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Interval Data Fusion with Preference Aggregation for Balancing Measurement Accuracy and Energy Consumption in WSN

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Abstract

An effective way to conserve energy in wireless sensor networks is reducing the amount of data transmissions. However, this can affect the accuracy and reliability of the sensed data considerably. To provide energy-accuracy trade-off, data fusion technique can be applied exploiting temporal and spatial correlation of sensed data. In this paper, we propose a novel approach for balancing energy consumption and measurement accuracy in wireless sensor networks. The approach is a combination of accuracy enhancement algorithm SensAcc and active node selection algorithm ActiveNode, which are based on the robust interval fusion with preference aggregation (IF&PA) method. The approach is aimed at selecting minimum number of nodes that can provide data of sufficient volume and quality to maintain required accuracy. The performance of the proposed algorithms has been evaluated by both simulation and real data processing. Simulation results show that the proposed approach significantly enhances the network lifetime while providing highly accurate measurement outcomes. Results of real data processing demonstrate noticeable decrease of measurement uncertainty even for small number of sensor nodes.

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References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Oliveira, L. M. L., & Rodrigues, J. J. P. C. (2011). Wireless sensor networks: A survey on environmental monitoring. Journal of Communications, 6(2), 143–155.

    Article  Google Scholar 

  4. Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks, 54(15), 2688–2710.

    Article  Google Scholar 

  5. Dubois, D., & Prade, H. (2001). Possibility Theory in Information Fusion. In G. Della Riccia, H.-J. Lenz, & R. Kruse (Eds.), Data fusion and perception. Berlin: Springer.

    MATH  Google Scholar 

  6. De Farias, C. M., Pirmez, L., Fortino, G., & Guerrieri, A. (2019). A multi-sensor data fusion technique using data correlations among multiple applications. Future Generation Computer Systems, 92, 109–118.

    Article  Google Scholar 

  7. Verma, N., & Singh, D. (2018). Data redundancy implications in wireless sensor networks. Procedia Computer Science, 132, 1210–1217.

    Article  Google Scholar 

  8. Chen, Y., Shu, J., Zhang, S., Liu, L., Sun, L. (2009). Data fusion in wireless sensor networks. In Proceedings of the 2nd International Symposium on Electronic Commerce and Security, (vol. 2, pp. 504–509).

  9. Muravyov, S. V., Tao, S., Chan, M. C., & Tarakanov, E. V. (2015). Consensus rankings in prioritized converge-cast scheme for wireless sensor network. Ad Hoc Networks, 24(1), 160–171.

    Article  Google Scholar 

  10. Shobana, M., Sabitha, R., Karthik, S. (2020). Cluster-based systematic data aggregation model (CSDAM) for real-time data processing in large-scale WSN. Wireless Personal Communications.

  11. Ayadi, A., Ghorbel, O., Obeid, A. M., & Abid, M. (2017). Outlier detection approaches for wireless sensor networks: A survey. Computer Networks, 129(Part 1), 319–333.

    Article  Google Scholar 

  12. Gil, P., Martins, H., & Januário, F. (2018). Outliers detection methods in wireless sensor networks. Artificial Intelligence Review, 52(4), 2411–2436.

    Article  Google Scholar 

  13. Boulis, A., Ganeriwal, S., & Srivastava, M. B. (2003). Aggregation in sensor networks: An energy–accuracy trade-off. Ad Hoc Networks, 1, 317–331.

    Article  Google Scholar 

  14. Villas, L. A., Boukerche, A., de Oliveira, H. A. B. F., de Araujo, R. B., & Loureiro, A. A. F. (2014). A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks. Ad Hoc Networks, 12, 69–85.

    Article  Google Scholar 

  15. Boulanouar, I., Rachedi, A., Lohier, S., Roussel, G. (2011). Energy-aware object tracking algorithm using heterogeneous wireless sensor networks. In Proceedings of the IEEE IFIP Wireless Days Conference, hal-00633034.

  16. Armaghani, F.R., Gondal, I., Kamruzzaman, J., Green, D.G. (2012). Dynamic sensors collaboration to balance the accuracy-lifetime trade-off in multiple-target tracking. In Proceedings of the IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications, 6362870.

  17. Feng, J., Zhao, H. and Lian, B. (2016). Efficient and adaptive node selection for target tracking in wireless sensor network. Journal of Sensors, p. 9152962.

  18. Benavoli, A., & Chisci, L. (2007). Towards optimal energy-quality tradeoff in tracking via sensor networks. Proceedings of the European Control Conference, 2007, 1523–1529.

    Google Scholar 

  19. Hasan, N. U., Ejaz, W., Lee, S., & Kim, H. S. (2012). Knapsack-based energy-efficient node selection scheme for cooperative spectrum sensing in cognitive radio sensor networks. IET Communications, 6(17), 2998–3005.

    Article  Google Scholar 

  20. Hu, D., Mao, S., Billor, N., & Agrawal, P. (2013). On the trade-off between energy efficiency and estimation error in compressive sensing. Ad Hoc Networks, 11, 1848–1857.

    Article  Google Scholar 

  21. Chowdhury, S., Roy, A., Benslimane, A., & Giri, Ch. (2019). On semantic clustering and adaptive robust regression based energy-aware communication with true outliers detection in WSN. Ad Hoc Networks, 94, 101934.

    Article  Google Scholar 

  22. Ashouri, M., Yousefi, H., Basiri, J., Hemmatyar, A. M. A., & Movaghar, A. (2015). PDC: Prediction-based data-aware clustering in wireless sensor networks. Journal of Parallel and Distributed Computing, 81–82, 24–35.

    Article  Google Scholar 

  23. Muravyov, S. V., Khudonogova, L. I., & Emelyanova, E. Y. (2018). Interval data fusion with preference aggregation. Measurement, 116, 621–630.

    Article  Google Scholar 

  24. Khudonogova, L. I., & Muravyov, S. V. (2018). Interval data fusion with preference aggregation in wireless sensor network: energy-accuracy trade-off in presence of outliers. Journal of Physics: Conference Series, 1065(7), 072016.

    Google Scholar 

  25. Muravyov, S. V. (2014). Dealing with chaotic results of Kemeny ranking determination. Measurement, 51, 328–334.

    Article  Google Scholar 

  26. Muravyov, S. V., Baranov, P. F., & Emelyanova, E. Y. (2019). How to transform all multiple solutions of the Kemeny Ranking Problem into a single solution. Journal of Physics: Conference Series, 1379(1), 012053.

    Google Scholar 

  27. Muravyov, S.V., Khudonogova, L.I. (2016). Sensor accuracy enhancement in wireless sensor network by preference aggregation, Cert. State Registr. Comp. Progr. No. 2016663686 (RU).

  28. Muravyov, S.V., Khudonogova, L.I. (2016). Active node selection in a cluster of wireless sensor network for energy decrease, Cert. State Registr. Comp. Progr. No. 2016663692 (RU).

  29. ISO/IEC Guide 98–3:2008/Suppl 1:2008 (JCGM/WG1/101) Propagation of distributions using a Monte Carlo method.

  30. Intel Lab Data. <http://db.lcs.mit.edu/labdata/labdata.html> (last accessed: 21.04.2020).

  31. Stojkoska, B., Solev, D., Davcev, D. (2011). Data prediction in WSN using variable step size LMS algorithm. In Proceedings of the 5th International Conference on Sensor Technologies and Applications, (pp. 191–196).

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Acknowledgements

The work has been supported by the Russian Science Foundation, Project # 18-19-00203. The research is carried out at Tomsk Polytechnic University within the framework of Tomsk Polytechnic University Competitiveness Enhancement Program.

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  1. Division for Automation and Robotics, National Research Tomsk Polytechnic University, 30, Lenin Avenue, Tomsk, Russian Federation, 634050

    Liudmila I. Khudonogova & Sergey V. Muravyov

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  1. Liudmila I. Khudonogova
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  2. Sergey V. Muravyov
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Correspondence to Liudmila I. Khudonogova.

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Khudonogova, L.I., Muravyov, S.V. Interval Data Fusion with Preference Aggregation for Balancing Measurement Accuracy and Energy Consumption in WSN. Wireless Pers Commun 118, 2399–2421 (2021). https://doi.org/10.1007/s11277-021-08132-9

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