Current trust and reputation management approaches usually offer rigid and inflexible mechanisms to compute reputation scores, which hinder their dynamic adaptation to the current circumstances in the system where they are deployed.
This model computes reputation score based on the recommendation given by other vehicles and RSUs.
Chen et al.[11] distinguishes between recommendations by computing the reputation for each recommender.
Therefore, to compute the reputation value (T rep), the indirect event trust value and the previous reputation value are considered.
Similarly, a collaborative reputation mechanism to enforce node cooperation in MANETs called CORE [9] also uses the second-hand information to compute the reputation of a node.
We present a distributed and secure method to compute global reputation values, based on Power iteration.
It is argued that social-network based (or group-based) trust metric is effective in resisting various attacks, which evaluates groups of assertions "in tandem", and generally computes peers' reputation ranks according to peers' social positions in a trust graph.
The server decrypts and verifies the signature of the feedback, and then computes a reputation score using any RS implementation.
If we desire a performance level no less than l∈[1,ϖ], we compute the reputation expectation for each user j as follows: begin{array}{*{20}l} w_{j}=sum_{s=1}^{l}{c_{j,s} s} end{array} (14).
In this article, we propose a novel reputation scheme which incorporates uncertainty-based subjective logic into the reputation computing.
