Abstract
In Wireless Sensor Network, sensed data reflects two types of correlations of physical attributes: spatial and temporal. In this paper, a scheme named, Adaptive Prediction Strategy with ClusTering (APSCT) is proposed. In APSCT, a data-driven clustering and grey prediction model is used to exploit both the correlations. APSCT minimizes the transmission of messages in the network. However, the use of prediction includes additional computation overhead. There is a trade-off between prediction accuracy and energy consumption in computation and communication in wireless networks. This paper also gives an approach to calculate the upper and lower bound of the prediction interval which is used to evaluate different confidence levels and provides an energy-efficient sensor environment. Simulation is carried out on real-world data collected by Intel Berkeley Lab and results are compared with existing approaches.
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References
D. Puccinelli and M. Haenggi, Wireless sensor networks: Applications and challenges of ubiquitous sensing, IEEE Circuits and Systems Magazine, Vol. 5, No. 3, pp. 19–29, 2005.
I. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, Wireless sensor networks: a survey, Computer Networks, Vol. 38, No. 4, pp. 393–422, 2002.
G. Dhand and S. S. Tyagi, Data aggregation techniques in WSN: Survey, Procedia Computer Science, Vol. 92, pp. 378–384, 2016. https://doi.org/10.1016/j.procs.2016.07.393.
B. Bhushan and G. Sahoo, Routing protocols in wireless sensor networks, EURASIP Journal on Wireless Communications and Networking., Vol. 1, pp. 215–248, 2019. https://doi.org/10.1007/978-3-662-57277-1_10.
R. Luo and S. Pan, Mobile user localization in wireless sensor network using grey prediction method, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005., p. 6 pp., 2005. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1569330.
Y. L. Chen, Y. C. Lin, and T. C. Sun, A prediction scheme for object tracking in grid wireless sensor networks, Proceedings - 7th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2013, pp. 360–364, 2013.
G. Wei, Y. Ling, B. Guo, B. Xiao and A. V. Vasilakos, Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter, Computer Communications, Vol. 34, No. 6, pp. 793–802, 2011. https://doi.org/10.1016/j.comcom.2010.10.003.
H. Cheng, Z. Xie, L. Wu, Z. Yu and R. Li, Data prediction model in wireless sensor networks based on bidirectional LSTM, EURASIP Journal on Wireless Communications and Networking, Vol. 1, p. 203, 2019. https://doi.org/10.1186/s13638-019-1511-4.
Z. L. Mao and J. H. Sun, Application of Grey-Markov model in forecasting fire accidents, Procedia Engineering, Vol. 11, pp. 314–318, 2011. https://doi.org/10.1016/j.proeng.2011.04.663.
D. P. Singh, V. Bhateja, and S. K. Soni, “Energy optimization in WSNs employing rolling grey model,” 2014 International Conference on Signal Processing and Integrated Networks (SPIN), pp. 801–808, 2014. http://ieeexplore.ieee.org/document/6777064/
H. Cheng, Z. Xie, L. Wu, Z. Yu and R. Li, An efficient data model for energy prediction using wireless sensors, EURASIP Journal on Wireless Communications and Networking, Vol. 1, No. 203, p. 2019, 2019. https://doi.org/10.1186/s13638-019-1511-4.
S. Diwakaran, B. Perumal and K. V. Devi, A cluster prediction model-based data collection for energy efficient wireless sensor network, The Journal of Supercomputing, Vol. 75, No. 6, pp. 3302–3316, 2019.
G. Li and Y. Wang, Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks, Eurasip Journal on Wireless Communications and Networking, Vol. 2013, No. 1, pp. 1–13, 2013.
D. Praveen Kumar, T. Amgoth and C. S. R. Annavarapu, Machine learning algorithms for wireless sensor networks: A survey, Information Fusion, Vol. 49, No. 1, pp. 1–25, 2019. https://doi.org/10.1186/s13638-019-1511-4.
D. P. Singh, V. Bhateja, and S. K. Soni, Prolonging the Lifetime of Wireless Sensor Networks using Prediction based Data Reduction Scheme, In: International Conference on Signal Processing and Integrated Network (SPIN), 2014, pp. 420–425.
B. Stojkoska and K. Mahoski, Comparison of different data prediction methods for wireless sensor networks, The 10th Conference for Informatics and Information Technology, pp. 307–311, 2013.
G. M. Dias, B. Bellalta, and S. Oechsner, A Survey about Prediction-Based Data Reduction in Wireless Sensor Networks, ACM Computing Surveys (CSUR), vol. V, 2016. arXiv:1607.03443.
J. Wang, L. Feng, W. Xue and Z. Song, A survey on energy-efficient data management, SIGMOD Record, Vol. 40, No. 2, pp. 17–23, 2011.
Y. L. Borgne, S. Santini, and G. Bontempi, Adaptive model selection for time series prediction in wireless sensor networks, Signal Processing, June 2007, 1–28, 2007. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0165168407001879.
S. K. Soni, N. Chand, and D. P. Singh, Reducing the Data Transmission in WSNs using Time Series Prediction Model, In: IEEE International Conference on Signal Processing, Computing and Control (ISPCC), 2012.
W. Hailong, S. Yan, and W. Tuming, Dynamic power management of wireless sensor networks based on grey model, In: 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), vol. 1, 2010.
S. Yoon and C. Shahabi, The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks, ACM Transactions on Sensor Networks, Vol. 3, p. 1, 2007. http://portal.acm.org/citation.cfm?doid=1210669.1210672.
M. Ashouri, H. Yousefi, J. Basiri, A. M. A. Hemmatyar and A. Movaghar, PDC: Prediction-based data-aware clustering in wireless sensor networks, Journal of Parallel and Distributed Computing, 2015. https://doi.org/10.1016/j.jpdc.2015.02.004.
S. Yoon and C. Shahabi, Exploiting spatial correlation towards an energy efficient clustered aggregation technique (cag), Communications, 2005. ICC 2005. 2005 IEEE International Conference on, Vol. 5, pp. 3307–3313, 2005.
D. Julong, Introduction to grey system theory, The Journal of grey system, Vol. 1, No. 1, pp. 1–24, 1989.
C. I. Chen and S. J. Huang, The necessary and sufficient condition for GM(1, 1) grey prediction model, Applied Mathematics and Computation, Vol. 219, No. 11, pp. 6152–6162, 2013. https://doi.org/10.1016/j.amc.2012.12.015.
S. Liu, J. Forrest, and Y. Yang, A brief introduction to grey systems theory, In: Proceedings of 2011 IEEE International Conference on Grey Systems and Intelligent Services. IEEE, 2011, pp. 1–9.
S. Liu, and J. Y.-L. Forrest. Grey systems theory and applications. Springer, Berlin, Heidelberg, 2011.
M. Madhi and N. Mohamed, An Initial Condition Optimization Approach for Improving the Prediction Precision of a GM(1,1) Model, Mathematical and Computational Applications, Vol. 22, No. 1, p. 21, 2017. http://www.mdpi.com/2297-8747/22/1/21.
D. C. Montgomery, E. A. Peck and G. G. Vining, Introduction to linear regression analysis, vol. 821st, WileyHoboken, 2012.
H. Jiang, S. Jin, C. Wang and S. Member, Prediction or not ? An energy-efficient framework for clustering-based data collection in wireless sensor networks, IEEE Trabsactions Parallel Distrib. Syst., Vol. 22, No. 6, pp. 1–8, 2010.
J. Hao, Q. Chen, H. Huan and J. Zhao, Energy efficient clustering algorithm for data gathering in wireless sensor networks, Journal of Networks, Vol. 6, No. 3, pp. 490–497, 2011.
L. A. Villas, A. Boukerche, H. A. De Oliveira, R. B. De Araujo and A. A. Loureiro, A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks, Ad Hoc Networks, Vol. 12, No. 1, pp. 69–85, 2014. https://doi.org/10.1016/j.adhoc.2011.08.005.
S. Bahrami, H. Yousefi, and A. Movaghar, “DACA: Data-aware clustering and aggregation in query-driven wireless sensor networks,” 2012 21st International Conference on Computer Communications and Networks, ICCCN 2012 - Proceedings, 2012.
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Kumar, R., Jain, V., Chauhan, N. et al. An Adaptive Prediction Strategy with Clustering in Wireless Sensor Network. Int J Wireless Inf Networks 27, 575–587 (2020). https://doi.org/10.1007/s10776-020-00496-2
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DOI: https://doi.org/10.1007/s10776-020-00496-2