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Efficient fault-tolerant routing in IoT wireless sensor networks based on path graph flow modeling with Marchenko–Pastur distribution (EFT-PMD)

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Abstract

In the internet of things (IoT) based wireless sensor network (WSN), the nodes are scattered to segregate the rapt data in the relevant field of application. In general, sensor nodes of IoT possess heterogeneous property and display cluster-based routing to transmit the data as it is considered as an efficient routing method. When one or more cluster heads (CHs) fail, the sensed data of sensor nodes that are currently serving cannot be forwarded by the faulty CHs. Consequently, data of the IoT application will not be sufficiently sensed by the sink node (gateway). As a result, information processing of this field will be affected profusely. This paper focuses on the development of a paired cluster fault-tolerant disjoint path routing in a path graph and a novel approach to solve this dilemma in polynomial time of the degree of the graph. The objective of this proposed IoT–WSN architecture is to diminish the latency, end-to-end delay as well as energy consumption and thereby improving the performance in terms of throughput and packet delivery ratio. The performance of this proposed method in IoT–WSN network is measured and affirmed using benchmark network simulator.

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References

  1. Akyildiz, I. F., Su, W., & Sankarasubramaniam, Y. (2002). Wireless sensor networks: A survey. Computer Networks,38, 393–398.

    Google Scholar 

  2. Abbasi, A. H., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communication,30, 38–41.

    Google Scholar 

  3. Chugh, A., & Panda, S. (2018). Strengthening clustering through relay nodes in sensor networks. Procedia Computer Science,132, 689–695.

    Google Scholar 

  4. Aziz, B. (2018). Towards a mutation analysis of IoT protocols. Information and Software Technology,100, 183–184.

    Google Scholar 

  5. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communication Surveys and Tutorials,17(4), 2347–2376.

    Google Scholar 

  6. Bauer, M., Boussard, M., Bui, N., Loof, J., Magerkurth, C., Meissner, S., et al. (2013). IoT reference architecture, Chapter 8. Enabling things to talk. Berlin: Springer.

    Google Scholar 

  7. Mosharaf, C., Muntasir, R. R., & Raouf, B. (2012). ViNEYard: Virtual network embedding algorithms with coordinated node and link mapping. IEEE Transactions on Networking,20(1), 206–219.

    Google Scholar 

  8. Fischer, A., Botero, J. F., Till Beck, M., de Meer, H., & Hesselbach, X. (2013). Virtual network embedding: A survey. IEEE Communications Surveys & Tutorials,15(4), 1888–1906.

    Google Scholar 

  9. Zhang, S., Qian, Z., Wu, J., Lu, S., & Epstein, L. (2014). Virtual network embedding with opportunistic resource sharing. IEEE Transactions on Parallel and Distributed Systems,25(3), 816–827.

    Google Scholar 

  10. Guo, B., Qiao, C., Wang, J., Yu, H., Zuo, Y., Li, J., et al. (2014). Survivable virtual network design and embedding to survive a facility node failure. Journal of Lightwave Technology,32(3), 483–493.

    Google Scholar 

  11. Ishaq, I., Hoebeke, J., Moerman, I., & Demeester, P. (2012). Internet of things virtual networks: Bringing network virtualization to resource constrained devices. In IEEE international journal on green computing and communications (pp. 293–300).

  12. Khan, I., Belqasmi, F., Glitho, R., Crespi, N., Morrow, M., & Polakos, P. (2015). Wireless sensor network virtualization: Early architecture and research perspectives. IEEE Network,29(3), 104–112.

    Google Scholar 

  13. Ding, M., Chen, D., Xing, K., & Cheng, X. (2005). Localized fault-tolerant event boundary detection in sensor networks. In Proceeding of the 24th annual joint conference of the IEEE computer and communications societies (INFOCOM 105). Miami, USA.

  14. Khadivi, A., & Shiva, M. (2006). FTPASC: A fault tolerant power aware protocol with static clustering for wireless sensor networks. In IEEE international journal on swireless and mobile computing, networking and communications.

  15. Cheraghlou, M. N., Khadem-Zadeh, A., & Haghparast, M. (2017). Increasing lifetime and fault tolerance capability in wireless sensor networks by providing a novel management framework. Wireless Personal Communications,92(2), 603–622.

    Google Scholar 

  16. Guan, Z., Gao, Z., Yang, Y., Li, Y., & Qiu, X. (2010). A distributed fault recovery method in clustering-based wireless sensor networks. In Proceedings of AIAI2010, state key laboratory of networking and switching technology. Beijing University of Posts and Telecommunications, Beijing, China.

  17. Shalma, H., & Rajesh, R. (2014). Dynamic cluster based energy controlled routing in wireless sensor network. In ICICES2014. S.A. Engineering College, Chennai, TamilNadu, India.

  18. Huang, J. M, Tai, S. C, & Chen, K. H. (2009). CRINet: A secure and fault-tolerant data collection scheme using 3-way forwarding and group key management in wireless sensor networks. In IEEE wireless telecommunications symposium (pp. 1–6).

  19. Man, K. L, Chen, C., & Hughes, D. (2010). Decentralized fault detection and management for wireless sensor networks. In IEEE journal on future information technology (pp. 1–6).

  20. Sampathkumar, A., Rastogi, R., Arukonda, S., Shankar, A., Kautish, S., & Sivaram, M. (2020). An efficient hybrid methodology for detection of cancer-causing gene using CSC for micro array data. Journal of Ambient Intelligence and Humanized Computing,32(3), 483–493.

    Google Scholar 

  21. Maheswar, R., & Kanagachidambaresan, G. R. (2020). Sustainable development through internet of things. Wireless Networks,26(5), 2305–2306.

    Google Scholar 

  22. Thirumoorthy, P., Kalyanasundaram, P., Maheswar, R., et al. (2019). Time-critical energy minimization protocol using PQM (TCEM-PQM) for wireless body sensor network. J Supercomputers,32(3), 483–493.

    Google Scholar 

  23. Jayarajan, P., Kanagachidambaresan, G. R., Sundararajan, T. V. P., et al. (2018). An energy-aware buffer management (EABM) routing protocol for WSN. J Supercomputers,42(6), 690–695.

    Google Scholar 

  24. Jayarajan, P., Maheswar, R., & Kanagachidambaresan, G. R. (2019). Modified energy minimization scheme using queue threshold based on priority queueing model. Cluster Computing,22, 12111–12118.

    Google Scholar 

  25. Jayarajan, P., Maheswar, R., Kanagachidambaresan, G. R., Sivasankaran, V., Balaji, M. & Das, J. (2018). Performance evaluation of fault nodes using queue threshold based on N-policy priority queueing model. In 2018 9th international conference on computing, communication and networking technologies (ICCCNT) (pp. 1–5). Bangalore.

  26. Maheswar, R., & Jayaparvathy, R. (2012). Performance analysis of fault tolerant node in wireless sensor network. In V. V. Das & J. Stephen (Eds.), Advances in communication, network, and computing, CNC. Berlin: Springer.

    Google Scholar 

  27. Sundararajan, T. V. P., Sumithra, M. G., & Maheswar, R. (2014). A novel smart routing protocol for remote health monitoring in medical wireless networks. Journal of Health Care Engineering, Multi-Science Publications,5, 1–28.

    Google Scholar 

  28. Abbasi, A. A., Younis, M. F., & Baroudi, U. A. (2015). Recovering from a node failure in wireless sensor-actor networks with minimal topology changes. IEEE Transaction on Vehicular Technology,8, 256–271.

    Google Scholar 

  29. Liu, L., et al. (2019). Fault-tolerant event region detection on trajectory pattern extraction for industrial wireless sensor networks. IEEE Transactions on Industrial Informatics,32(3), 483–493.

    Google Scholar 

  30. Mohapatra, H., & Rath, A. K. (2019). Fault-tolerant mechanism for wireless sensor network. IET Wireless Sensor Systems,18(8), 390–395.

    Google Scholar 

  31. Mohapatra, H., & Rath, A. K. (2019). Fault tolerance in WSN through PE-LEACH protocol. IET Wireless Sensor Systems,32(3), 358–365.

    Google Scholar 

  32. Li, H., et al. (2019). BIM2RT: BWAS-immune mechanism based multipath reliable transmission with fault tolerance in wireless sensor networks. Journal on Swarm and Evolutionary Computation,47, 44–55.

    Google Scholar 

  33. Malathy, S., Porkodi, V., Sampathkumar, A., NourHindia, M. H. D., Dimyati, K., Tilwari, V., et al. (2019). An optimal network coding based backpressure routing approach for massive IoT network. Wireless Networks,10(3), 189–197.

    Google Scholar 

  34. Sampathkumar, A., Mulerikkal, J., & Sivaram, M. (2020). Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks. Wireless Networks. https://doi.org/10.1007/s11276-020-02336-w.

    Google Scholar 

  35. Sampathkumar, A., Murugan, S., Rastogi, R., Mishra, M. K., Malathy, S., & Manikandan, R. (2020). Energy efficient ACPI and JEHDO mechanism for IoT device energy management in healthcare. In G. Kanagachidambaresan, R. Maheswar, V. Manikandan, & K. Ramakrishnan (Eds.), Internet of things in smart technologies for sustainable urban development. EAI/springer innovations in communication and computing. Cham: Springer.

    Google Scholar 

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Sivakumar, S., Vivekanandan, P. Efficient fault-tolerant routing in IoT wireless sensor networks based on path graph flow modeling with Marchenko–Pastur distribution (EFT-PMD). Wireless Netw 26, 4543–4555 (2020). https://doi.org/10.1007/s11276-020-02359-3

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