Abstract
Fault diagnosis is one of the challenging problems in wireless sensor network (WSN). This paper proposes a fault diagnosis algorithm based on clustering and credibility (FDCC). Firstly, the network is divided into several clusters according to both geographic positions and measurements of sensor nodes for the purpose of improving the accuracy of network diagnostic result. The process of clustering can be divided into five phases: region division, head selection, coarse clustering, coarse cluster merge and cluster adjustment. Then, in order to further improve the accuracy of diagnostic result, a credibility model based on historical diagnostic result and remaining energy is established for each neighbor node. At last, nodes with higher credibility are selected to participate in diagnostic process. Simulation results show that the proposed algorithm can guarantee higher diagnostic accuracy.
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References
Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)
Chen, J., Kher, S., Somani, A.: Distributed fault detection of wireless sensor networks. In: Proceedings of Workshop on Dependability Issues in Wireless Ad Hoc Networks and Sensor Networks, pp. 65–73 (2006)
Gupta, G., Younis, M.: Fault-tolerant clustering of wireless sensor networks. Wirel. Commun. Netw. 3, 1579–1584 (2003)
Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference (2000)
Julie, E.G., Tamilselvi, S., Robinson, Y.H.: Performance analysis of energy efficient virtual back bone path based cluster routing protocol for WSN. Wireless Pers. Commun. 91(3), 1171–1189 (2016)
Krishnamachari, B., Iyengar, S.: Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor network. IEEE Trans. Comput. 53(3), 241–250 (2004)
Lee, M.-H., Choi, Y.-H.: Fault detection of wireless sensor networks. Comput. Commun. 31(14), 3469–3475 (2008)
Lin, C.-R., Liu, K.-H., Chen, M.-S.: Dual clustering: integrating data clustering over optimization and constraint domains. IEEE Trans. Knowl. Data Eng. 17(5), 628–637 (2005)
Liu, K., Ma, Q., Zhao, X., Liu, Y.: Self-diagnosis for large scale wireless sensor networks. In: Proceedings of IEEE International Conference on Computer Communications, pp. 1539–1547 (2011)
Mahapatro, A., Khilar, P.M.: Detection of node failure in wireless image sensor networks. ISRN Sens. Netw. 2012, 8 p. (2012)
Mahapatro, A., Khilar, P.M.: Fault diagnosis in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 15(4), 2000–2026 (2013)
Mahapatro, A., Khilar, P.M.: Online distributed fault diagnosis in wireless sensor networks. Wireless Pers. Commun. 71(3), 1931–1960 (2013)
Shao, S., Guo, S., Qiu, X.: Distributed fault detection based on credibility and cooperation for WSNs in smart grids. Sensors 17(5), 983 (2017)
Teng, Y.-H., Lin, C.-K.: A test round controllable local diagnosis algorithm under the PMC diagnosis model. Appl. Math. Comput. 244(2), 613–623 (2014)
Venkataraman, G., Thambipillai, S.: Energy-efficient cluster-based scheme for failure management in sensor networks. IET Commun. 2(4), 528–537 (2008)
Wang, L.D., Zhang, X.F., Teng, Y.-H., Lin, C.-K.: Parallel and local diagnostic algorithm for wireless sensor networks. In: Proceedings of Asia-Pacific Network Operations and Management Symposium, pp. 334–347 (2017)
Wang, W., Wang, B., Liu, Z.: A cluster-based real-time fault diagnosis aggregation algorithm for wireless sensor networks. Inf. Technol. J. 10(1), 80–88 (2011)
Wang, A., Heinzelman, W.B., Sinha, A., Chandrakasan, A.P.: Energy-scalable protocols for battery-operated microSensor networks. J. VLSI Signal Process. Syst. Signal Image Video Technol. 29(3), 223–237 (2001)
Wei, L.-Y., Peng, W.-C.: Clustering spatial data with a geographic constraint: exploring local search. Knowl. Inf. Syst. 31(1), 153–170 (2012)
Xiao, X.-Y., Peng, W.-C., Hung, C.-C., Lee, W.-C.: Using sensor ranks for in-network detection of faulty readings in wireless sensor networks. In: Proceedings of 6th ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 1–8 (2007)
Yao, Y., Yu, Z., Wang, G.: Clustering routing algorithm of self-energized wireless sensor networks based on solar energy harvesting. J. China Univ. Posts Telecommun. 22(4), 66–73 (2015)
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Wang, L., Xu, X., Zhang, X., Lin, CK., Tseng, YC. (2018). Fault Diagnosis Algorithm for WSN Based on Clustering and Credibility. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11335. Springer, Cham. https://doi.org/10.1007/978-3-030-05054-2_11
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DOI: https://doi.org/10.1007/978-3-030-05054-2_11
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