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High Reliable In-Network Data Verification in Wireless Sensor Networks

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Abstract

In order to provide efficient and suitable services for users in a ubiquitous computing environment, many kinds of context information technologies have been researched. Wireless sensor networks are among the most popular technologies providing such information. Therefore, it is very important to guarantee the reliability of sensor data gathered from wireless sensor networks. However, there are several factors associated with faulty sensor readings which make sensor readings unreliable. In this paper, we classify faulty sensor readings into sensor faults and measurement errors, then propose a novel in-network data verification algorithm which includes adaptive fault checking, measurement error elimination and data refinement. The proposed algorithm eliminates faulty readings as well as refines normal sensor readings, to increase reliability. Also, to achieve scalability of sensor networks and minimize network overhead, the proposed scheme involves a distributed implementation in a local area. The simulation study shows that the in-network data verification algorithm is highly reliable and its network overhead is very low compared to previous works. Reliability and overhead is improved by a maximum of 10–30% and 70%, respectively.

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References

  1. Akyildiz I.F., Su W., Sankarasubramaniam Y., Cayirci E. (2002) Wireless sensor networks: A survey. Elsevier Computer Networks 38: 393–422

    Article  Google Scholar 

  2. Park, C., & Chou, P. H. (2006). Eco: Ultra-wearable and expandable wireless sensor platform. In Proceedings of the third international workshop on body sensor networks, April 3–5, 2006.

  3. Pasquale, J., Dozier, J., & Katz, R. H. (1995). The redwood project. compcon, 40th IEEE Computer Society International Conference (COMPCON’95), p. 478.

  4. Thomopoulos S.C.A., Viswanathan R., Bougoulias D.K. (1989) Optimal distributed decision fusion. IEEE Transaction on Aerospace and Electronic Systems 25(5): 761–764

    Article  Google Scholar 

  5. Chair Z., Varshney P.K. (1986) Optimal data fusion in multiple sensor detection systems. IEEE Transactions on Aerospace and Electronic Systems 22(1): 98–101

    Article  Google Scholar 

  6. Tsitsiklis J., Athans M. (1985) On the complexity of distributed decision problems. IEEE Transactions on Automatic Control 30(5): 440–446

    Article  MATH  MathSciNet  Google Scholar 

  7. Krishnamachari, B.,&Iyengar, S. (2003). Efficient and fault-tolerant feature extraction in wireless sensor networks. IPSN 2003, Lecture Note in Computer Science, 2634, 488–501.

  8. Krishnamachari B., Iyengar S. (2004) Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Transactions on Computers 53(3): 241–250

    Article  Google Scholar 

  9. Clouqueur, T., Ramanathan, P., Saluja, K. K., & Wang, K.-C. (2001). Value-fusion versus decision-fusion for fault-tolerance in collaborative target detection in sensor networks. In Proceedings of Fourth Annual Conference Information Fusion, Aug. 2001.

  10. Clouqueur T., Saluja K.K., Ramanathan P. (2004) Fault tolerance in collaborative sensor networks for target detection. IEEE Transactions on 53(3): 320–333

    Article  Google Scholar 

  11. Luo X., Dong M., Huang Y. (2006) On distributed fault-tolerant detection in wireless sensor networks. IEEE Transactions on Computers 55(1): 58–70

    Article  Google Scholar 

  12. Bokareva, T., Bulusu, N., & Jha, S. (2005). SASHA: Towards a self-healing hybrid sensor network architecture. In Proceedings of the 2nd IEEE international workshop on embedded networked sensors (EmNetS-II), Sydney, Australia, May 2005.

  13. Ding, M., Chen, D., Xing, K., & Cheng, X. (2005). Localized fault-tolerant event boundary detection in sensor networks. In Proceedings of the 24th annual joint conference of the IEEE computer and communications societies (INFOCOM 2005) (Vol. 2, pp. 902–913). Mar. 13–17, 2005.

  14. Krasniewski, M., Varadharajan, P., Rabeler, B., Bagchi, S., & Hu, Y. C. (2005). TIBFIT: Trust Index Based Fault Tolerance for arbitrary data faults in sensor networks. In The international conference on Dependable Systems and Networks (DSN), Yokohama, Japan, Jun. 28–Jul. 1, 2005.

  15. Ye F., Zhong G., Lu S., Zhang L. (2005) GRAdient broadcast: A robust data delivery protocol for large scale sensor networks. ACM Wireless Networks 11(2): 285–298

    Article  Google Scholar 

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Correspondence to Dong-Wook Lee.

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Lee, DW., Kim, JH. High Reliable In-Network Data Verification in Wireless Sensor Networks. Wireless Pers Commun 54, 501–519 (2010). https://doi.org/10.1007/s11277-009-9737-9

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  • DOI: https://doi.org/10.1007/s11277-009-9737-9

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