Abstract
A vehicular ad hoc network (VANET) is an emerging technology that has the potential to improve road safety and traveler comfort. In VANETs, mobile vehicles communicate with each other for the purpose of sharing various kinds of information. This information is very useful for preventing road accidents and traffic jams. On Contrary, bogus and inaccurate information may cause undesirable things, such as automobile fatalities and traffic congestion. Therefore, it is highly beneficial to consider risk before vehicle takes any decision based on the received information from the surrounding vehicles. To overcome these issues, we propose a new risk-based decision method for vehicular ad hoc networks. It determines a risk-based the three key elements: 1) application type and sensitivity level, 2) vehicle context and 3) driver’s attitude. This paper also provides theoretical analysis and evaluation of the proposed method, and it also discusses the applications of the proposed model.
Highlights
In vehicular ad hoc networks (VANET), vehicles and roadside units (RSUs) communicate with each other by sharing various types of information, such as safety-related warning messages
Many trust management methodologies [1,2,3,4,5,6,7] exist in the literature that mainly focuses on evaluating the trustworthiness of the received data in VANETs
We have proposed a new fuzzy risk-based decision method for vehicular ad hoc networks
Summary
In vehicular ad hoc networks (VANET), vehicles and roadside units (RSUs) communicate with each other by sharing various types of information, such as safety-related warning messages This collaborative communication is very helpful in avoiding road accidents and traffic congestion. We have proposed a new fuzzy risk-based decision method for vehicular ad hoc networks In this method, the risk is estimated based on three key factors: 1) application type and it’s sensitivity level, 2) vehicle context and 3) driver’s attitude. The risk is estimated based on three key factors: 1) application type and it’s sensitivity level, 2) vehicle context and 3) driver’s attitude The use of these three dimensions in the risk estimation makes our proposal unique.
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