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Network Health Monitoring of WSNs Using Node Loss Rate Calculations

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Abstract

The inherent stringent energy and bandwidth constraints complicate the ascertainment of the reasons for the failure in wireless sensor networks (WSNs). This work proposes a scheme to monitor the networks health, based on node loss rate in a randomly deployed WSN. Considering the node loss inference relies on the knowledge stored at the sink, which is limited to the information of the nodes reporting the data in each round, we first propose a new improved data gathering strategy based on which a inference algorithm is proposed. The proposed algorithm passively tracks the health of the entire network with the help of beacon packets from every node and uses network inference techniques not only to calculate the per node loss rate, but also to precisely identify the faulty nodes in the data transmission path and identify the critical areas in the monitoring field. Results prove the efficacy of the proposed algorithm under two loss scenarios, namely equal loss and cascaded loss.

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References

  1. Sangwan, A., & Singh, R. P. (2017). Coverage hole detection and healing to enhance coverage and connectivity in 3D spaces for WSNs: A mathematical analysis. Wireless Personal Communications, 96(2), 2863–2876.

    Article  Google Scholar 

  2. Singh, V. K., Kumar, M., & Verma, S. (2017). Accurate detection of important events in WSNs. IEEE Systems Journal, 99, 1–10.

    Google Scholar 

  3. Tati, S., Silvestri, S., He, T., & La Porta, T. (2014). Robust network tomography in the presence of failures. In 2014 IEEE 34th international conference on distributed computing systems (ICDCS) (pp. 481–492). IEEE.

  4. Zhang, L., Wang, W., Gao, J., & Wang, J. (2014). Lossy links diagnosis for wireless sensor networks by utilising the existing traffic information. International Journal of Embedded Systems, 6(2–3), 140–147.

    Article  Google Scholar 

  5. Niu, Z., Li, Q., Ma, T. & Jiang, L. (2018). Research on non-cooperative topology inference method based on node location information. In 2018 IEEE 18th international conference on communication technology (ICCT) (pp. 271–275). IEEE.

  6. Hartl, G., & Li, B. (2004). Loss inference in wireless sensor networks based on data aggregation. In Proceedings of the international symposium on information processing in sensor network, Apr. 26/27, 2004 (pp. 396–404).

  7. Dermany, M. K., Sabaei, M., & Shamsi, M. (2015). Topology control in network–coding–based–multicast wireless sensor networks. International Journal of Sensor Networks, 17(2), 93–104.

    Article  Google Scholar 

  8. Firooz, M. H., & Roy, S. (2014). Link delay estimation via expander graphs. IEEE Transactions on Communications, 62(1), 170–181.

    Article  Google Scholar 

  9. Singh, V. K., & Kumar, M. (2018). A compressed sensing approach to resolve the energy hole problem in large scale WSNs. Wireless Personal Communications, 99(1), 185–201.

    Article  Google Scholar 

  10. Singh, V. K., Kumar, M., & Verma, S. (2018). Node scheduling and compressed sampling for event reporting in WSNs. IEEE Transactions on Network Science and Engineering, 1, 1.

    Article  Google Scholar 

  11. Coates, M. J., & Nowak, R. D. (2000). Network loss inference using unicast end-to-end measurement. In ITC conference on IP traffic, modeling and management (pp. 28–1).

  12. Cáceres, R., Duffield, N. G., Horowitz, J., & Towsley, D. F. (1999). Multicast-based inference of network-internal loss characteristics. IEEE Transactions on Information Theory, 45(7), 2462–2480.

    Article  MathSciNet  MATH  Google Scholar 

  13. Bu, T., Duffield, N., Presti, F. L., & Towsley, D. (2002). Network tomography on general topologies. In ACM SIGMETRICS performance evaluation review (Vol. 30, No. 1, pp. 21–30). ACM.

  14. Marano, S., Matta, V., & Willett, P. (2015). Sensor network tomography: The revenge of the detected. IEEE Transactions on Signal Processing, 63(16), 4329–4338.

    Article  MathSciNet  MATH  Google Scholar 

  15. Qin, P., Dai, B., Huang, B., Xu, G., & Wu, K. (2014). A survey on network tomography with network coding. IEEE Communications Surveys & Tutorials, 16(4), 1981–1995.

    Article  Google Scholar 

  16. Nie, L., Jiang, D., & Guo, L. (2015). End-to-end network traffic reconstruction via network tomography based on compressive sensing. Journal of Network and Systems Management, 23(3), 709–730.

    Article  Google Scholar 

  17. Li, Y., Cai, W., Tian, G., Wang, W. (2007). Loss tomography in wireless sensor network using Gibbs sampling. In Proceedings of European conference on wireless sensor network, Jan. 29–31, 2007 (pp. 150–162).

  18. Li, Y., Cai, W., Tian, G., & Wang, W. (2007). Loss tomography in wireless sensor network using Gibbs sampling. In European conference on wireless sensor networks (pp. 150–162). Springer, Berlin.

  19. Wang, L., Massey, D., Patel, K., & Zhang, L. (2004). FRTR: A scalable mechanism for global routing table consistency. In 2004 international conference on dependable systems and networks (pp. 465–474). IEEE.

  20. Wang, C., Ma, H., He, Y., & Xiong, S. (2012). Adaptive approximate data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 23(6), 1004–1016.

    Article  Google Scholar 

  21. Chandrakasan, P., & Heinzelman, W. B. (2000). Application-specific protocol architectures for wireless networks. In IEEE transactions on wireless communications.

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Authors and Affiliations

  1. Department of Information Technology, Indian Institute of Information Technology, Lucknow, India

    Vishal Krishna Singh

  2. Data Analytics Lab, Computer Centre 2, Department of Information Technology, Indian Institute of Information Technology, Jhalwa, Allahabad, Uttar Pradesh, 211012, India

    Vivek Kumar Singh & Manish Kumar

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  1. Vishal Krishna Singh
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  2. Vivek Kumar Singh
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  3. Manish Kumar
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Correspondence to Vishal Krishna Singh.

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Singh, V.K., Singh, V.K. & Kumar, M. Network Health Monitoring of WSNs Using Node Loss Rate Calculations. Wireless Pers Commun 108, 253–268 (2019). https://doi.org/10.1007/s11277-019-06400-3

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  • DOI: https://doi.org/10.1007/s11277-019-06400-3

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