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针对无线传感网络中节点被俘获导致融合值偏离的问题,提出了一种基于信任的态势数据融合机制,根据历史信任与节点数据相关度,制定信任度感知规则,并分3个阶段确保数据完整性.首先,事件检测时,运用最可信多数规则,提升事件检测的准确性;其次,数据融合时,运用数据筛选规则处理不可靠数据,提升数据可靠性;最后,一致性检测时,采用一致性检测规则,在降低通信量与计算量的同时,有效而简便地检测了融合中心一致性.新机制能有效遏制谎报攻击,改善异常节点数占有比重大时的网络性能,让融合值更贴近真实值.理论分析及仿真证实了新机制的可靠性与有效性.
Aiming at the problem that the nodes in wireless sensor networks are trapped and lead to the deviation of the fusion values, a confidence-based data fusion mechanism is proposed. According to the correlation between historical trust and node data, a trust awareness rule is formulated. Integrity.First, the most credible most rules are used to improve the accuracy of event detection in event detection. Secondly, when data fusion, data filtering rules are used to deal with unreliable data to improve data reliability. Finally, Consistency detection rules are adopted to reduce the amount of traffic and computation and to detect the convergence of the fusion centers effectively and conveniently.The new mechanism can effectively restrain the misrepresentation attacks and improve the network performance when the proportion of abnormal nodes occupies a large proportion, Closer to the true value.Theoretical analysis and simulation confirm the reliability and effectiveness of the new mechanism.