MSc Thesis Proposal by: Lavanya Nagaraju

Tuesday, May 14, 2024 - 14:30

The School of Computer Science is pleased to present…

Detection of Road Side Unit Misbehaviour in VANETs using Trust model

MSc Thesis Proposal by: Lavanya Nagaraju

Date: Tuesday, 14 May 2024

Time:  2:30 pm

Location: Essex Hall, Room 122


In the ever-evolving field of Vehicular Networks, numerous concerns exist across the various components. Still, the one that is specifically crucial in managing network traffic and data exchange is Road Side Units (RSUs). These devices ensure robust and reliable communication between various components in VANETs. They are also susceptible to misbehaviours that could compromise the entire network's integrity. Given the critical nature of these, it becomes crucial to detect the misbehaviour. Hence, in this research, we have developed a trust-based model for detecting RSU misbehaviour in VANETs. The initial phase of research involves an in-depth literature review to identify the existing trust models and related limitations. Then, the preliminary simulations have provided an understanding of the baseline behaviour of RSUs under normal and compromised conditions. The methodology involves designing a global trust model, in which, locally, the RSU assesses the trustworthiness of other RSUs based on direct and indirect interactions, such as packet management and trust values, while looking at factors like packet drop rate and sends the local decision to a central server which aggregates these trust assessments from individual RSUs to adjust the global trust scores of RSUs dynamically. The effectiveness of this model will be evaluated by carrying out simulations and test scenarios with different percentages of compromised RSUs to assess the model's responsiveness and reliability. This research will provide a trust-based framework capable of accurately detecting RSU misbehaviour. At the same time, it will also contribute to the theoretical understanding of trust dynamics in vehicular networks.
Thesis Committee:
Internal Reader: Dr. Shaoquan Jiang 
External Reader: Dr. Mitra Mirhassani          
Advisor: Dr. Ikjot Saini

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