An Overview of Security Challenges in Vehicular Ad-Hoc Networks

© 2017 IEEE. Vehicular Ad hoc Networks (VANET) is emerging as a promising technology of the Intelligent Transportation systems (ITS) due to its potential benefits for travel planning, notifying road hazards, cautioning of emergency scenarios, alleviating congestion, provisioning parking facilities and environmental predicaments. But, the security threats hinder its wide deployment and acceptability by users. This paper gives an overview of the security threats at the various layers of the VANET communication stack and discuss some of the existing solutions, thus concluding why designing a security framework for VANET needs to consider these threats for overcoming security challenges in VANET

Fixed Cluster Based Cluster Head Selection Algorithm in Vehicular Adhoc Network

The emergence of Vehicular Adhoc Networks (VANETs) is expected support variety of applications for driver assistance, traffic efficiency and road safety. For proper transmission of messages in VANET, one of the proposed solutions is dividing the network into clusters and then selecting a cluster head (CH) in each cluster. This can decrease the communication overhead between road side units (RSUs) and other components of VANETs, because instead of every node communicating with RSU, only CH communicates with RSU and relays relevant messages. In clustering, an important step is the selection of CH. In this thesis, we implemented vehicle to vehicle (V2V), cluster head to road side unit and road side unit to trusted authority authentication for the clustered network. We also presented a heuristic algorithm for selecting a suitable vehicle as the cluster head in a cluster. For the selection of head vehicle, we used weighted fitness values based on three parameters; trust value, position from the cluster boundary and absolute relative average speed. Simulation results indicate that the proposed approach can lead to improvements in terms of QoS metrics like delay, throughput and packet delivery ratio

Privacy-aware Biometric Blockchain based e-Passport System for Automatic Border Control

In the middle of 1990s, World Wide Web technology initially steps into our life. Now, 30 years after that, widespread internet access and established computing technology bring embodied real life into Metaverse by digital twin. Internet is not only blurring the concept of physical distance, but also blurring the edge between the real and virtual world. Another breakthrough in computing is the blockchain, which shifts the root of trust attached to a system administrator to the computational power of the system. Furthermore, its favourable properties such as immutable time-stamped transaction history and atomic smart contracts trigger the development of decentralized autonomous organizations (DAOs). Combining above two, this thesis presents a privacy-aware biometric Blockchain based e-passport system for automatic border control(ABC), which aims for improving the efficiency of existing ABC system. Specifically, through constructing a border control Metaverse DAO, border control workload can be autonomously self-executed by atomic smart contracts as transaction and then immutably recorded on Blockchain. What is more, to digitize border crossing documentation, biometric Blockchain based e-passport system(BBCVID) is created to generate an immutable real-world identity digital twin in the border control Metaverse DAO through Blockchain and biometric identity authentication. That is to say, by digitizing border crossing documentation and automatizing both biometric identity authentication and border crossing documentation verification, our proposal is able to significantly improve existing border control efficiency. Through system simulation and performance evaluation by Hyperledger Caliper, the proposed system turns out to be able to improve existing border control efficiency by 3.5 times more on average, which is remarkable. What is more, the dynamic digital twin constructed by BBCVID enables computing techniques such as machine learning and big data analysis applicable to real-world entity, which has a huge potential to create more value by constructing smarter ABC systems