科研之友
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摘要
Mobile ad hoc networks are deployed under the assumption that participating nodes are willing to forward other nodes' packets. However, for civilian applications where nodes are not owned by a single entity but are profit-oriented independent agents, cooperation cannot be taken for granted. In this paper, we present a novel reputation computation model to discover and prevent selfish behaviors by combining familiarity values with subjective opinions. The familiarity value represents a node's familiar degree with another individual node. In our model, a node that queries another's reputation first accumulates subjective opinions from their common neighbors. The familiarity values are used to calculate the weighting factor that determines how much a node's recommending opinion impacts on the reputation computation result. The utilization of this familiarity allows nodes to obtain opinions with lower uncertainty values, which helps nodes to recognize selfish nodes much earlier and can decrease the convergence time for isolating selfish nodes. We evaluate the performance of our model based on ns-2 simulations to analyze the impact of different parameters on the network performance. The simulation results show that our model outperforms the pure subjective logic-based model and achieves up to a 25% improvement in the convergence time.
