基于多粒度拓扑图的无线传感器网络逐级精化溯源方法

摘要

针对溯源数据分段传输方法要求所有分段准确到达基站(BS)后才能解码,鲁棒性较弱的问题,提出一种无线传感器网络(WSN)溯源逐级精化方法.首先,在BS端利用商空间划分理论将较大的WSN拓扑图划分为由少量抽象节点组成的较粗粒度的拓扑图;然后,利用字典编码溯源的方式分段传输溯源;最后,在BS端根据依次到达的分段进行逐级精化解码,实现了在BS端由粗到细逐级精化解码溯源的过程,且BS可以根据前期解码出的较粗粒度下的溯源信息判断是否放弃此数据还是须采用更细粒度的数据进行深入评估.理论分析、仿真与实验数据均表明,与传统分段方法相比,所提方法平均压缩比提高约51.8％,平均能量消耗降低约50.5％.%Focusing on the problem that the session-based provenance scheme requires that all of the provenance sessions must be received by the Base Station (BS) correctly before decoding,which decreases the robustness of the scheme,a provenance scheme with stepwise refinement was proposed.Firstly,a large Wireless Sensor Network (WSN) topology was divided into different granularities which were composed of a series of abstract nodes through quotient space dividing theol.Then,the dictionary based provenance scheme was used to transmit provenance at different grained layers.Therefore,the BS reconstructed the provenance from the coarse-grained layer to the fine-grained one as well as judged whether to discard the data or not according to the results of the coarse-grained layer's decoding.The theoretical analysis,simulations and experimental results show that in comparison of the traditional provenance schemes,the average compression ratio of the proposed method is improved by 51.8％,and the energy consumption of that is reduced by 50.5％.