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为了提高机会网络中数据传输成功率,降低网络延迟,达到快速收集数据的目的,提出一种在机会网络中基于时间序列分析的自适应信任模型.该模型采用集中评价,分布存储的信任机制,根据即时参数和过程参数两类评价因素对网络中节点进行评价.模型利用数据传输证据链收集评价证据,结合时间序列分析理论动态调整评价参数的权重系数,对参与数据转发的节点进行评价,并将评价表通过覆盖更新机制扩散到网络中分布存储.最终,节点相遇时结合即时评价和过程评价选择下一跳转发路由.仿真实验结果表明,与现有的典型机会网络信任机制相比,该模型有效提高了消息传输成功率,降低了消息传输时延.
In order to improve the success rate of data transmission in opportunistic networks, reduce the network delay and achieve the purpose of rapid data collection, an adaptive trust model based on time series analysis in opportunistic networks is proposed.This model adopts a trust mechanism of centralized evaluation and distributed storage, According to two kinds of evaluation factors: instantaneous parameter and process parameter, the nodes in the network are evaluated.The model uses the evidence chain of data transmission evidence to collect the evidence of evaluation, combined with the time series analysis theory to dynamically adjust the weight coefficient of evaluation parameters, and evaluate the nodes involved in data forwarding The evaluation table is spread to the network for distribution and storage through the coverage update mechanism.Finally, nodes meet with immediate evaluation and process evaluation to select the next-hop forwarding route.The simulation results show that compared with the existing typical opportunistic network trust mechanism, The model effectively improves the success rate of message transmission and reduces the message transmission delay.