A secure message-passing framework for inter-vehicular communication using blockchain

Due to exponential growth in the daily usage of vehicles, the traffic congestion and roadside accidents are increasing day by day. The communication among vehicles is critical to avoid the emergencies and to address the issue of congestion of vehicles. Internet of vehicles provides the communication...

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Bibliographic Details
Main Authors: Muhammd Awais Hassan, Ume Habiba, Usman Ghani, Muhmmad Shoaib
Format: Article
Language:English
Published: SAGE Publishing 2019-02-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1177/1550147719829677
Description
Summary:Due to exponential growth in the daily usage of vehicles, the traffic congestion and roadside accidents are increasing day by day. The communication among vehicles is critical to avoid the emergencies and to address the issue of congestion of vehicles. Internet of vehicles provides the communication channel between the vehicles, but existing solutions require a centralized communication system to distribute the message and to authenticate the source. This centralized infrastructure is subject to disturb the vehicular communication in case of server breakdown or due to any natural disaster where hardware stops working. Also, the centralized system proves to be costly as the communication of each vehicle necessitates access to the central server resulting in the more resource requirement. A secure distributed system is required to avoid the emergencies and reduce the traffic rate. To address the issues, we proposed a secure distributed message-passing framework that does not require a centralized server, and it rates the credibility of message source using blockchain technology. The messages in the proposed system are forwarded through dedicated short-range communication protocol. To validate the proposed system, we have performed different simulations using SUMO, OMNET, and VEINS. The results demonstrated an increase in the average speed of vehicles that showed a reduced congestion rate. Moreover, our system identified the malicious vehicles with 77.1% accuracy.
ISSN:1550-1477