Abstract
In this paper, we propose a novel approach, the Experience-Oriented Smart Things that allows experiential knowledge discovery, storage, involving, and sharing for Internet of Things. The main features, architecture, and initial experiments of this approach are introduced. Rather than take all the data produced by Internet of Things, this approach focuses on acquiring only interesting data for its knowledge discovery process. By catching decision events, this approach gathers its own daily operation experience, which is the interesting data, and uses such experience for knowledge discovery. An initial experiment was made at the end of this paper, by applying this approach to a sensors-equipped bicycle, the bicycle is able to learn user’s physical features and recognize its user out of other riders. Customized version of Decisional DNA is used in this approach as the knowledge representation technique. Decisional DNA is a domain-independent, and flexible, and standard experiential knowledge repository solution that allows knowledge to be acquired, reused, evolved and shared easily. The presented conceptual approach demonstrates how knowledge can be discovered through its domain’s experiences and stored as Decisional DNA.
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References
Atzori, L.: Antonio Iera, and Giacomo Morabito.: The internet of things: A survey. Comput. Netw. 54(15), 2787–2805 (2010)
Ashton, K.: That ‘Internet of Things’ Thing. RFID Journal. http://www.rfidjournal.com/article/print/4986
Tsai, C., et al.: Data Mining for Internet of Things: A Survey. 1-21 (2013)
Kortuem, G., et al.: Smart objects as building blocks for the internet of things. IEEE Internet Comput. 14(1), 44–51 (2010)
Perera, C., et al.: Context aware computing for the internet of things: A survey, 1-41 (2013)
Bandyopadhyay, D., Jaydip, S.: Internet of things: Applications and challenges in technology and standardization. Wireless Pers. Commun. 58(1), 49–69 (2011)
Domingo, M.C.: An overview of the Internet of Things for people with disabilities. Journal of Network and Computer Applications 35(2), 584–596 (2012)
Miorandi, D., et al.: Internet of things: Vision, applications and research challenges. Ad Hoc Netw. 10(7), 1497–1516 (2012)
López, T.S., et al.: Taxonomy, technology and applications of smart objects. Information Systems Frontiers 13(2), 281–300 (2011)
López, T.S., et al.: Adding sense to the Internet of Things. Pers. Ubiquit. Comput. 16(3), 291–308 (2012)
Li, Xu, et al.: Smart community: an internet of things application. Communications Magazine, IEEE 49(11), 68–75 (2011)
Vlacheas, P., et al.: Enabling smart cities through a cognitive management framework for the internet of things. IEEE Communications Magazine 51(6), (2013)
López, T.S., et al.: Adding sense to the Internet of Things. Pers. Ubiquit. Comput. 16(3), 291–308 (2012)
Lee, S.W., Oliver, P., Zeungnam, B.: Applying human learning principles to user-centered IoT systems. Computer 46(2), 46–52 (2013)
Vasseur, J.-P., Adam D.: Interconnecting smart objects with ip: The next internet. Morgan Kaufmann (2010)
The IPSO Alliance. http://www.ipso-alliance.org
Maldonado Sanin, C.A.: Smart Knowledge Management System. PhD Thesis, Faculty of Engineering and Built Environment - School of Mechanical Engineering, University of Newcastle, E. Szczerbicki, Doctor of Philosophy Degree, Newcastle (2007)
Sanin, C., Szczerbicki, E.: Experience-based Knowledge Representation SOEKS. Cybernetics and Systems 40(2), 99–122 (2009)
Sanin, C., Szczerbicki, E.: An OWL Ontology of Set of Experience Knowledge Structure. Journal of Universal Computer Science 13, 209–223 (2007)
Zhang, H.: Cesar Sanín, and Edward Szczerbicki.: Implementing Fuzzy Logic to Generate User Profile in Decisional DNA Television: The Concept and Initial Case Study. Cybernetics and Systems 44(2–3), 275–283 (2013)
Sanin, C., Mancilla-Amaya, L., Szczerbicki, E., CayfordHowell, P.: Application of a Multi-domain Knowledge Structure: The Decisional DNA. Intel. Sys. For Know. Management, SCI 252, 65–86 (2009)
Sanín, C., Toro, C., Sanchez, E., Mancilla-Amaya, L., Zhang, H., Szczerbicki, E., Crasco, E., Peng, W.: Decisional DNA: A Multi-technology Shareable Knowledge Structure for Decisional Experience. Neurocomputing 88, 42–53 (2012)
Lloyd, J.W.: Logic for Learning: Learning Comprehensible Theories from Structure Data. Springer, Berlin (2003)
The NXP LPCXpresso Board for LPC1769. http://www.nxp.com/demoboard/OM13000.html
Hochbaum, D.S., Shmoys, D.B.: A best possible heuristic for the k-center problem. Mathematics of operations research 10(2), 180–184 (1985)
Witten, I. H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann (2005)
Ali, N., Abu-Elkheir, M.: Data management for the internet of things: green directions. In: Proc. IEEE Globecom Workshops. pp. 386–390 (2012)
Russom, P.: Big data analytics. TDWI Best Practices Report, Fourth Quarter (2011)
Xu, R., Wunsch, D.: Clustering. vol. 10. John Wiley & Sons (2008)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: ACM SIGMOD Record, vol. 25(2), pp. 103–114. ACM (1996)
Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams: Theory and practice. IEEE Trans. Knowl. Data Eng. 15(3), 515–528 (2003)
Ng, R.T., Han, J.: CLARANS: A method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 14(5), 1003–1016 (2002)
Madden, S.: From databases to big data. IEEE Internet Comput. 16(3), 0004–6 (2012)
Cantoni, V., Lombardi, L., Lombardi, P.: Challenges for data mining in distributed sensor networks. In: IEEE 18th International Conference on Pattern Recognition, ICPR 2006, vol. 1, pp. 1000–1007 (2006)
Baraniuk, R.G.: More is less: signal processing and the data deluge. Science 331(6018), 717–719 (2011)
Ding, C., He, X.: K-means clustering via principal component analysis. In: Proceedings of the twenty-first international conference on Machine learning, p. 29. ACM (2004)
Chiang, M.C., Tsai, C.W., Yang, C.S.: A time-efficient pattern reduction algorithm for k-means clustering. Inf. Sci. 181(4), 716–731 (2011)
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems 29(7), 1645–1660 (2013)
Aggarwal, C.C., Ashish, N., Sheth, A.: The internet of things: a survey from the data-centric perspective. In: Managing and mining sensor data, pp. 383–428. Springer US (2013)
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Zhang, H., Sanin, C., Szczerbicki, E. (2015). Experience-Oriented Enhancement of Smartness For Internet of Things. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9012. Springer, Cham. https://doi.org/10.1007/978-3-319-15705-4_49
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DOI: https://doi.org/10.1007/978-3-319-15705-4_49
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