Abstract
Wireless sensor networks (WSN) have been employed in numerous fields of real world applications. Data failure and noise reduction still remain tough unsolved problems for WSN. Predicting methods for data recovery by empirical treatment, mostly based on statistics has been studied exclusively. Machine learning models can greatly enhance the predicting performance. In this paper, an improved HMM is proposed for multi-step predicting of wireless sensing data given historical data. The proposed model is based on clustering of wireless sensing data and multi-step predicting is accordingly accomplished for different varying patterns using HMM whose parameters are optimized by Particle Swarm Optimization (PSO). We evaluate our model on two real wireless sensing datasets, and comparison between Naive Bayesian, Grey System, BP Neural Networks and traditional HMMs are conducted. The experimental results show that our proposed model can provide higher accuracy in sensing data predicting. This proposed model is promising in the fields of agriculture, industry and other domains, in which the sensing data usually contains various varying patterns.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kamal, A.R.M., Bleakley, C.J., Dobon, S.: Failure detection in wireless sensor networks: A sequence-based dynamic approach. ACM Trans. Sensor Netw. 10(2), 35 (2014)
Wu, M., Tan, L., Xiong, N.: Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications. Inf. Sci. 329, 800–818 (2016)
Aggarwal, C.C.: Managing and mining sensor data (2013)
Mahmood, A., Shi, K., Khatoon, S., Xiao, M.: Data mining techniques for wireless sensor networks: A survey. Int. J. Distrib. Sensor Netw. 2013, 24 (2013)
Liu, Q., Zhang, Y.Y., Shen, J., Xiao, B., Linge, N.: A wsn-based prediction model of microclimate in a greenhouse using an extreme learning approach. In: Advanced Communication Technology, pp. 133–137 (2015)
Vuppala, S.K., Ghosh, A., Patil, K.A., Padmanabh, K.: A scalable WSN based data center monitoring solution with probabilistic event prediction. In: Advanced Information Networking and Applications (2012)
Kharade, S.S., Khiani, S.: Fault prediction and relay node placement in wireless sensor network-a survey. Int. J. Sci. Res. 3(10), 702–704 (2014)
Saini, A., Sharma, K.K., Dalal, S.: A survey on outlier detection in wsn. Int. J. Res. Aspects Eng. Manage. 1(2), 69–72 (2014)
Li, J., Li, L., Wu, Y., Chen, S.: An improved recommender based on hidden Markov model. In: Anthony, P., Ishizuka, M., Lukose, D. (eds.) PRICAI 2012. LNCS, vol. 7458, pp. 870–874. Springer, Heidelberg (2012)
Teoh, T.T., Cho, S.Y., Nguwi, Y.Y.: Hidden markov model for hard-drive failure detection, pp. 3–8 (2012)
Jianbo, Y.: Health condition monitoring of machines based on hidden markov model and contribution analysis. IEEE Trans. Instrum. Meas. 61(8), 2200–2211 (2012)
Gupta, A., Dhingra, B.: Stock market prediction using hidden Markov models. Engineering and Systems, pp. 1–4 (2012)
de Araújo, G.M., Kaiser, J., Becker, L.B.: An optimized markov model to predict link quality in mobile wireless sensor networks. In: Computers and Communications, pp. 000307–000312 (2012)
Deeb, B., Hassan, Z., Beseiso, M.: An adaptive HMM based approach for improving e-learning methods (2014)
Liu, L., Luo, D., Liu, M., Zhong, J., Wei, Y., Sun, L.: A self-adaptive hidden markov model for emotion classification in chinese microblogs. Mathematical Problems in Engineering (2015)
Acknowledgment
This work is supported by Natural Science Foundations of China (No. 61170192), National High-tech R&D Program of China (No. 2013AA013801).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, Z., Deng, B., Chen, S., Li, L. (2016). An Improved HMM Model for Sensing Data Predicting in WSN. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-39937-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39936-2
Online ISBN: 978-3-319-39937-9
eBook Packages: Computer ScienceComputer Science (R0)