A novel reputation computation model based on subjective logic for mobile ad hoc networks
A novel reputation computation model based on subjective logic for mobile ad hoc networks
复制标题
一种基于主观逻辑的移动自组织网络信誉计算模型
DOI：
10.1016/j.future.2010.03.006
复制
发表时间：
2011-05-01
影响因子：
7.5
通讯作者：
Qu, Wenyu
中科院分区：
文献类型：
--
作者：
Liu, Yining;Li, Keqiu;Qu, Wenyu
移动的自组织网络是在参与节点愿意转发其他节点的分组的假设下部署的。然而，对于民用应用，节点不是由一个单一的实体，而是以盈利为导向的独立代理人，合作不能被视为理所当然。在本文中，我们提出了一种新的声誉计算模型，发现和防止自私的行为相结合的熟悉值与主观意见。熟悉度值表示节点与另一个单独节点的熟悉程度。在我们的模型中，一个节点查询另一个节点的声誉，首先从他们的共同邻居积累主观意见。熟悉度值用于计算权重因子，该权重因子确定节点的推荐意见对信誉计算结果的影响程度。利用这种熟悉度，节点可以获得具有较低不确定性值的意见，这有助于节点更早地识别自私节点，并可以减少隔离自私节点的收敛时间。我们评估我们的模型的性能的基础上ns-2模拟分析不同的参数对网络性能的影响。仿真结果表明，我们的模型优于纯粹的主观逻辑为基础的模型，并实现了高达25%的改善收敛时间。(C)2010 Elsevier B. V.保留所有权利。
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. (C) 2010 Elsevier B.V. All rights reserved.