WSN中基于逐级重建的Provenance增量压缩

Provenance increment compression based on gradual reconstruction in WSNs

在无线传感器网络(WSN)中,一般使用溯源(Provenance)对基站(BS)接收的数据进行可信性评估。针对当前的Provenance分段传输方法平均压缩比较低,并且只有当获取所有Provenance分段后才可以解码Provenance等问题,提出了一种基于逐级重建的Provenance增量传输方法,即在WSN多级分簇基础上,运用整数素因子分解具有唯一性这一性质,首次传输最粗粒度的Provenance,其余每次只传输各个粒度之间的增量信息,实现了在解码Provenance时BS能够按粒度从高到低逐级重建Provenance,因此有效克服了上述现有Provenance分段传输方法平均压缩比低、在未获得全部Provenance分段前不能解密等问题。理论分析与实验数据显示,与传统的Provenance分段传输方法相比,本方法在Provenance压缩比、鲁棒性以及能耗方面均优于当前存在的Provenance分段传输方法。

In wireless sensor networks(WSNs),provenance is generally used to assess the credibility of the data received by a base station(BS).The present segmented method of provenance transmission has defects,such as a relatively low average compression ratio and a limited ability of BS to decode provenance only when all blocks are received.Therefore,an incremental provenance transmission scheme based on gradual reconstruction was proposed,i.e.,on basis of WSN hierarchical clustering.By utilizing the uniqueness of the integer prime number factorization,the provenance with the largest granularity is first transmitted.Subsequent provenances in terms of granularity levels will only be transmitted after they are decoded.The BS can reconstruct provenance from high to low granularity;hence,the abovementioned defects can be effectively overcome.Both theoretical analyses and experimental results show that this scheme outperforms the existing segmented provenance transmission method on aspects,such as compression ratio,energy consumption,and robustness.