RTEAM: Risk-Based Trust Evaluation Advanced Model for VANETs

Abstract

In Vehicular ad hoc networks (VANETs), vehicles share and exchange information regarding road safety and traffic conditions. Thus, trust is established among vehicles to ensure the integrality and reliability of the received reports. Ensuring the security of VANETs is the key to enhance road safety, and for this purpose, several trust establishing, evaluation, and management models have been proposed. When a vehicle receives conflicting reports about an event such as a car accident from its neighboring vehicles, the receiving vehicle must decide which report has to follow. Therefore, the vehicle takes advantage of the available data about the report's sender. Then, the vehicle takes the right action. To this end, we propose a Risk-based Trust Evaluation Advanced Model (RTEAM) based on Multifaceted Trust and Hop-based trust to take action. The proposed model provides a decision-making process according to the risk estimation for each required action of both reports (i.e., reports that deny or confirm the event). The risk is estimated according to the likelihood of taking an incorrect action and its associated impact. Finally, a decision is made corresponding to the action with the lowest risk. The experimental results show that the proposed model shows that the risk-based trust model outperforms a purely trust-based model in terms of undefined cases and true positive rates.

MESMERIC: Machine Learning-Based Trust Management Mechanism for the Internet of Vehicles

“…A holistic overview of the existing trust management mechanisms reveals that a number of trust-based parameters have been applied in different settings in order to measure and evaluate trust. The trust-based parameters include, but are not limited to, resource availability [6], similarity [19,25,26], familiarity [6,7,22,27], timeliness [7], context [19,20,28,29], cooperativeness [19,30], community-of-interest (CoI) [19,30], confidence [31,32], reward [28,33], attitude, subjective norms, and perceptual behavioral control [5], freshness of data [34], and packet delivery ratio [7,25,31,35,36]. Also, the selection of a trust-based threshold for determining trustworthy and untrustworthy behavior is crucial.…”

On the design of trust and mobility based evaluation for intelligent collaborative UAVs assisted VANETs

“…To improve the effectiveness of communication in VANETs in earlier research several trust models are introduced in VANETs. They are trust cascading-based emergency message dissemination (TCEMD) model [13], risk-based trust evaluation advanced model (RTEAM) [14], trust model for location privacy protection [15], context-aware trust management model [16], anonymous cloaking zone creation approach based on a trust mechanism [17], "VAR2" strategy that permits autonomous trust model [18], a vehicle trust evaluation approach based on a hidden markov model (HMM) that increases the accuracy in the identification of malicious activity [19], a trusted routing strategy based on block-chain and fuzzy logic to enhance the identification of rogue nodes in VANET [20], a consortium block-chain-based strategy for preventing insider assaults in the VANET system utilizing the trusted AODV protocol [21], adaptive traffic-management system (ATM), an effective active-detection trust management system [22], the trust model to protect the UAVs assisted VANETs [23], UAV-assisted ubiquitous trust evaluation (UUTE) framework [24], and lightweight attestation mechanism [25]. Once after analysis the earlier research which is based on trust in UAV assisted VANETs the major drawback which is identified are increased energy consumption, delay and overhead in the network.…”

Analysis of TuSimple’s Investment Value

“…The author concluded by testing and developing various vehicle safety technologies. It also combines a security risk assessment framework established by taking the GMITS model as an example to assess threats and vulnerabilities [9]. This research was focused on the AI stock, this one provides a solution called Stock-AssIstant that models the reliability of stock commentary by taking into account a variety of factors such as stock price movements, commentary content, and analyst performance.…”