Abstract
The hierarchical cluster-based topology is commonly accepted as an optimal structure for sensor network to increase communication scalability, prolong network lifetime, and reduce data redundancy. However, the data privacy and security are challenging the proliferation of clustering wireless sensor network (CWSN) due to its highly constrained resources and violably deployed environments, which make it infeasible to directly apply traditional cryptography and therefore vulnerable to various attacks. This article proposes a scheme that provides efficient privacy-preserving data fusion as well as malicious data tolerance by mining concealed data within groups. And the dynamically organized groups in each cluster improves resilience against large number of node compromise comparing with the existing data aggregation schemes. The simulation results and mathematical comparison show the effectiveness and fitness of our scheme for CWSN in terms of fault tolerance and process efficiency, which costs a little of additional overheads in memory and communication.
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References
ANSI X9.63. (1999). Elliptic curve key agreement and key transport protocols. American Bankers Association.
Boulis A., Ganeriwal S., Srivastava M.B. (2003) Aggregation in sensor networks: An energy-accuracy trade-off. ElseVier J. Ad Hoc Netw 1(2–3): 317–331
Castelluccia, C., Mykletun, E., & Tsudik, G. (2005). Efficient aggregation of encrypted data in wireless sensor networks. In Proceedings of Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous 2005), pp. 109–117.
Chan, H., Perrig, A., & Song, D. (2003). Random key predistribution schemes for sensor networks. In Proceedings of the 2003 IEEE Symposium on Security and Privacy (pp. 197–213).
Chang, S., Eberle, H., Gupta, V., & Gura, N. Elliptic curve cryptography—How it Works. Sun Microsystems Laboratories Next Generation Crypto. http://research.sun.com/projects/crypto.
Cheng X., Narahari B., Simha R., Cheng M.X., Liu D. (2002) Strong minimum energy topology in wireless sensor networks: NP completeness and heuristics. IEEE Trans Mob Comput, 2(3): 248–256
Girao, J., Schneider, M., & Westhoff, D. (2004). CDA: Concealed data aggregation in sensor networks. In Proceedings of the ACM Workshop on Wireless Security 2004.
Gupta, V., Millard, M., Fung, S., Zhu, Y., Gura, N., Eberle, H., & Shantz, S. C. (2005). Sizzle: A standards-based end-to-end security architecture for the embedded internet. In Proceedings of IEEE International Conference on Pervasive Computing and Communications 2005 (pp. 247–246).
Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques (2nd ed.). Elsevier Inc.
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocols for wireless microsensor networks. In Proceedings of Hawaaian International Conference on Systems Science 2000.
Heinzelman W.R., Chandrakasan A., Balakrishnan H. (2002) An Application-specific protocol archtitecture for wireless microsensor networks. IEEE Trans Wireless Comm 1(4): 660–670
IETF Internet-draft (2003). Elliptic curve Diffie-Hellman key exchange for the SSH transport level protocol. Draft-stebila-secsh-ecdh-00.txt specifying the use of elliptic curve cryptography with SSH 2003.
Josep D.-F. (1996) A new privacy homomorphism and applications. Information Process Lett. 60(5): 277–282
Madhukar, A., Zachary, I., & Insup, L. (2005). Quantifying eavesdropping vulnerability in sensor networks. ACM International Conference Proceeding Series 2005, Vol. 96: Proceedings of the 2nd International Workshop on Data Management for Sensor Networks (pp. 3–9).
Malan, D., Welsh, M., & Smith, M. (2004). A public-key infrastructure for key distribution in TinyOS based on elliptic curve cryptography. In Proceedings of the 2nd IEEE Conference on Sensor and Ad Hoc Communications and Networks (SECON 2004) (pp. 71–80).
Park, S., & Srivastava, M. (1999) Power aware routing in sensor networks using dynamic source routing. ACM mobile networks and applications, special issue on energy conserving protocols in wireless networks.
Perrig A., Szewczyk R. (2002) SPINS: Security protocols for sensor. J. Wireless Netw. 8: 521–534
Przydatek B., Song D., & Perrig, A. (2003). SIA: Secure information aggregation in sensor networks. ACM Conference on Embedded Networked Sensor Systems 2003.
Ren, S. Q., Kim, D. S., & Park, J. S. (2007) A secure data aggregation scheme for wireless sensor networks. In Proceedings of Frontiers of High Performance Computing and Networking—ISPA 2007 Workshops, (pp. 32–40).
Singh, S., Woo, M., & Raghavendra, C. (1998). Power-aware routing in mobile ad hoc networks. In Proceedings of Fourth Annual ACM/IEEE International Conference on Mobile Computing Networking (MobiCom 1998).
Tanveer, Z., & Albert, Z. (2006). A security framework for wireless sensor networks. In Proceddings of International Conference on Small-angle Scattering 2006 (pp. 49–53).
Wagner, D. (2004). Resilient aggregation in sensor networks. In Proc. Workshop on Security of Ad Hoc and Sensor Networks (SASN 2004) (pp. 78–87).
Westhoff D., Girao J., Schneider M. (2006) Concealed data aggregation for reverse multicast traffic in sensor networks: Encryption, key distribution, and routing adaptation. IEEE Trans. Mob. Comput. 5(10): 1417–1431
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Ren, S.Q., Park, J.S. Fault-Tolerance Data Aggregation for Clustering Wireless Sensor Network. Wireless Pers Commun 51, 179–192 (2009). https://doi.org/10.1007/s11277-008-9598-7
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DOI: https://doi.org/10.1007/s11277-008-9598-7