基于多粒度拓扑图的无线传感器网络逐级精化溯源方法
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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 theory.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%.
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 theory.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%.
引文
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[11]袁裕琳.WSN中基于多级算术编码的溯源数据压缩方法[D].镇江:江苏大学,2016.(YUAN Y L.Data provenance compression using cluster based arithmetic coding in wireless sensor networks[D].Zhenjiang:Jiangsu University,2016.)
[12]YUAN L,KEVIN C,DESOUZA,et.al.Measuring agility of networked organizational structures via network entropy and mutual information[J].Applied Mathematics and Computation,2010,216(10):2824-2836.
[13]严蔚敏,吴伟民.数据结构[M].北京:清华大学出版社,2007:135.(YAN W M,WU W M.Date Structure[M].Beijing:Tsinghua University Press,2007:135.)
[14]宋泽.基于分层视图的WSN溯源压缩算法的研究[D].镇江:江苏大学,2017.(SONG Z.Research on provenance compression algorithm based on hierarchical view in WSN[D].Zhenjiang:Jiangsu University,2017.)
[2]李建中,高宏.无线传感器网络的研究进展[J].计算机研究与发展,2008,45(1):1-15.(LI J Z,GAO H.Survey on sensor network research[J].Journal of Computer Research and Development,2008,45(1):1-15.
[3]DOGAN G.A survey of provenance in wireless sensor networks[J].Ad Hoc&Sensor Wireless Networks,2016,30(1/2):21.
[4]BISDIKIAN C,KAPLAN L M,SRIVASTAVA M B.On the quality and value of information in sensor networks[J].ACM Transactions on Sensor Networks,2013,9(4):48.
[5]ALAM S M I,FAHMY S.A practical approach for provenance transmission in wireless sensor networks[J].Ad Hoc Networks,2014,16(4):28-45.
[6]SULTANA S,SHEHAB M,BERTINO E.Secure provenance transmission for streaming data[J].IEEE Transactions on Knowledge&Data Engineering,2013,25(8):1890-1903.
[7]SHEBARO B,SALMIN S,BERTINO E,et al.Demonstrating a lightweight data provenance for sensor networks[C]//Proceeding of the 2012 ACM Conference on Computer and Communications Security.New York:ACM,2012:1022-1024.
[8]WANG C,BERTINO E.Sensor network provenance compression using dynamic Bayesian networks[J].ACM Transactions on Sensor Networks,2017,13(1):Article No.5.
[9]张燕平,张铃,吴涛.不同粒度世界的描述法---商空间法[J].计算机学报,2004,27(3):328-333.(ZHANG Y P,ZHANG L,WU T.The representation of different granular worlds:a quotient space[J].Chinese Journal of Computers,2004,27(3):328-333.)
[10]WANG C,HUSSAIN S R,BERTINO E.Dictionary based secure provenance compression for wireless sensor networks[J].IEEETransactions on Parallel&Distributed Systems,2016,27(2):405-418.
[11]袁裕琳.WSN中基于多级算术编码的溯源数据压缩方法[D].镇江:江苏大学,2016.(YUAN Y L.Data provenance compression using cluster based arithmetic coding in wireless sensor networks[D].Zhenjiang:Jiangsu University,2016.)
[12]YUAN L,KEVIN C,DESOUZA,et.al.Measuring agility of networked organizational structures via network entropy and mutual information[J].Applied Mathematics and Computation,2010,216(10):2824-2836.
[13]严蔚敏,吴伟民.数据结构[M].北京:清华大学出版社,2007:135.(YAN W M,WU W M.Date Structure[M].Beijing:Tsinghua University Press,2007:135.)
[14]宋泽.基于分层视图的WSN溯源压缩算法的研究[D].镇江:江苏大学,2017.(SONG Z.Research on provenance compression algorithm based on hierarchical view in WSN[D].Zhenjiang:Jiangsu University,2017.)