Abstract
WSNs generate a large amount of data in the form of streams, and temporal regularity in occurrence behavior is considered as an important measure for assessing the importance of patterns in WSN data. A frequent sensor pattern that occurs after regular intervals in WSNs is called regularly frequent sensor patterns (RFSPs). Existing RFSPs techniques assume that the data structure of the mining task is small enough to fit in the main memory of a processor. However, given the emergence of the Internet of Things (IoT), WSNs in future will generate huge volume of data, which means such an assumption does not hold any longer. To overcome this, a distributed solution using MapReduce model has not yet been explored extensively. Since MapReduce is becoming the de-facto model for computation on large data, an efficient RFSPs mining algorithm on this model is likely to provide a highly effective solution. In this work, we propose a regularly frequent sensor patterns mining algorithm called RFSP-H which uses MapReduce based framework. Extensive performance analyses show that our technique is significantly time efficient in finding regularly frequent sensor patterns.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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). doi:10.1155/2013/406316
Duarte, M.F., Hu, Y.H.: Vehicle classification in distributed sensor networks. J. Parallel Distrib. Comput. 64(7), 826–838 (2004)
Boukerche, A., Samarah, S.A.: Novel algorithm for mining association rules in wireless ad-hoc sensor networks. IEEE Trans. Para. Dist. Sys. 19(7), 865–877 (2008)
Rashid, M.M., Gondal, I., Kamruzzaman, J.: Mining associated patterns from wireless sensor networks. IEEE Trans. Comput. PP(99) (2014)
Rashid, M.M., Gondal, I., Kamruzzaman, J.: Regularly frequent patterns mining from sensor data stream. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part II. LNCS, vol. 8227, pp. 417–424. Springer, Heidelberg (2013)
Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A.V., Rong, X.: Data mining for the internet of things: literature review and challenges. Int. J. Distrib. Sensor Netw. 2015, 14 (2015)
Dean, J., Ghemawa, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 55(1), 107–113 (2008)
Leung, C.K.-S., Hayduk, Y.: Mining frequent patterns from uncertain data with MapReduce for big data analytics. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013, Part I. LNCS, vol. 7825, pp. 440–455. Springer, Heidelberg (2013)
Zhou, Z. J., and Feng, S.: Balanced parallel FP-Growth with MapReduce. In: IEEE Youth Conference on Information Computing and Telecom (YC-ICT), pp. 243–246 (2010)
Karim, M.R., Jeong, B.S., Choi, H.J.: A MapReduce framework for mining maximal contiguous frequent patterns in large DNA sequence datasets. IETE Tech. Rev. 29(2), 162–168 (2012)
Xie, J., Majors, J., Qin, X.: Improving MapReduce performance through data placement in heterogeneous hadoop clusters. In: IEEE International Symposium, IPDPSW (2010)
Frequent itemset mining repository. http://fimi.cs.helsinki.fi/data/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Rashid, M.M., Gondal, I., Kamruzzaman, J. (2015). A MapReduce Based Technique for Mining Behavioral Patterns from Sensor Data. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-26561-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26560-5
Online ISBN: 978-3-319-26561-2
eBook Packages: Computer ScienceComputer Science (R0)