraging the fact that information about one particular object is available from multiple sources and can therefore be validated by, e.g., a majority voting.

Task

Your tasks in this theses are as following:

  • Implement currently existing redundancy mitigation techniques (might already be done by the time the thesis starts)
  • Evaluate their effectiveness in terms of channel load reduction
  • Implement and evaluate a redundancy based MBD scheme like [2]
  • Re-evaluate the MBD scheme with active Redundancy mitigation schemes
  • Propose a trade-off

Framework

All the evaluations will be done in simulation using the Artery V2X Simulation Framework [3], using the existing implementation of the CPS [4]. In order to complete the task, the student needs basic skills in C++ and knowledge about vehicular networks. However, it is also possible to acquire those skills along the way.

Material




If you have any questions regarding this thesis, please contact me any time!





last changed 2020-01-14, 14:09 by Keno Garlichs
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