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Fault tolerant data transmission reduction method for wireless sensor networks

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Abstract

Several theoretical studies have clearly demonstrated that the Dual Prediction Mechanism (DPM) remains the most efficient technique for data reduction in Wireless Sensor Networks (WSNs). In real world, the deployed sensor nodes suffers from packet loss and even failures which renders the DPM unreliable, since it requires flawless synchronization between the source (sensor node) and the destination (Sink). In this paper, we introduce a Fault Tolerant Data Transmission Reduction (FTDTR) technique consisting of three main components: DPM-based transmission reduction, synchronization and packet loss detection, and finally reconstruction of missing data. Our method was evaluated on real-world data sets collected at our laboratory and compared to three recent prediction-based data reduction approaches. The results were promising in quality of the replicated measurements and transmission reduction.

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References

  1. Matlab simulator. https://github.com/BouTayehGaby/Matlab-Simulator---Fault-Tolerant-Data-Transmission-Reduction

  2. Aderohunmu, F.A., Paci, G., Brunelli, D., Deng, J.D., Benini, L., Purvis, M.: An Application-Specific Forecasting Algorithm for Extending Wsn Lifetime. In: 2013 IEEE International Conference on Distributed Computing in Sensor Systems, pp. 374–381 (2013)

  3. Alsheikh, M.A., Lin, S., Niyato, D., Tan, H.P.: Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun. Surv. Tutorial. 16(4), 1996–2018 (2014)

    Article  Google Scholar 

  4. Alves, M.M., Pirmez, L., Rossetto, S., Delicato, F.C., de Farias, C.M., Pires, P.F., dos Santos, I.L., Zomaya, A.Y.: Damage prediction for wind turbines using wireless sensor and actuator networks. J. Netw. Comput. Appl. 80, 123–140 (2017)

    Article  Google Scholar 

  5. Askari Moghadam, R., Keshmirpour Mehrnaz, e.M.A., Sahibuddin, S., Ahmad, R., Mohd Daud, S., El-Qawasmeh, E.: Hybrid ARIMA and Neural Network Model for Measurement Estimation in Energy-Efficient Wireless Sensor Networks, pp. 35–48. Springer, Berlin (2011)

    Chapter  Google Scholar 

  6. Basheer, A., Sha, K.: Cluster-based quality-aware adaptive data compression for streaming data. J. Data Inf. Qual. 9(1), 2:1–2:33 (2017)

    Google Scholar 

  7. Bhuiyan, M.Z.A., Wu, J., Wang, G., Wang, T., Hassan, M.M.: e-sampling: Event-sensitive autonomous adaptive sensing and low-cost monitoring in networked sensing systems. ACM Trans. Auton. Adapt. Syst. 12(1), 1:1–1:29 (2017). https://doi.org/10.1145/2994150

    Article  Google Scholar 

  8. Du, T., Qu, Z., Guo, Q., Qu, S.: A high efficient and real time data aggregation scheme for wsns. Int. J. Distrib. Sens. Netw. 11(6), 261381 (2015)

    Article  Google Scholar 

  9. Gao, Z., Cheng, W., Qiu, X., Meng, L.: A missing sensor data estimation algorithm based on temporal and spatial correlation. Int. J. Distrib. Sen. Netw., pp. 178:178–178:178 (2016)

  10. Gruenwald, L., Yang, H., Sadik, M.S., Shukla, R.: Using data mining to handle missing data in multi-hop sensor network applications. Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 9–16 (2010)

  11. Guestrin, C., Bodik, P., Thibaux, R., Paskin, M., Madden, S.: Distributed Regression: an Efficient Framework for Modeling Sensor Network Data. In: Third International Symposium on Information Processing in Sensor Networks, pp. 1–10 (2004)

  12. Halgamuge, M.N., Zukerman, M., Ramamohanarao, K., Vu, H.L.: An estimation of sensor energy consumption. Progress Electromagn. Res. 12, 259–295 (2009)

    Article  Google Scholar 

  13. Harb, H., Makhoul, A.: Energy-efficient sensor data collection approach for industrial process monitoring. IEEE Trans. Indust. Inf. 14(2), 661–672 (2018)

    Article  Google Scholar 

  14. Lemos, M., Rabêlo, R., de Carvalho, C., Mendes, D., Costa, V., et al.: An energy-efficient approach to enhance virtual sensors provisioning in sensor clouds environments. Sensors 18(3), 689 (2018)

    Article  Google Scholar 

  15. Li, G., Wang, Y.: Automatic arima modeling-based data aggregation scheme in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking (1), 85. https://doi.org/10.1186/1687-1499-2013-85 (2013)

  16. Li, J., McCann, J., Pollard, N., Faloutsos, C.: Dynammo: Mining and summarization of coevolving sequences with missing values. ACM SIGKDD, pp. 527–534. (CMU-RI-TR-) (2009)

  17. Liu, X., Liu, Y., Xie, Q., Li, L., Li, Z.: A potential-based clustering method with hierarchical optimization. World Wide Web 21(6), 1617–1635 (2018)

    Article  Google Scholar 

  18. Monteiro, L.C., Delicato, F.C., Pirmez, L., Pires, P.F., Miceli, C.: Dpcas: Data Prediction with Cubic Adaptive Sampling for Wireless Sensor Networks. In: Au, M. H. A., Castiglione, A., Choo, K. K. R., Palmieri, F., Li, K. C. (eds.) Green, Pervasive, and Cloud Computing, pp 353–368. Springer International Publishing, Cham (2017)

    Chapter  Google Scholar 

  19. Neto, A.R., Soares, B., Barbalho, F., Santos, L., Batista, T., Delicato, F.C., Pires, P.F.: Classifying Smart Iot Devices for Running Machine Learning Algorithms. In: 45 Seminário Integrado De Software E Hardware 2018 (SEMISH 2018), vol. 45. SBC, Porto Alegre (2018)

  20. Pan, L., Gao, H., Li, J., Gao, H., Guo, X.: Ciam: an Adaptive 2-In-1 Missing Data Estimation Algorithm in Wireless Sensor Networks. In: 19Th IEEE International Conference on Networks (ICON), pp. 1–6 (2013)

  21. Raza, U., Camerra, A., Murphy, A.L., Palpanas, T., Picco, G.P.: Practical data prediction for real-world wireless sensor networks. IEEE Trans. Knowl. Data Eng. 27(8), 2231–2244 (2015)

    Article  Google Scholar 

  22. Rocha, A.R., Pirmez, L., Delicato, F.C., Rico Lemos, Santos, I., Gomes, D.G., de Souza, J.N.: Wsns clustering based on semantic neighborhood relationships. Comput. Netw. 56(5), 1627–1645 (2012)

    Article  Google Scholar 

  23. Santini, S., Römer, K.: An adaptive strategy for quality-based data reduction in wireless sensor networks. In: Proceedings of the 3rd International Conference on Networked Sensing Systems, pp. 29–36 (2006)

  24. Sarkar, C., Rao, V.S., Prasad, R.V., Das, S.N., Misra, S., Vasilakos, A.: Vsf: an energy-efficient sensing framework using virtual sensors. IEEE Sens. J. 16 (12), 5046–5059 (2016)

    Article  Google Scholar 

  25. Shumway, R.H., Stoffer, D.S.: An approach to time series smoothing and forecasting using the em algorithm. J. Time Ser. Anal. 3(4), 253–264 (1982)

    Article  Google Scholar 

  26. Tan, L., Wu, M.: Data reduction in wireless sensor networks: a hierarchical lms prediction approach. IEEE Sens. J. 16(6), 1708–1715 (2016)

    Article  Google Scholar 

  27. Tayeh, G. B., Makhoul, A., Demerjian, J., Laiymani, D.: A New Autonomous Data Transmission Reduction Method for Wireless Sensors Networks. In: 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM), pp. 1–6 (2018)

  28. Wang, R., Ji, W., Song, B.: Durable relationship prediction and description using a large dynamic graph. World Wide Web 21(6), 1575–1600 (2018)

    Article  Google Scholar 

  29. Wen, G., Zhu, Y., Cai, Z., Zheng, W.: Self-tuning clustering for high-dimensional data. World Wide Web 21(6), 1563–1573 (2018)

    Article  Google Scholar 

  30. Wu, H., Wang, J., Suo, M., Mohapatra, P.: A holistic approach to reconstruct data in ocean sensor network using compression sensing. IEEE Access PP(99), 1–1 (2017)

    Google Scholar 

  31. Wu, H., Xian, J., Wang, J., Khandge, S., Mohapatra, P.: Missing data recovery using reconstruction in ocean wireless sensor networks. Comput. Commun. 132, 1–9 (2018)

    Article  Google Scholar 

  32. Wu, M., Tan, L., Xiong, N.: Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications. Inf. Sci. 329(Supplement C), 800–818 (2016)

    Article  Google Scholar 

  33. Yang, J., Tilak, S., Rosing, T. S.: An Interactive Context-Aware Power Management Technique for Optimizing Sensor Network Lifetime. In: SENSORNETS, pp. 69–76 (2016)

  34. Zhao, J., Govindan, R.: Understanding packet delivery performance in dense wireless sensor networks. In: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, SenSys ’03, 1-13. ACM, New York (2003)

  35. Zong, C., Yang, X., Wang, B., Liu, C.: Minimal explanations of missing values by chasing acquisitional data. World Wide Web 20(6), 1333–1362 (2017)

    Article  Google Scholar 

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Acknowledgements

This work is partially funded by the EIPHI Graduate School (contract “ANR-17-EURE-0002”), the France-Suisse Interreg RESponSE project, and the Lebanese University Research Program (Number: 4/6132).

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Authors and Affiliations

  1. Femto-St Institute, UMR 6174 CNRS, Université de Bourgogne Franche-Comté, Besançon, France

    Gaby Bou Tayeh, Abdallah Makhoul, Christophe Guyeux & Jacques Bahi

  2. LaRRIS, Faculty of Sciences, Lebanese University, Fanar, Lebanon

    Jacques Demerjian

Authors
  1. Gaby Bou Tayeh
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  2. Abdallah Makhoul
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  3. Jacques Demerjian
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  4. Christophe Guyeux
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  5. Jacques Bahi
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Correspondence to Gaby Bou Tayeh.

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This article belongs to the Topical Collection: Special Issue on Smart Computing and Cyber Technology for Cyberization

Guest Editors: Xiaokang Zhou, Flavia C. Delicato, Kevin Wang, and Runhe Huang

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Tayeh, G.B., Makhoul, A., Demerjian, J. et al. Fault tolerant data transmission reduction method for wireless sensor networks. World Wide Web 23, 1197–1216 (2020). https://doi.org/10.1007/s11280-019-00767-w

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  • DOI: https://doi.org/10.1007/s11280-019-00767-w

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