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EFT: Novel Fault Tolerant Management Framework for Wireless Sensor Networks

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Abstract

The main application of wireless sensor networks is monitoring, and the nodes of these networks are located usually in harsh environments. Network management is the most important factor in network operation and efficiency. From the perspective of distributed management task force, management should have FCAPS features. In the FCAPS word, the letter F stands for Fault tolerance, the letter C stands for configuration, the letter A represents the accounting, the letter P represents the performance, and the letter S stands for the security. So the first feature is fault tolerance. This paper proposes a management framework for wireless sensor networks that both increases the quality of fault tolerance and decreases the energy consumption of the network. Due to the very fact that the nature of the relationship between energy consumption and fault tolerance is essentially a trade-off, the proposed framework will bilaterally increase the network lifetime. Based on the results, implementing this framework will noticeably increase fault tolerance and decrease the energy consumption. Moreover, for protocols with cluster heads that change frequently at the end of each round, simulations have shown comparatively more success.

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References

  1. Zhang, Y., Yang, L., & Chen, J. (2010). RFID and sensor networks. Boca Raton: CRC Press.

    Google Scholar 

  2. Yu, M., Mokhtar, H., & Merabti, M. (2007). Fault management in wireless sensor networks. IEEE Wireless Communications, 14, 13–19.

    Article  Google Scholar 

  3. Asim, M., & Mokhtar, M. (2009). A cellular approach to fault detection and recovery in wireless sensor network. In Third international conference on sensor technologies and applications.

  4. Saleh, I., Eltoweissy, M., Ahbariya, A., & El-sayed, H. (2007). A fault tolerance management framework for wireless sensor networks. Journal of Communications, 2(4).

  5. Paradis, L., & Han, Q. (2007). Fault management in wireless sensor networks: A survey. Journal of Networks and Systems Management, 15(2), 171–190.

    Article  Google Scholar 

  6. Desovski, D., Liu, Y., & Cukic, B. (2005). Linear randomized voting algorithm for fault tolerant sensor fusion and the corresponding reliability model. In IEEE international symposium on systems engineering (pp. 153–162).

  7. 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.

  8. Moreira, L. (2006). Ft-cowisenets: A fault tolerance framework for wireless sensor networks. In 2007 international conference on sensor technologies and applications (SENSORCOMM 2007) (pp. 289–294).

  9. Harte, S., & Rahman, A. (2005). Fault tolerance in sensor networks using self-diagnosing sensor nodes. In The IEEE international workshop on intelligent environment (pp. 7–12).

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

    Article  Google Scholar 

  11. Ramanathan, N., Chang, K., Kapur, R., Girod, L., Kohler, E., & Estrin, D. (2004). Sympathy: A debugging system for sensor networks. In IEEE international conference on local computer networks.

  12. Rost, S., & Balakrishnan, H. (2006). Memento: A health monitoring system for wireless sensor networks. In SECON.

  13. Li, N., & Hou, J. C. (2004). FLSS: A fault-tolerant topology control algorithm for wireless networks. In Proceedings of the 10th annual international conference on mobile computing and networking (pp. 275–286).

  14. Gupta, G., & Younis, M. (2003). Fault-tolerant clustering of wireless sensor networks. Wireless Communications and Networking, 3, 1579–1584.

    Google Scholar 

  15. Gupta, I., Riordan, D., & Sampalli, S. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In Proceedings of the 3rd annual communication networks and services research conference (pp. 255–260).

  16. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless micro sensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences (Vol. 8, pp. 10).

  17. Staddon, J., Balfanz, D., & Durfee, G. (2002). Efficient tracing of failed nodes in sensor networks. In Proceedings of the 1st ACM international workshop on wireless sensor networks and applications (pp. 122–130).

  18. Frank, C., & Romer, K. (2005). Algorithms for generic role assignment in wireless sensor networks. In Proceedings of the 3rd international conference on embedded networked sensor systems (pp. 230–242).

  19. Levis, P., & Culler, D. (2002). Mate: A tiny virtual machine for sensor networks. In ASPLOS-X: Proceedings of the 10th international conference on architectural support for programming languages and operating systems (pp. 85–95). ACM Press.

  20. Rong, P., & Pedram, M. (2003). Extending the lifetime of a network of battery-powered mobile devices by remote processing: A markovian decision-based approach. In DAC’03: Proceedings of the 40th conference on design automation (pp. 906–911). ACM Press.

  21. Bajaber, F., & Awan, I. (2008). Dynamic/static clustering protocol for wireless sensor network. In Second UKSIM European symposium on computer modeling and simulation, EMS’08 (pp. 524–529).

  22. Karim, L., Nasser, N., & Sheltami, T. (2009). A fault tolerant dynamic clustering protocol of wireless sensor networks. In IEEE communications.

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

  24. Cheraghlou, M. N., & Haghparast, M. (2014). A novel fault-tolerant leach clustering protocol for wireless sensor networks. Journal of Circuits, Systems, and Computers, 23, 1450041.

    Article  Google Scholar 

  25. Ganesa, D., Govindan, R., Shenker, S., & Estring, D. (2001). Highly–resilient, energy-efficient multipath routing in wireless sensor networks. ACM SIGMOBILE Mobile Computing and Communications Reviw, 5(4), 11–25.

    Article  Google Scholar 

  26. Kim, D. Y., & Cho, J. (2009). Active caching: a transmission method to guarantee desired communication reliability in wireless sensor networks. IEEE Communications Letters, 13(6), 378–380.

    Article  Google Scholar 

  27. Akbari, A., Dana, A., Khademzadeh, A., & Beikmahdavi, N. (2011). Fault detection and recovery in wireless sensor network using clustering. International Journal of Wireless and Mobile Networks, 3, 130–138.

    Article  Google Scholar 

  28. Venkatesh, S. (2013). An efficient fault tolerant system using improved clustering in wireless sensor networks. Graduate Research in Engineering and Technology, 1, 2320–6632.

    Google Scholar 

  29. Cheraghlou, M. N., Babaie, S., & Samadi, M. (2012). LRC: Novel fault tolerant local re-clustering protocol for wireless sensor network. Journal of Computing, 4, 2151–9617.

    Google Scholar 

  30. Zheng, J., & Jamalipour, A. (2009). Wireless sensor networks a networks perspective. New York: Wiley.

    Book  Google Scholar 

  31. Rajeswari, K., & Neduncheliyan, S. (2017). Genetic algorithm based fault tolerant clustering in wireless sensor network. IET Communications, 11(12), 1927–1932.

    Article  Google Scholar 

  32. Kaur, T., & Kumar, D. (2018). Particle swarm optimization-based unequal and fault tolerant clustering protocol for wireless sensor networks. IEEE Sensors Journal, 18(11), 4614–4622.

    Article  Google Scholar 

  33. 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.

    Article  Google Scholar 

  34. Kaiwartya, O., Abdullah, A. H., Cao, Y., Lloret, J., Kumar, S., Shah, R. R., et al. (2018). Virtualization in wireless sensor networks: Fault tolerant embedding for internet of things. IEEE Internet Things, 5, 571–580.

    Article  Google Scholar 

  35. Wang, K., Qiu, X., Guo, S., & Qi, F. (2015). Fault tolerance oriented sensors relay monitoring mechanism for overhead transmission line in smart grid. IEEE Sensors Journal, 15(3), 1982–1991.

    Article  Google Scholar 

  36. Sun, Y., Luo, H., & Das, S. K. (2012). A trust-based framework for fault-tolerant data aggregation in wireless multimedia sensor networks. IEEE Transactions on Dependable and Secure Computing, 9(6), 785–797.

    Article  Google Scholar 

  37. Ding, S. X., Zhang, P., Yin, S., & Ding, E. L. (2013). An integrated design framework of fault-tolerant wireless networked control systems for industrial automatic control applications. IEEE Transactions on Industrial Informatics, 9(1), 462–471.

    Article  Google Scholar 

  38. Sahoo, M. N., & Khilar, P. M. (2014). Diagnosis of wireless sensor networks in presence of permanent and intermittent faults. Wireless Personal Communications, 78(2), 1571–1591.

    Article  Google Scholar 

  39. Ludeña-Choez, J., Choquehuanca-Zevallos, J. J., & Mayhua-López, E. (2018). Sensor nodes fault detection for agricultural wireless sensor networks based on NMF. Computers and Electronics in Agriculture. In Press.

  40. Swain, R. R., & Khilar, P. M. (2017). Composite fault diagnosis in wireless sensor networks using neural networks. Wireless Personal Communications, 95(3), 2507–2548.

    Article  Google Scholar 

  41. Yue, Y., Li, J., Fan, H., Qin, Q., Gu, L., & Du, L. (2017). Fault prediction based on the Kernel function for ribbon wireless sensor networks. Wireless Personal Communications, 97(3), 3277–3292.

    Article  Google Scholar 

  42. Lo, C., Lynch, J. P., & Liu, M. (2016). Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks. Mechanical Systems and Signal Processing, 66–67, 470–484.

    Article  Google Scholar 

  43. Cayirci, E., & Rong, C. (2009). Security in wireless ad hoc and sensor networks. New York: Wiley.

    Book  Google Scholar 

  44. Karl, H. (2005). Protocol and architectures for wireless sensor networks. New York: Wiley.

    Book  Google Scholar 

  45. Lai, Y., Chen, H. (2007). Energy-efficient fault tolerant mechanism for clustered wireless sensor networks. In 2007 16th international conference on computer communications and networks. IEEE.

  46. Mishra, S., Jena, L., Chakrabarty, A., & Choudhury, J. (2012). Fault tolerant multi cluster head data aggregation protocol in WSN (FMCDA). International Journal of Technological Exploration and Learning, 1, 32–36.

    Google Scholar 

  47. Nasiriavanaki, M., Xia, J., Wan, H., Bauer, A. Q., Culver, J. P., & Wang, L. V. (2014). Resting-state functional connectivity imaging of the mouse brain using photoacoustic tomography. International Society for Optics and Photonics, 111(1), 21–26.

    Google Scholar 

  48. Singh, A. K., & Purohit, N. (2014). An optimised fuzzy clustering for wireless sensor networks. International Journal of Electronics, 101(8), 1027–1041.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Mehdi Nazari Cheraghlou

  2. Iran Telecommunication Research Center (ITRC), Tehran, Iran

    Ahmad Khadem-Zadeh

  3. Department of Computer Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran

    Majid Haghparast

Authors
  1. Mehdi Nazari Cheraghlou
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  2. Ahmad Khadem-Zadeh
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  3. Majid Haghparast
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Correspondence to Ahmad Khadem-Zadeh.

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Cheraghlou, M.N., Khadem-Zadeh, A. & Haghparast, M. EFT: Novel Fault Tolerant Management Framework for Wireless Sensor Networks. Wireless Pers Commun 109, 981–999 (2019). https://doi.org/10.1007/s11277-019-06600-x

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