Road Oriented Traffic Information System for Vehicular Ad hoc Networks

SummaryTraffic information of disparate types in Internet of Vehicles (IoV) is the basis for supporting various IoV applications. Since traffic flows vary in real time, it is challenging to carry out efficient and reliable transfer of such information. Moreover, IoV is vulnerable to security threats due to its inherent properties such as dynamically changing topology and high‐speed motion of vehicles. Attacks, once launched successfully, would also disrupt message transmission between vehicles. We propose an adaptive traffic information forwarding mechanism, which divides traffic information in two categories, that is, early warning information and service information. The former is handled by selecting a relay node based on node connection stability evaluation, while the latter is handled by adopting an appropriate forwarding method according to the identified traffic flow density. Specifically, in a low‐density environment, we employ a broadcast method; while in a high‐density environment, we evaluate the cognitive interaction values of vehicle nodes and employ a cognitive interaction‐based method to select a relay node. Simulation results show that the proposed mechanism improves the forwarding efficiency of traffic information and yields satisfactory performance in mitigating black hole attacks.

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“…There are three commonly used protocols in DTN, that is, Epidemic protocol, 8 Spray and Wait protocol, 9 and Prophet protocol 10–12 …”

Section: Related Workmentioning

confidence: 99%

An adaptive and secure traffic information forwarding mechanism in Internet of Vehicles

Fan

Duan

et al. 2020

Int J Communication

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“…Cluster key was generated to ensure the identity of vehicles. Bilal et al proposed that fixed segment length was not good for better density estimation because the range of communication of all vehicles is in a circle. For any two consecutive segments, there was an area of the road that is not covered in any of the segments.…”

Section: Related Workmentioning

confidence: 99%

Performance enhancement of traffic information gathering (PEnTInG) algorithm for vehicular ad‐hoc networks

Kumar

2019

Int J Communication

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Summary Vehicular ad‐hoc networks (VANETs) play a vital role in today's context of vehicular traffic. In this paper, clusters of vehicles are created on the basis of average speed of the vehicles. One cluster communicates with the next cluster through a cluster head and also share the same information with next cluster heads and installed road side units (RSUs). By using this technique, we can solve the problem of rough driving behavior and road terrorism which is due to speed variation of vehicles and fake information dissemination by the drivers. Many a times, drivers may spread fake accident‐related information into the network which is a serious cause of concern in VANETs. It is ensured that such drivers are not allowed to spread wrong information in the network to avoid accidents. To solve this problem, we developed performance enhancement of traffic information gathering (PEnTInG) algorithm that selects only those drivers/vehicles as cluster heads in a cluster who has maximum value of the cluster head factor (CHF). The CHF is derived by considering different weights in range of 0 to 1 of relative average speed, time to leave, trust factor, and neighborhood degree. Further, the elected cluster head shares and stores the same information with the RSUs. In case, a driver wants to disseminate fake or wrong information in a network, then that vehicle driver can be easily tracked by the local authority by accessing RSU data. Simulation results show that the stability of PEnTInG is increased by 25% against the existing schemes viz. lowest‐ID, MCMF, and cluster‐based technique.

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“…The shortest path is then only recalculated when the current carrier cannot find a neighbor inside the attached shortest path. Other authors have also proposed different distributed data traffic statistics services [74], which show the feasibility of this approach.…”

Section: From Our Point Of 20mentioning

confidence: 99%