Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Automated Fault Diagnosis in Wireless Sensor Networks: A Comprehensive Survey

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper covers the basics of fault diagnosis in wireless sensor networks, as well as fault diagnosis terminology, sensor fault classification, causes and effects, and fault diagnosis performance metrics. In recent years, it has been observed that a large variety of fault diagnosis techniques have been proposed by researchers. The existing fault diagnosis methods for sensor networks can be divided into three categories: centralised, distributed, and hybrid approach. This paper gives a detailed review of state-of-the-art wireless sensor network fault diagnosis approaches. It specifically discusses some existing automated fault detection and diagnosis approaches in wireless sensor networks, as well as their benefits and drawbacks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

Not applicable.

Code Availability

Not applicable.

References

  1. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Google Scholar 

  2. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Google Scholar 

  3. Perillo, M., Cheng, Z., & Heinzelman, W. (2005) “An analysis of strategies for mitigating the sensor network hot spot problem,” In The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, MobiQuitous. IEEE, pp. 474–478.

  4. Luo, H., Wu, K., Guo, Z., Gu, L., & Ni, L. M. (2012). Ship detection with wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 23(7), 1336–1343.

    Google Scholar 

  5. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422.

    Google Scholar 

  6. Avizienis, A., Laprie, J.-C., Randell, B., & Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on Dependable and Secure Computing, 1(1), 11–33.

    Google Scholar 

  7. Ni, K., Ramanathan, N., Chehade, M. N. H., Balzano, L., Nair, S., Zahedi, S., Kohler, E., Pottie, G., Hansen, M., & Srivastava, M. (2009). Sensor network data fault types. ACM Transactions on Sensor Networks (TOSN), 5(3), 25.

    Google Scholar 

  8. Barooah, P., Chenji, H., Stoleru, R., & Kalmar-Nagy, T. (2012). Cut detection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 23(3), 483–490.

    Google Scholar 

  9. Chessa, S., & Santi, P. (2002). Crash faults identification in wireless sensor networks. Computer Communications, 25(14), 1273–1282.

    Google Scholar 

  10. Panda, M., & Khilar, P. M. (2012)“Distributed soft fault detection algorithm in wireless sensor networks using statistical test,” In 2nd IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, pp. 195–198.

  11. Bondavalli, A., Chiaradonna, S., Di Giandomenico, F., & Grandoni, F. (2000). Threshold-based mechanisms to discriminate transient from intermittent faults. IEEE Transactions on Computers, 49(3), 230–245.

    Google Scholar 

  12. Mahapatro, A., & Khilar, P. M. (2013). Fault diagnosis in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 15(4), 2000–2026.

    Google Scholar 

  13. Chessa, S. (1999)“Self-diagnosis of grid-interconnected systems, with application to self-test of VLSI wafers,” Ph.D. dissertation, Citeseer.

  14. Jalote, P., & Jalote, P. (1994). Fault tolerance in distributed systems. New Jersey: PTR Prentice Hall Englewood Cliffs.

    MATH  Google Scholar 

  15. Hajiyev, C., & Caliskan, F. (2013). Fault diagnosis and reconfiguration in flight control systems. Springer Science & Business Media, 2, 1.

    MATH  Google Scholar 

  16. Elhadef, M., Boukerche, A., & Elkadiki, H. (2008). A distributed fault identification protocol for wireless and mobile ad hoc networks. Journal of Parallel and Distributed Computing, 68(3), 321–335.

    MATH  Google Scholar 

  17. Siewiorek,D., & Swarz, R. (2017).Reliable Computer Systems: Design and Evaluatuion. Digital Press.

  18. Barborak, M., Dahbura, A., & Malek, M. (1993). The consensus problem in fault-tolerant computing. ACM Computing Surveys (CSur), 25(2), 171–220.

    Google Scholar 

  19. Panda, M. (2015).“Distributed self fault diagnosis in wireless sensor networks using statistical methods,” Ph.D. dissertation.

  20. You, Z., Zhao, X., Wan, H., Hung, W. N., Wang, Y., & Gu, M. (2011). A novel fault diagnosis mechanism for wireless sensor networks. Mathematical and Computer Modelling, 54(1–2), 330–343.

    MATH  Google Scholar 

  21. Mahapatro, A. (2012).“Fault diagnosis algorithms for wireless sensor networks,” Ph.D. dissertation.

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

    Google Scholar 

  23. Gobriel, S., Khattab, S., Mossé, D., Brustoloni, J., & Melhem, R. (2006).“Ridesharing: Fault tolerant aggregation in sensor networks using corrective actions,” In 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, SECON’06., vol. 2. IEEE, pp. 595–604.

  24. Zhao, J., & Govindan, R. (2003).“Understanding packet delivery performance in dense wireless sensor networks,” In Proceedings of the 1st International Conference on Embedded Networked Sensor Systems. ACM, pp. 1–13.

  25. Fok, C.-L., Roman, G.-C., & Lu, C. (2005). “Rapid development and flexible deployment of adaptive wireless sensor network applications,” In Proceedings. 25th IEEE International Conference on Distributed Computing Systems (ICDCS). IEEE, pp. 653–662.

  26. Szewczyk, R., Polastre, J., Mainwaring, A., & Culler, D. (2004).“Lessons from a sensor network expedition,” In European Workshop on Wireless Sensor Networks. Springer, pp. 307–322.

  27. Szewczyk, R., Mainwaring, A., Polastre, J., Anderson, J., & Culler, D. (2004).“An analysis of a large scale habitat monitoring application,” In Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems. ACM, pp. 214–226.

  28. Ramanathan, N., Schoellhammer, T., Estrin,D., Hansen, M., Harmon, T., Kohler, E., & Srivastava, M. (2006).“The final frontier: Embedding networked sensors in the soil” .

  29. Li, H., Price, M. C., Stott, J., & Marshall, I. W. (2007).“The development of a wireless sensor network sensing node utilising adaptive self-diagnostics,” In International Workshop on Self-Organizing Systems. Springer, pp. 30–43.

  30. Elnahrawy, E., & Nath, B. (2003).“Cleaning and querying noisy sensors,” In Proceedings of the 2nd ACM International Conference on Wireless Sensor Networks and Applications. ACM, pp. 78–87.

  31. Ramanathan, N., Balzano, L., Burt, M., Estrin, D., Harmon, T., Harvey, C., Jay, J., Kohler, E., Rothenberg, S., & Srivastava, M. (2006). “Rapid deployment with confidence: Calibration and fault detection in environmental sensor networks” .

  32. Panda, M., & Khilar, P. M. (2015). Distributed self fault diagnosis algorithm for large scale wireless sensor networks using modified three sigma edit test. Ad Hoc Networks, 25, 170–184.

    Google Scholar 

  33. Lee, M.-H., & Choi, Y.-H. (2008). Fault detection of wireless sensor networks. Computer Communications, 31(14), 3469–3475.

    Google Scholar 

  34. Malek, M. (1980).“A comparison connection assignment for diagnosis of multiprocessor systems,” In Proceedings of the 7th Annual Symposium on Computer Architecture. ACM, pp. 31–36.

  35. Blough, D. M., & Brown, H. W. (1999). The broadcast comparison model for on-line fault diagnosis in multicomputer systems: theory and implementation. IEEE Transactions on Computers, 48(5), 470–493.

    MathSciNet  MATH  Google Scholar 

  36. Yang, X., Megson, G. M., & Evans, D. J. (2005). A comparison-based diagnosis algorithm tailored for crossed cube multiprocessor systems. Microprocessors and Microsystems, 29(4), 169–175.

    Google Scholar 

  37. Yang, X., & Tang, Y. Y. (2007). Efficient fault identification of diagnosable systems under the comparison model. IEEE Transactions on Computers, 56(12), 1612–1618.

    MathSciNet  MATH  Google Scholar 

  38. Hsieh, S.-Y., & Chen, Y.-S. (2008). Strongly diagnosable product networks under the comparison diagnosis model. IEEE Transactions on Computers, 57(6), 721–732.

    MathSciNet  MATH  Google Scholar 

  39. Chang, G.-Y. (2010). (t, k)-diagnosability for regular networks. IEEE Transactions on Computers, 59(9), 1153–1157.

    MathSciNet  MATH  Google Scholar 

  40. Chang, G.-Y., Chen, G.-H., & Chang, G. J. (2007). (t, k)-diagnosis for matching composition networks under the mm* model. IEEE Transactions on Computers, 56, 1.

    MathSciNet  Google Scholar 

  41. Tsai, C.-H. (2013). A quick pessimistic diagnosis algorithm for hypercube-like multiprocessor systems under the pmc model. IEEE Transactions on Computers, 62(2), 259–267.

    MathSciNet  MATH  Google Scholar 

  42. Chang, G.-Y., et al. (2011). Conditional (\(\{\)t\(\}\), k)-diagnosis under the pmc model. IEEE Transactions on Parallel and Distributed Systems, 22(11), 1797–1803.

    Google Scholar 

  43. Chessa, S., & Santi, P. (2001).“Comparison-based system-level fault diagnosis in ad hoc networks,” In Proceedings. 20th IEEE Symposium on Reliable Distributed Systems. IEEE, pp. 257–266.

  44. Duarte, E. P., Jr., Weber, A., & Fonseca, K. V. (2012). Distributed diagnosis of dynamic events in partitionable arbitrary topology networks. IEEE Transactions on Parallel and Distributed Systems, 23(8), 1415–1426.

    Google Scholar 

  45. Arampatzis, T., Lygeros, J., & Manesis, S. (2005).“A survey of applications of wireless sensors and wireless sensor networks,” In Proceedings of the IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control. IEEE, pp. 719–724.

  46. Liu, G., Wang, Z., & Jiang, T. (2016). Qos-aware throughput maximization in wireless powered underground sensor networks. IEEE Transactions on Communications, 64(11), 4776–4789.

    Google Scholar 

  47. 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.

    Google Scholar 

  48. Jiang, P. (2009). A new method for node fault detection in wireless sensor networks. Sensors, 9(2), 1282–1294.

    Google Scholar 

  49. Chen, J., Kher, S., & Somani, A. (2006). “Distributed fault detection of wireless sensor networks,” In Proceedings of the Workshop on Dependability Issues in Wireless Ad Hoc Networks and Sensor Networks. ACM, pp. 65–72.

  50. Xu, X., Chen, W., Wan, J., & Yu, R. (2008).“Distributed fault diagnosis of wireless sensor networks,” in 11th IEEE International Conference on Communication Technology (ICCT). IEEE, pp. 148–151.

  51. 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.

    Google Scholar 

  52. Swain, R. R., Dash, T., & Khilar, P. M. (2017). An effective graph-theoretic approach towards simultaneous detection of fault (s) and cut (s) in wireless sensor networks. International Journal of Communication Systems, 30(13), e3273.

    Google Scholar 

  53. Panda, M., & Khilar, P. M. (2015). Distributed byzantine fault detection technique in wireless sensor networks based on hypothesis testing. Computers & Electrical Engineering, 48, 270–285.

    Google Scholar 

  54. Zhang, Z., Shu, L., Mehmood, A., Yan, L., & Zhang, Y. (2016).“A short survey on fault diagnosis in wireless sensor networks,” in International Wireless Internet Conference. Springer, pp. 21–26.

  55. Arbaugh, W. A., Shankar, N., Wan, Y. J., & Zhang, K. (2002). Your 80211 wireless network has no clothes. IEEE Wireless Communications, 9(6), 44–51.

    Google Scholar 

  56. Yu, M., Mokhtar, H., & Merabti, M. (2007). Fault management in wireless sensor networks. IEEE Wireless Communications, 14(6), 1284–1536.

    Google Scholar 

  57. Muhammed, T., & Shaikh, R. A. (2017). An analysis of fault detection strategies in wireless sensor networks. Journal of Network and Computer Applications, 78, 267–287.

    Google Scholar 

  58. Zhang, Z., Mehmood, A., Shu, L., Huo, Z., Zhang, Y., & Mukherjee, M. (2018).“A survey on fault diagnosis in wireless sensor networks,” IEEE Access, vol. 6, pp. 11 349–11 364.

  59. Preparata, F. P., Metze, G., & Chien, R. T. (1967). On the connection assignment problem of diagnosable systems. IEEE Transactions on Electronic Computers, 6, 848–854.

    MATH  Google Scholar 

  60. Wang, H., Agoulmine, N., Ma, M., & Jin, Y. (2010)“Network lifetime optimization in wireless sensor networks,” IEEE Journal on Selected Areas in Communications, vol. 28, no. 7.

  61. Xu, J., & Lilien, L. (1987).“A survey of methods for system-level fault diagnosis,” in Proceedings of the Fall Joint Computer Conference on Exploring technology: today and tomorrow. IEEE Computer Society Press, pp. 534–540.

  62. Somani, A. K. (1997)“System level diagnosis: A review,” Technique Report, Dependable Computer Laboratory, Iowa State University.

  63. Barsi, F., Grandoni, F., & Maestrini, P. (1976). A theory of diagnosability of digital systems. IEEE Transactions on Computers, 6, 585–593.

    MathSciNet  MATH  Google Scholar 

  64. Hakimi, S. L., & Amin, A. (1974). Characterization of connection assignment of diagnosable systems. IEEE Transactions on Computers, 100(1), 86–88.

    MathSciNet  MATH  Google Scholar 

  65. Kuhl, J. G., & Reddy, S. M. (1980)“Distributed fault-tolerance for large multiprocessor systems,” In Proceedings of the 7th Annual Symposium on Computer Architecture. ACM, pp. 23–30.

  66. Somani, A. K., Agarwal, V. K., & Avis, D. (1987). A generalized theory for system level diagnosis. IEEE Transactions on Computers, 5, 538–546.

    MATH  Google Scholar 

  67. Duarte, E. P., Jr., Ziwich, R. P., & Albini, L. C. (2011). A survey of comparison-based system-level diagnosis. ACM Computing Surveys (CSUR), 43(3), 22.

    MATH  Google Scholar 

  68. Chwa, K.-Y., & Hakimi, S. L. (1981). Schemes for fault-tolerant computing: A comparison of modularly redundant and t-diagnosable systems. Information and Control, 49(3), 212–238.

    MathSciNet  MATH  Google Scholar 

  69. Dahbura, A. T., & Masson, G. M. (1984). An 0 (n 2.5) fault identification algorithm for diagnosable systems. IEEE Transactions on Computers, 6, 486–492.

    MATH  Google Scholar 

  70. Rangarajan, S., Fussell, D., & Malek, M. (1990). Built-in testing of integrated circuit wafers. IEEE Transactions on Computers, 2, 195–205.

    Google Scholar 

  71. Blough, D. M., & Pelc, A. (1992). Complexity of fault diagnosis in comparison models. IEEE Transactions on Computers, 41(3), 318–324.

    Google Scholar 

  72. Chen, Y., Bucken, W., & Echtle, K. (1993). Efficient algorithms for system diagnosis with both processor and comparator faults. IEEE Transactions on Parallel and Distributed Systems, 4(4), 371–381.

    Google Scholar 

  73. Fuhrman, C. P., & Nussbaumer, H. J. (1996)“Comparison diagnosis in large multiprocessor systems,” In Proceedings of the Fifth Asian Test Symposium. IEEE, pp. 244–249.

  74. Gao, J.-L., Xu, Y.-J., & Li, X.-W. (2007). Weighted-median based distributed fault detection for wireless sensor networks. Ruan Jian Xue Bao (Journal of Software), 18(5), 1208–1217.

    MATH  Google Scholar 

  75. Choi, J.-Y., Yim, S.-J., Huh, Y. J., & Choi, Y.-H. (2009). A distributed adaptive scheme for detecting faults in wireless sensor networks. WSEAS Transactions on Communications, 8(2), 269–278.

    Google Scholar 

  76. Miao, X., Liu, K., He, Y., Papadias, D., Ma, Q., & Liu, Y. (2013). Agnostic diagnosis: Discovering silent failures in wireless sensor networks. IEEE Transactions on Wireless Communications, 12(12), 6067–6075.

    Google Scholar 

  77. Panda, R. R., Gouda, B. S., & Panigrahi, T. (2014).“Efficient fault node detection algorithm for wireless sensor networks,” In International Conference on High Performance Computing and Applications (ICHPCA). IEEE, pp. 1–5.

  78. Maronna, R., Martin, D., & Yohai, V. (2006).“Robust statistics (pp. 978-0)” .

  79. Jin, X., Chow, T. W., Sun, Y., Shan, J., & Lau, B. C. (2015). Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis. Wireless Networks, 21(3), 829–839.

    Google Scholar 

  80. Team, R. C. (2014)“R: A language and environment for statistical computing. r foundation for statistical computing, vienna, austria” .

  81. Kar, C., & Mohanty, A. (2004). Application of ks test in ball bearing fault diagnosis. Journal of Sound and Vibration, 1(269), 439–454.

    Google Scholar 

  82. Anděl, J. (1976)“Autoregressive series with random parameters,” Statistics: A Journal of Theoretical and Applied Statistics, vol. 7, no. 5, pp. 735–741.

  83. Lau, B. C., Ma, E. W., & Chow, T. W. (2014). Probabilistic fault detector for wireless sensor network. Expert Systems with Applications, 41(8), 3703–3711.

    Google Scholar 

  84. Gong, W., Liu, K., & Liu, Y. (2015). Directional diagnosis for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(5), 1290–1300.

    Google Scholar 

  85. Tang, P., & Chow, T. W. (2016). Wireless sensor-networks conditions monitoring and fault diagnosis using neighborhood hidden conditional random field. IEEE Transactions on Industrial Informatics, 12(3), 933–940.

    Google Scholar 

  86. Dhal, R., Torres, J. A., & Roy, S. (2015). Detecting link failures in complex network processes using remote monitoring. Physica A: Statistical Mechanics and its Applications, 437, 36–54.

    Google Scholar 

  87. Torres, J. A., Dhal, R., & Roy, S. (2015).“Detecting link failures in complex network processes using remote monitoring,” In American Control Conference (ACC). IEEE, pp. 189–194.

  88. Kamal, A. R. M., Bleakley, C. J., & Dobson, S. (2014). Failure detection in wireless sensor networks: A sequence-based dynamic approach. ACM Transactions on Sensor Networks (TOSN), 10(2), 35.

    Google Scholar 

  89. Guo, S., Zhong, Z., & He, T. (2009).“Find: faulty node detection for wireless sensor networks,” in Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems. ACM, pp. 253–266.

  90. Guo, S., Zhang, H., Zhong, Z., Chen, J., Cao, Q., & He, T. (2014). Detecting faulty nodes with data errors for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 10(3), 40.

    Google Scholar 

  91. Warriach, E. U., & Tei, K. (2013).“Fault detection in wireless sensor networks: A machine learning approach,” in IEEE 16th International Conference on Computational Science and Engineering (CSE). IEEE, pp. 758–765.

  92. Rabiner, L. R., & Juang, B.-H. (1986). An introduction to hidden markov models. IEEE ASSP Magazine, 3(1), 4–16.

    Google Scholar 

  93. Yang, Y., Su, L., Khan, M., Lemay, M., Abdelzaher, T., & Han, J. (2015). Power-based diagnosis of node silence in remote high-end sensing systems. ACM Transactions on Sensor Networks (TOSN), 11(2), 33.

    Google Scholar 

  94. Abid, A., Kachouri, A., Guiloufi, A. B. F., Mahfoudhi, A., Nasri, N., & Abid, M. (2015) “Centralized knn anomaly detector for wsn,” in 12th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, pp. 1–4.

  95. Yu, C.-B.., Hu, J.-J., Li, R., Deng, S.-H., & Yang, R.-M. (2014).“Node fault diagnosis in wsn based on rs and svm,” in International Conference on Wireless Communication and Sensor Network (WCSN). IEEE, pp. 153–156.

  96. Chanak, P., Banerjee, I., Samanta, T., & Rahaman, H. (2012).“Ffms: Fuzzy based fault management scheme in wireless sensor networks,” In Eco-friendly Computing and Communication Systems. Springer, pp. 30–38.

  97. Febriansyah, I. I., Saputro, W. C., Achmadi, G. R., Arisha, F., Tursina, D., Pratomo, B. A., & Shiddiqi, A. M. (2021).“Outlier detection and decision tree for wireless sensor network fault diagnosis,” In 2021 13th International Conference on Information & Communication Technology and System (ICTS). IEEE, pp. 56–61.

  98. Oßner, C., Buchmann, E., & Böhm, K. (2016). Identifying defective nodes in wireless sensor networks. Distributed and Parallel Databases, 34(4), 591–610.

    Google Scholar 

  99. 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. ACM, pp. 122–130.

  100. Koushanfar, F., Potkonjak, M., & Sangiovanni-Vincentelli, A. (2003). “On-line fault detection of sensor measurements,” In Proceedings of IEEE Sensors, vol. 2. IEEE, pp. 974–979.

  101. Ruiz, L. B., Siqueira, I. G., Wong, H. C., Nogueira, J. M. S., Loureiro, A. A. et al., (2004). “Fault management in event-driven wireless sensor networks,” in Proceedings of the 7th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems. ACM, pp. 149–156.

  102. Ramanathan, N., Chang, K., Kapur, R., Girod, L., Kohler, E., & Estrin, D. (2005) “Sympathy for the sensor network debugger,” in Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems. ACM, pp. 255–267.

  103. Lee, W. L., Datta, A., & Cardell-Oliver, R. (2006). “Winms: Wireless sensor network-management system, an adaptive policy-based management for wireless sensor networks” .

  104. Perrig, A., Szewczyk, R., Tygar, J. D., Wen, V., & Culler, D. E. (2002). Spins: Security protocols for sensor networks. Wireless Networks, 8(5), 521–534.

    MATH  Google Scholar 

  105. Ssu, K.-F., Chou, C.-H., Jiau, H. C., & Hu, W.-T. (2006). Detection and diagnosis of data inconsistency failures in wireless sensor networks. Computer Networks, 50(9), 1247–1260.

    MATH  Google Scholar 

  106. Krunic, V., Trumpler, E., & Han, R. (2007). “Nodemd: Diagnosing node-level faults in remote wireless sensor systems,” In Proceedings of the 5th International Conference on Mobile Systems, Applications and Services.ACM, pp. 43–56.

  107. Liu, Y., Liu, K., & Li, M. (2010). Passive diagnosis for wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 18(4), 1132–1144.

    Google Scholar 

  108. Feng, Z., Fu, J. Q., & Wang, Y. (2014).“Weighted distributed fault detection for wireless sensor networks based on the distance,” In 33rd Chinese Control Conference (CCC). IEEE, pp. 322–326.

  109. Senapati, B. R., Khilar, P. M., & Swain, R. R. (2021). Composite fault diagnosis methodology for urban vehicular ad hoc network. Vehicular Communications, 29, 100337.

    Google Scholar 

  110. Ji, S., Yuan, S.-f., Ma, T.-h., & Tan, C. (2010). “Distributed fault detection for wireless sensor based on weighted average,” In Second International Conference on Networks Security, Wireless Communications and Trusted Computing. IEEE, pp. 57–60.

  111. Ding, M., Chen, D., Xing, K., & Cheng, X. (2005). “Localized fault-tolerant event boundary detection in sensor networks,” In Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies. INFOCOM, 2, 902–913.

    Google Scholar 

  112. Saihi, M., Boussaid, B., Zouinkhi, A., & Abdelkrim, M. N. (2013).“Decentralized fault detection in wireless sensor network based on function error,” In 10th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, pp. 1–5.

  113. Swain, R. R., Khilar, P. M., & Dash, T. (2018). Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance. International Journal of Communication Systems, 31(14), e3769.

    Google Scholar 

  114. Swain, R. R., Khilar, P. M., & Bhoi, S. K. (2020). Underlying and persistence fault diagnosis in wireless sensor networks using majority neighbors co-ordination approach. Wireless Personal Communications, 111(2), 763–798.

    Google Scholar 

  115. Vuran, M. C., Akan, Ö. B., & Akyildiz, I. F. (2004). Spatio-temporal correlation: theory and applications for wireless sensor networks. Computer Networks, 45(3), 245–259.

    MATH  Google Scholar 

  116. Hsin, C., & Liu, M. (2006). Self-monitoring of wireless sensor networks. Computer Communications, 29(4), 462–476.

    Google Scholar 

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

    Google Scholar 

  118. Yim, S.-J., & Choi, Y.-H. (2010). An adaptive fault-tolerant event detection scheme for wireless sensor networks. Sensors, 10(3), 2332–2347.

    Google Scholar 

  119. Xiao, X.-Y., Peng, W.-C., Hung, C.-C., & Lee, W.-C. (2007). “Using sensorranks for in-network detection of faulty readings in wireless sensor networks,” In Proceedings of the 6th ACM International Workshop on Data Engineering for Wireless and Mobile Access. ACM, pp. 1–8.

  120. Saha, T., & Mahapatra, S. (2011). “Distributed fault diagnosis in wireless sensor networks,” In International Conference on Process Automation, Control and Computing (PACC). IEEE, pp. 1–5.

  121. Xu, X., Geng, W., Yang, G., Bessis, N., & Norrington, P. (2014). Ledfd: A low energy consumption distributed fault detection algorithm for wireless sensor networks. International Journal of Distributed Sensor Networks, 10(2), 1–10.

    Google Scholar 

  122. 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, 470–484.

    Google Scholar 

  123. Sharma, K. P., & Sharma, T. P. (2017). rdfd: reactive distributed fault detection in wireless sensor networks. Wireless Networks, 23(4), 1145–1160.

    Google Scholar 

  124. H. Yuan, X. Zhao, L. Yu et al., “A distributed bayesian algorithm for data fault detection in wireless sensor networks,” in International Conference on Information Networking (ICOIN). IEEE, 2015, pp. 63–68.

  125. M. Zhao, Z. Tian, and T. W. Chow, “Fault diagnosis on wireless sensor network using the neighborhood kernel density estimation,” Neural Computing and Applications, pp. 1–12, 2018.

  126. Obst, O. (2014). Distributed fault detection in sensor networks using a recurrent neural network. Neural Processing Letters, 40(3), 261–273.

    Google Scholar 

  127. Obst, O. (2009). “Distributed fault detection using a recurrent neural network,” In Proceedings of the International Conference on Information Processing in Sensor Networks. IEEE Computer Society, pp. 373–374.

  128. Mahapatro, A., & Khilar, P. M. (2013). Detection and diagnosis of node failure in wireless sensor networks: A multiobjective optimization approach. Swarm and Evolutionary Computation, 13, 74–84.

    Google Scholar 

  129. Mahapatro, A., & Panda, A. K. (2014). Choice of detection parameters on fault detection in wireless sensor networks: A multiobjective optimization approach. Wireless Personal Communications, 78(1), 649–669.

    Google Scholar 

  130. Ghorbel, O., Jmal, M. W., Abid, M., & Snoussi, H. (2015).“Distributed and efficient one-class outliers detection classifier in wireless sensors networks,” In International Conference on Wired/Wireless Internet Communication. Springer, pp. 259–273.

  131. Gao, Y., Xiao, F., Liu, J., & Wang, R. (2018). “Distributed soft fault detection for interval type-2 fuzzy-model-based stochastic systems with wireless sensor networks,” IEEE Transactions on Industrial Informatics.

  132. Chanak, P., & Banerjee, I. (2016). Fuzzy rule-based faulty node classification and management scheme for large scale wireless sensor networks. Expert Systems with Applications, 45, 307–321.

    Google Scholar 

  133. Mohapatra, S., Khilar, P. M., & Swain, R. R. (2019). Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach. International Journal of Communication Systems, 32(16), e4138.

    Google Scholar 

  134. Mohapatra, S., & Khilar, P. M. (2020). Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine. Computational Intelligence, 36(3), 1374–1393.

    MathSciNet  Google Scholar 

  135. Yang, C., Liu, C., Zhang, X., Nepal, S., & Chen, J. (2015). A time efficient approach for detecting errors in big sensor data on cloud. IEEE Transactions on Parallel and Distributed Systems, 26(2), 329–339.

    Google Scholar 

  136. B. R. Senapati, R. R. Swain, and P. M. Khilar, “Hard and soft fault detection using cloud based vanet,” in Intelligent and Cloud Computing. Springer, 2022, pp. 133–143.

  137. I. Banerjee, A. Datta, S. Pal, S. Chatterjee, and T. Samanta, “A novel fault detection and replacement scheme in wsn,” in Recent Advances in Intelligent Informatics. Springer, 2014, pp. 303–310.

  138. Swain, R. R., Dash, T., & Khilar, P. M. (2020). Lightweight approach to automated fault diagnosis in wsns. IET Networks, 9(3), 110–119.

    Google Scholar 

  139. Prasad, R., & Baghel, R. K. (2021). A novel fault diagnosis technique for wireless sensor network using feedforward neural network. IEEE Sensors Letters, 6(1), 1–4.

    Google Scholar 

  140. F. Koushanfar, M. Potkonjak, and A. Sangiovanni-Vincentell, “Fault tolerance techniques for wireless ad hoc sensor networks,” in Proceedings of IEEE Sensors, vol. 2. IEEE, 2002, pp. 1491–1496.

  141. Harte, S., Rahmanl, A., & Razeeb, K. (2005).“Fault tolerance in sensor networks using self-diagnosing sensor nodes,” IEE International Workshop on Intelligent Environments, pp. 7–12.

  142. Wang, N., & Chen, Y.-X. (2013). A fault-event detection model using trust matrix in wsn. Sensors & Transducers, 158(11), 190.

    Google Scholar 

  143. Li, C., & Zhang, Y. (2017). “A novel energy-efficient sensor networks’ fault diagnosis,” In 36th Chinese Control Conference (CCC). IEEE, pp. 8879–8884.

  144. Nitesh, K., & Jana, P. K. (2015). “Dfda: a distributed fault detection algorithm in two tier wireless sensor networks,” In Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Springer, pp. 739–746.

  145. Nitesh, K., & Jana, P. K. (2016). Distributed fault detection and recovery algorithms in two-tier wireless sensor networks. International Journal of Communication Networks and Distributed Systems, 16(3), 281–296.

    Google Scholar 

  146. Afsar, M. M. (2014).“Maximizing the reliability of clustered sensor networks by a fault-tolerant service,” In IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, pp. 1–8.

  147. Zafar, A., Wajid, B., & Akram, B. A. (2015).“A hybrid fault diagnosis architecture for wireless sensor networks,” In International Conference on Open Source Systems & Technologies (ICOSST). IEEE, pp. 7–15.

  148. Mahapatro, A., & Khilar, P. M. (2013). Online distributed fault diagnosis in wireless sensor networks. Wireless Personal Communications, 71(3), 1931–1960.

    Google Scholar 

  149. Gupta, G., & Younis, M. (2003).“Fault-tolerant clustering of wireless sensor networks,” in IEEE Wireless Communications and Networking (WCNC), vol. 3. IEEE, pp. 1579–1584.

  150. Jaikaeo, C., Srisathapornphat, C., & Shen, C.-C. (2001).“Diagnosis of sensor networks,” In IEEE International Conference on Communications (ICC), vol. 5. IEEE, pp. 1627–1632.

  151. Tai, A. T., Tso, K. S., & Sanders, W. H. (2004).“Cluster-based failure detection service for large-scale ad hoc wireless network applications,” In International Conference on Dependable Systems and Networks. IEEE, pp. 805–814.

  152. Younis, O., Fahmy, S., & Santi, P. (2005). An architecture for robust sensor network communications. International Journal of Distributed Sensor Networks, 1(3–4), 305–327.

    Google Scholar 

  153. Wang, P., Zheng, J., & Li, C. (2007). “An agreement-based fault detection mechanism for under water sensor networks,” In IEEE Global Telecommunications Conference, GLOBECOM’07. IEEE, pp. 1195–1200.

  154. Venkataraman, G., Emmanuel, S., & Thambipillai, S. (2008). Energy-efficient cluster-based scheme for failure management in sensor networks. IET Communications, 2(4), 528–537.

    Google Scholar 

  155. Asim, M., Mokhtar, H., & Merabti, M. (2008).“A fault management architecture for wireless sensor network,” In International Wireless Communications and Mobile Computing Conference, IWCMC’08. IEEE, pp. 779–785.

  156. Wang, W., Wang, B., Liu, Z., & Guo, L. (2011). A cluster-based real-time fault diagnosis aggregation algorithm for wireless sensor networks. Information Technology Journal, 10(1), 80–88.

    Google Scholar 

  157. Sakib, K. (2012). Asynchronous failed sensor node detection method for sensor networks. International Journal of Network Management, 22(1), 27–49.

    Google Scholar 

  158. Swain, R. R., Khilar, P. M., & Bhoi, S. K. (2018). Heterogeneous fault diagnosis for wireless sensor networks. Ad Hoc Networks, 69, 15–37.

    Google Scholar 

  159. Swain, R. R., Dash, T., & Khilar, P. M. (2019). A complete diagnosis of faulty sensor modules in a wireless sensor network. Ad Hoc Networks, 93, 101924.

    Google Scholar 

  160. Moridi, E., Haghparast, M., Hosseinzadeh, M., & Jafarali Jassbi, S. (2021).“A novel hierarchical fault management framework for wireless sensor networks: Hfmf,” Peer-to-Peer Networking and Applications, pp. 1–11.

  161. Quoc, D. N., Liu, N., & Guo, D. (2021). “A hybrid fault-tolerant routing based on gaussian network for wireless sensor network,” Journal of Communications and Networks.

  162. Biswas,P., & Samanta, T. (2021).“A method for fault detection in wireless sensor network based on pearson’s correlation coefficient and support vector machine classification,” Wireless Personal Communications, pp. 1–16.

  163. Titouna, C., Aliouat, M., & Gueroui, M. (2015). Outlier detection approach using bayes classifiers in wireless sensor networks. Wireless Personal Communications, 85(3), 1009–1023.

    Google Scholar 

  164. Titouna, C., Aliouat, M., & Gueroui, M. (2016). Fds: fault detection scheme for wireless sensor networks. Wireless Personal Communications, 86(2), 549–562.

    Google Scholar 

  165. Wu, J.-Y., Duh, D.-R., Wang, T.-Y., & Chang, L.-Y. (2007).“Fast and simple on-line sensor fault detection scheme for wireless sensor networks,” In International Conference on Embedded and Ubiquitous Computing. Springer, pp. 444–455.

  166. Kaur, A., & Sharma, T. P. (2010).“Afdep: agreement based ch failure detection and election protocol for a wsn,” In International Conference on Advances in Information and Communication Technologies. Springer, pp. 249–257.

  167. Nguyen, T. A., Bucur, D., Aiello, M., & Tei, K. (2013).“Applying time series analysis and neighbourhood voting in a decentralised approach for fault detection and classification in wsns,” In Proceedings of the Fourth Symposium on Information and Communication Technology. ACM, pp. 234–241.

  168. Huang, D.-W., Liu, W., & Bi, J. (2021). Data tampering attacks diagnosis in dynamic wireless sensor networks. Computer Communications, 172, 84–92.

    Google Scholar 

  169. Khan, S. A., Daachi, B., & Djouani, K. (2012). Application of fuzzy inference systems to detection of faults in wireless sensor networks. Neurocomputing, 94, 111–120.

    Google Scholar 

  170. He, W., Qiao, P.-L., Zhou, Z.-J., Hu, G.-Y., Feng, Z.-C., & Wei, H. (2018). A new belief-rule-based method for fault diagnosis of wireless sensor network. IEEE Access, 6, 9404–9419.

    Google Scholar 

  171. 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.

    Google Scholar 

  172. Jan, S. U., Lee, Y. D., & Koo, I. S. (2021). A distributed sensor-fault detection and diagnosis framework using machine learning. Information Sciences, 547, 777–796.

    MathSciNet  Google Scholar 

  173. Saeed, U., Jan, S. U., Lee, Y.-D., & Koo, I. (2021). Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliability Engineering & System Safety, 205, 107284.

    Google Scholar 

  174. Rajan, M. S. Dilip, G., Kannan, N., Namratha, M., Majji, S., Mohapatra, S. K., Patnala, T. R., & Karanam, S. R. (2021).“Diagnosis of fault node in wireless sensor networks using adaptive neuro-fuzzy inference system,” Applied Nanoscience, pp. 1–9.

  175. Swain, R. R., Khilar, P. M., & Dash, T. (2019). Neural network based automated detection of link failures in wireless sensor networks and extension to a study on the detection of disjoint nodes. Journal of Ambient Intelligence and Humanized Computing, 10(2), 593–610.

    Google Scholar 

  176. Swain, R. R., Khilar, P. M., & Dash, T. (2020). Multifault diagnosis in wsn using a hybrid metaheuristic trained neural network. Digital Communications and Networks, 6(1), 86–100.

    Google Scholar 

  177. Swain, R. R., Khilar, P. M. A., & fuzzy mlp approach for fault diagnosis in wireless sensor networks”, in,. (2016). IEEE region 10 conference (TENCON). IEEE, 2016, 3183–3188.

  178. Swain, R. R., & Khilar, P. M. (2017)“Soft fault diagnosis in wireless sensor networks using pso based classification,” In TENCON 2017-2017 IEEE Region 10 Conference. IEEE, pp. 2456–2461.

  179. Swain, R. R., Dash, T., & Khilar, P. M. (2019).“Investigation of rbf kernelized anfis for fault diagnosis in wireless sensor networks,” In Computational Intelligence: Theories, Applications and Future Directions-Volume II. Springer, pp. 253–264.

  180. Chanak, P., Banerjee, I., & Sherratt, R. S. (2016). Mobile sink based fault diagnosis scheme for wireless sensor networks. Journal of Systems and Software, 119, 45–57.

    Google Scholar 

  181. Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile sink-based adaptive immune energy-efficient clustering protocol for improving the lifetime and stability period of wireless sensor networks. IEEE Sensors Journal, 15(8), 4576–4586.

    Google Scholar 

Download references

Funding

Not Applicable.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, Uttar Pradesh, India

    Rakesh Ranjan Swain

  2. Department of Computer Science and Engineering, Birla Institute of Technology and Science Pilani, K.K. Birla Goa Campus, Sancoale, 403726, Goa, India

    Tirtharaj Dash

  3. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, 769008, Odisha, India

    Pabitra Mohan Khilar

Authors
  1. Rakesh Ranjan Swain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tirtharaj Dash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pabitra Mohan Khilar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rakesh Ranjan Swain.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Swain, R.R., Dash, T. & Khilar, P.M. Automated Fault Diagnosis in Wireless Sensor Networks: A Comprehensive Survey. Wireless Pers Commun 127, 3211–3243 (2022). https://doi.org/10.1007/s11277-022-09916-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09916-3

Keywords

Search

Navigation