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Research and Improvement of Wireless Sensor Network Secure Data Aggregation Protocol Based on SMART

  • Jun Wang
  • Yu Chen
Article

Abstract

The privacy-preserving of information is one of the most important problems to be solved in wireless sensor network (WSN). Privacy-preserving data aggregation is an effective way to protect security of data in WSNs. In order to deal with the problem of energy consumption of the SMART algorithm, we present a new dynamic slicing D-SMART algorithm which based on the importance degree of data. The proposed algorithm can decrease the communication overhead and energy consumption effectively while provide good performance in preserving privacy by the reasonable slicing based on the importance degree of the collected raw data. Simulation results show that the proposed D-SMART algorithm improve the aggregation accuracy, enhance the privacy-preserving, reduce the communication overhead to some extent, decrease the energy consumption of sensor node and prolong the network lifetime indirectly.

Keywords

Component Dynamic data slicing Wireless sensor network Data aggregation Privacy-preserving Communication overhead 

Notes

Acknowledgements

This paper is supported by the project of Natural Science Foundation of Liaoning Province (2015020082, 2015020643), Liaoning BaiQianWan Talents Program, Liaoning Innovative Talents Program, Liaoning Special Professor Project and Shenyang program for scientific and technological innovation talents of middle and young people.

References

  1. 1.
    T. Ko, J. Hyman and E. Graham, Embedded imagers: detecting, localizing, and recognizing objects and events in natural habitats, Proceedings of the IEEE, Vol. 98, No. 11, pp. 1934–1946, 2010.CrossRefGoogle Scholar
  2. 2.
    R. Szewczyk and A. Ferencz, Energy implications of network sensor designs. Berkeley Wireless Research Center Report, 2000.Google Scholar
  3. 3.
    W. He, X. Liu, H. Nguyen, K. Nahrstedt, and T. Abdelzaher, PDA: privacy-preserving data aggregation in wireless sensor network. In Proceedings of the 26th IEEE International Conference on Computer Communications, Anchorage, AK, pages 2045–2053, 2007.Google Scholar
  4. 4.
    L. I. Sen and Yang Geng, Research on precision aggregation privacy-preserving algorithm in wireless sensor networks, Computer Technology and Development, Vol. 23, pp. 139–142, 2013.Google Scholar
  5. 5.
    Geng Yang, Sen Li and Zheng-yu Chen, High-accuracy and privacy-preserving oriented data aggregation algorithm in sensor networks, Chinese Journal Of Computers, Vol. 36, pp. 188–200, 2013.Google Scholar
  6. 6.
    Lu-sheng Shi and Xiao-lin Qin, Privacy-preserving data aggregation algorithm with integrity verification, Computer Science, Vol. 40, pp. 197–202, 2013.Google Scholar
  7. 7.
    Yong-jian Fan, Hong Chen and Xiao-ying Zhang, Data privacy preservation in wireless sensor networks, Chinese Journal Of Computers, Vol. 35, pp. 1141–1146, 2012.MathSciNetGoogle Scholar
  8. 8.
    Jiang-hong Guo and Jian-feng Ma, Efficient encrypted data aggregation algorithm for wireless sensor networks, Journal of Xidian University, Vol. 40, pp. 95–101, 2013.Google Scholar
  9. 9.
    Sun Long and Xu Ting-rong, A new uneven clustering routing protocol in WSN based on chain-cluster type, Computer Applications and Software, Vol. 32, pp. 106–109, 2015.Google Scholar
  10. 10.
    L. Wang and S.Y. Zhao, Research and application of key technology of secure data aggregation in wireless sensor network, Computer Engineering and Applications, pp. 63–79, 2016.Google Scholar
  11. 11.
    Zheng-yu Chen and Geng Yang, Survey of data aggregation for wireless sensor networks, Application Research of Computers, Vol. 28, pp. 1601–1604, 2011.Google Scholar
  12. 12.
    Sumedha Sirsikar and Samarth Anavatti, Issues of data aggregation methods in wireless sensor network: a survey, Procedia Computer Science, Vol. 49, pp. 194–201, 2015.CrossRefGoogle Scholar
  13. 13.
    S. Roy, M. Conti and S. Setia, Secure data aggregation in wireless sensor networks: filtering out the attacker’s impact, IEEE Transactions on Information Forensics and Security, Vol. 9, No. 4, pp. 681–694, 2014.CrossRefGoogle Scholar
  14. 14.
    C.-X. Liu, Y. Liu and Z.-J. Zhang, High energy-efficient privacy secure data aggregation for wireless sensor networks, International Journal of Communication Systems, Vol. 26, No. 3, pp. 380–394, 2013.MathSciNetCrossRefGoogle Scholar
  15. 15.
    M. Rezvani, A. Ignjatovic, E. Bertino and S. Jha, Secure data aggregation technique for wireless sensor networks in the presence of collusion attacks, IEEE Transactions on Dependable and Secure Computing, Vol. 12, No. 1, pp. 98–110, 2015.CrossRefGoogle Scholar
  16. 16.
    J. Girao, D. Westhoff, and M. Schneider, CDA: concealed data aggregation for reverse multicast traffic in wireless sensor networks. In IEEE International Conference on Communications, 2005. ICC 2005. 2005, vol. 5, pages 3044–3049, 2005.Google Scholar
  17. 17.
    Sankardas Roy, Mauro Conti and Sushil Jajodia, Secure data aggregation in wireless sensor networks, IEEE Transactions on Information Forensics and Security, Vol. 7, No. 3, pp. 1040–1052, 2012.CrossRefGoogle Scholar
  18. 18.
    M. Conti, L. Zhang, S. Roy and R. DiPietro, Privacy-preserving robust data aggregation in wireless sensor networks, Security and Communication Networks, Vol. 2, pp. 195–213, 2009.CrossRefGoogle Scholar
  19. 19.
    M. Yu, C. Li, G. Chen and J. Wu, An energy efficient clustering scheme in wireless sensor networks, Journal of Control Theory and Applications, Vol. 9, No. 1, pp. 99–119, 2011.CrossRefGoogle Scholar
  20. 20.
    T.W. Kuo and M.J. Tsai, On the construction of data aggregation tree with minimum energy cost in wireless sensor networks: NP-completeness and approximation algorithms. In IEEE INFOCOM, pages 2591–2595, 2012.Google Scholar
  21. 21.
    C. M. Chao and T. Y. Hsiao, Design of structure-tree and energy-balanced data aggregation in wireless sensor networks, Journal of Network and Computer Applications, Vol. 12, pp. 2012–2030, 2012.Google Scholar
  22. 22.
    H. Yousefi, M. H. Yeganeh and N. Alinaghipour, Structure-free real-time data aggregation in wireless sensor networks, Computer Communication, Vol. 35, pp. 1132–1140, 2012.CrossRefGoogle Scholar
  23. 23.
    S. Peter, K. Piotrowski, and P. Langendoerfer, On concealed data aggregation for WSNs. In IEEE Consumer Communication and Networking Conference, pages 192–196, 2007.Google Scholar
  24. 24.
    C. Castelluccia, E. Mykletun, and G. Tsudik, Efficient aggregation of encrypted data in wireless sensor networks. In The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, pages 109–117, 2005.Google Scholar
  25. 25.
    S.R. Madden and M.J. Franklin, TAG: a tiny aggregation service for ad hoc sensor networks. In OSDI, 2002.Google Scholar
  26. 26.
    Adwitiya Sinha and D. K. Iobiyal, Prediction models for energy efficient data aggregation in wireless sensor network, Wireless Personal Communications, Vol. 84, pp. 1325–1343, 2015.CrossRefGoogle Scholar
  27. 27.
    Qi-biao Guo, Wireless sensor network based on homomorphic encryption security data aggregation analysis, Network Security Technology and Application, pp. 76–77, 2015.Google Scholar
  28. 28.
    H. Bao and R.A. Lu, A lightweight data aggregation scheme achieving privacy preservation and data integrity with differential privacy and fault tolerance, Peer-to-Peer Networking and Applications, pp. 1–16, 2015.Google Scholar
  29. 29.
    A. Sinha and D. K. Lobiyal, Prediction models for energy efficient data aggregation in wireless sensor network, Wireless Personal Communication, Vol. 72, No. 2, pp. 1–19, 2015.Google Scholar
  30. 30.
    A. Awang and S. Agarwal, Data aggregation using dynamic selection of aggregation points based on RSSI for wireless sensor network, Wireless Personal Communication, Vol. 80, No. 2, pp. 611–633, 2015.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science and TechnologyShenyang University of Chemical TechnologyShenyangChina

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