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RFDCS: A reactive fault detection and classification scheme for clustered wsns

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Abstract

Wireless sensor networks (WSNs) are resource-constrained, self-organizing systems that can operate in favorable as well as hostile environments. The deployment capabilities of WSNs, especially in inaccessible and hostile environments, have increased the confidence in the use of WSNs manifold. However, data delivery in sensor networks remains intrinsically faulty and unpredictable. Transmission of erroneous data in the network wastes limited precious resources of the network (such as nodes energy, bandwidth, etc.) and reduces the fidelity of the data. Timely identification of faulty nodes is essential to minimize their consequences, enhance data quality, improve decision making, and extend the lifetime of the network. This work proposes a scheme for detecting and classifying permanent, transient, and intermittent faults in clustered WSNs. The scheme is conversation efficient and reactive. The cluster heads silently analyze information obtained from the nodes during the regular operation for identifying any suspicious behavior. This is done by utilizing moving average and correlation in the obtained data. Cluster heads initiate fault identification process only when they find any suspicious behavior. The presented model localizes certain events which attenuate over distance and neutralize the impact of distance on fault detection accuracy. Thus, our fault diagnosis and classification process utilize correlations that exist in data and handle the problem of measurement variations due to the differences in positions of sensor nodes from the event location when events attenuate over distance. NS-2 based simulation is carried out to evaluate and validate the performance of the proposed scheme. The results show a significant improvement in the performance in terms of fault detection accuracy, false alarm rate, false negative rate, fault classification accuracy and communication overhead.

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References

  1. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Networks 38(4):393–422

    Article  Google Scholar 

  2. Estrin D, Girod L, Pottie G, Srivastava M (2001) Instrumenting the world with wireless sensor networks. In Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP’01). 2001 IEEE International Conference on 4:2033–2036

  3. Verdone R, Dardari D, Mazzini G, Conti A (2010) Wireless sensor and actuator networks: technologies, analysis and design. Academic Press

  4. Akyildiz IF, Vuran MC (2010) Wireless sensor networks, vol. 4. John Wiley & Sons

  5. Manjeshwar A, Member S, Zeng Q (2002) An Analytical Model for Information Retrieval in Wireless Sensor Networks Using Enhanced APTEEN Protocol. IEEE Transactions Parallel Distrib Syst 13(12):1290–1302

    Article  Google Scholar 

  6. Teng R, Zhang B (2011) On-demand information retrieval in sensor networks with localised query and energy-balanced data collection. Sensors 11(1):341–361

    Article  Google Scholar 

  7. Jeličić V, Ražov T, Oletić D, Kuri M, Bilas V (2011) MasliNET: a wireless sensor network based environmental monitoring system. In MIPRO, 2011 Proceedings of the 34th International Convention 150–155

  8. Stoianov I, Nachman L, Madden S, Tokmouline T, Csail M (2007) PIPENET: A wireless sensor network for pipeline monitoring. In Information Processing in Sensor Networks, 2007. IPSN 2007. 6th International Symposium 264–273

  9. Naderan M, Dehghan M, Pedram H (2009) Mobile object tracking techniques in wireless sensor networks. In Ultra Modern Telecommunications & Workshops, 2009. ICUMT’09. Int Conf 1–8

  10. Chinrungrueng J, Sununtachaikul U, Triamlumlerd S (2006) A vehicular monitoring system with power-efficient wireless sensor networks. In ITS Telecommunications Proceedings, 2006 6th International Conference 951–954

  11. Ren Q, Li J, Cheng S (2011) Target tracking under uncertainty in wireless sensor networks. In Mobile Adhoc and Sensor Systems (MASS), 2011 IEEE 8th International Conference 430–439

  12. Milenković A, Otto C, Jovanov E (2006) Wireless sensor networks for personal health monitoring: Issues and an implementation. Comput Commun 29(13):2521–2533

    Article  Google Scholar 

  13. Gao J, Wang J, Zhang X (2012) HMRF-based distributed fault detection for wireless sensor networks. In Global Communications Conference (GLOBECOM). IEEE 2012:640–644

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

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

    Article  Google Scholar 

  16. Duarte EP Jr, Ziwich RP, Albini LC (2011) A survey of comparison-based system-level diagnosis. ACM Computing Survey 43:1–56

    Article  Google Scholar 

  17. Malek M (1980) A comparison connection assignment for diagnosis of multiprocessor systems. ACM 31–36

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

  19. Elhadef M, Boukerche A, Elkadiki H (2006) Diagnosing mobile ad-hoc networks: two distributed comparison-based self-diagnosis protocols. In Proc. 4th ACM international workshop on Mobility management and wireless access. New York, NY, USA: ACM 18–27

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

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

    Article  Google Scholar 

  22. Choi J-Y, Yim S-J, Huh YJ, Choi Y-H (2009) A distributed adaptive scheme for detecting faults in wireless sensor networks. WSEAS Trans Commun 8(2):269–278

    Google Scholar 

  23. Xu X, Geng W, Yang G, Bessis N, Norrington P (2014) LEDFD: A low energy consumption distributed fault detection algorithm for wireless sensor networks. Int J Distrib Sens Networks 2014

  24. Sharma KP, Sharma TP (2017) rDFD: Reactive distributed fault detection in wireless sensor networks. Wireless Netw 23(4):1145–1160

    Article  Google Scholar 

  25. Yang Y, Gao Z, Zhou H, Qiu X (2014) An Uncertainty-Based Distributed Fault Detection Mechanism for Wireless Sensor Networks. Sensors 14(5):7655–7683

    Article  Google Scholar 

  26. Sahoo MN, Khilar PM (2014) Diagnosis of wireless sensor networks in presence of permanent and intermittent faults. Wirel Pers Commun 78(2):1571–1591

    Article  Google Scholar 

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

  28. Guo S, Zhong Z, He T (2009) {FIND}: Faulty node detection for wireless sensor networks. Proc Conf Embed Networked Sens Syst

  29. Jaikaeo C, Srisathapornphat C, Shen C (2001) Diagnosis of sensor networks. IEEE International Conference on Communications 5:1627–1632

    Article  Google Scholar 

  30. Lee WL, Datta A, Cardell-oliver, R. (2006) Winms: Wireless sensor network-management system, an adaptive policy based management for wireless sensor networks. Technical Report, The University of Western Australia

  31. Ruiz LB, Siqueira IG, Oliveira LB, Wong HC, Nogueira JMS, Loureiro AAF (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, MSWiM’04 (pp. 149– 156). ACM

  32. Moustapha AI, Selmic RR (2008) Wireless sensor network modeling using modified recurrent neural networks: application to fault detection. IEEE Trans Instrum Meas 57(5):981–988

    Article  Google Scholar 

  33. Swain RR, Khilar PM, Bhoi SK (2018) Heterogeneous fault diagnosis for wireless sensor networks. Ad Hoc Netw 69:15–37

    Article  Google Scholar 

  34. Panda M, Khilar PM (2015) Distributed self fault diagnosis algorithm for large scale wireless sensor networks using modified three sigma edit test. Ad Hoc Netw 25:170–184

    Article  Google Scholar 

  35. Azar HN, Malazi HT (2018) Decentralized detection of hybrid faults in mobile sensor nodes. Simul Model Pract Theory 87:210–225

    Article  Google Scholar 

  36. Mohapatraa AD, Sahooa MN, Sangaiah AK (2018) Distributed fault diagnosis with dynamic cluster-head and energy efficient dissemination model for smart city. Sustain Cities Soc 43:624–634

    Article  Google Scholar 

  37. Noshad Z, Javaid N, Saba T, Wadud Z, Saleem MQ, Alzahrani ME, Sheta OE (2019) Fault Detection in Wireless Sensor Networks through the Random Forest Classifier. Sensors 19(7):1568

    Article  Google Scholar 

  38. Bae J, Lee M, Shin C (2019) A Data-Based Fault-Detection Model for Wireless Sensor Networks. Sustainability 11:6171

    Article  Google Scholar 

  39. Azharuddin MD, Kuila P, Jana PK (2014) Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks. Comput Electr Eng

  40. Muhammed T, Shaikh RA (2017) An analysis of fault detection strategies in wireless sensor networks. J Netw Comput Appl 78(15):267–287

    Article  Google Scholar 

  41. Waelchli M, Scheidegger M, Braun T (2006) Intensity-based event localization in wireless sensor networks

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Graphic Era Deemed To Be University, Dehradun, India

    Ankur Choudhary & Santosh Kumar

  2. Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India

    Krishna Pal Sharma

Authors
  1. Ankur Choudhary
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  2. Santosh Kumar
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  3. Krishna Pal Sharma
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Correspondence to Santosh Kumar.

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Choudhary, A., Kumar, S. & Sharma, K.P. RFDCS: A reactive fault detection and classification scheme for clustered wsns. Peer-to-Peer Netw. Appl. 15, 1705–1732 (2022). https://doi.org/10.1007/s12083-022-01308-5

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  • DOI: https://doi.org/10.1007/s12083-022-01308-5

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