A Novel Reputation Computation Model Based on Subjective Logic for Mobile Ad hoc Networks

Selfish behaviors significantly affect the overall performance of mobile ad hoc networks (MANETs). Reputation systems have been proved to be an efficient way to block such behaviors in MANETs. Several reputation models based on subjective logic have been proposed to improve the reputation mechanism, in which an uncertainty value is introduced for reputation computation when the local information is not sufficient. However, these reputation models fail to utilize the recommended opinions effectively and reduce the uncertainty value while these opinions are combined. In this paper, we propose a novel reputation computation model based on subjective logic to overcome the above deficiencies. We consider not only the recommenders' trustworthiness but also the familiarities among the recommended nodes during reputation computations. This familiarity is defined as a certainty value which is used to weight opinions in reputation computation. In our model, the recommendations of nodes with low trustworthiness or high uncertainty on the recommended nodes have little impact on the recommended nodes' reputations so that nodes can reach opinions with lower uncertainty value through reputation computations. We conduct simulations to evaluate our model on its performance. The simulation results show that the proposed model achieves about 40% improvement in the time of discovering and isolating selfish nodes compared with a previous model based on subjective logic and selfish nodes' success rate is further reduced by up to 10%.