Abstract
Hundreds of billions of things are expected to be integrated for heterogeneous Internet-of-Things (IoT) applications, which promises to drive the Future Internet. This variant IoT data mandates intelligent solutions to make sense of current data in real-time closer to the data origin. Clustering physically distributed data would enable efficient utilization where finding similarity becomes the central issue. To counter this, Jaro-Winkler and Jaccard-like algorithm have been proposed and extended to a distributed protocol to enable distributed clustering at the edge. Performance study, on a scalable IoT platform and an edge device, shows feasibility and effectiveness of the approach with respect to efficiency and applicability.
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References
Guillemin, P., Friess., P.: Internet of things strategic research roadmap. The Cluster of European Research Projects, Technical report, September 2009
Perera, C., et al.: Context aware computing for the Internet of Things: a survey. IEEE Commun. Surv. Tutor. 16(1), 414–454 (2014)
Rahman, H., Rahmani, R.: Enabling distributed intelligence assisted Future Internet of Things Controller (FITC). Appl. Comput. Inf. (2017). https://doi.org/10.1016/j.aci.2017.05.001
Tele2 IoT talks, May 2017. http://www.tele2iot.com/wp/uploads/2017/05/IoT-Talks-Stockholm-2017.pdf. Accessed 5 May 2017
Seth, A.: Internet of Things to smart IoT through semantic, cognitive, and perceptual computing. IEEE Intell. Syst. 31(2), 108–112 (2016)
Maarala, A., Su, X., Riekki, J.: Semantic reasoning for context-aware internet of things applications. IEEE Internet Things J. PP(99), 1 (2016)
Rahman, H., Rahmani, R., Kanter, T.: Multi-modal Context-Aware reasoNer (CAN) at the edge of IoT. Procedia Comput. Sci. 109, 335–342 (2017)
Perera, C., et. al.: Ca4iot: context awareness for Internet of Things. In: Proceedings of the IEEE International Conference on Green Computing and Communications (2012)
Rahman, H., et al.: Reasoning service enabling smarthome automation at the edge of context networks. In: Advances in Information Systems and Technologies. Springer (2016)
Rahmani, A.M. et al: Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: a fog computing approach. In: FGCS (2017)
TongKe, F.: Smart agriculture based on cloud computing and IOT. JCIT 8(2), 210–216 (2013)
Rahmani, R., Rahman, H., Kanter, T.: On performance of logical-clustering of flow-sensors. Int. J. Comput. Sci. Issues (IJCSI) 10(5, No. 2), 1–13 (2013)
Rahmani, R., Rahman, H., Kanter, T.: Context-based logical clustering of flow sensors - exploiting hyperflow and hierarchical DHTs. In: Proceeding(s) of 4th International Conference on Next Generation Information Technology, CNIT (2013)
Tsai, C., et al.: Data mining for Internet of Things: a survey. IEEE Commun. Surv. Tutor. 16(1), 77–97 (2014). 1st Quart.
Ienco, D., Pensa, R.G., Meo, R.: Context-based distance learning for categorical data clustering. In: Proceedings of the 8th International Symposium, IDA, pp. 83–94 (2009)
Lulli, A. et. al.: Scalable k-NN based text clustering. In: 2015 IEEE International Conference on Big Data (Big Data). IEEE (2015)
Tara, L., Prasad, G.V.S.N.R.V.: PageRank technique along with probability-maximization in sentence-clustering. In: IJESC. https://doi.org/10.4010/2016.1898
Kanungo, T., et al.: An efficient K-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
Rahman, H., Rahmani, R., Kanter, T.: Enabling scalable publish/subscribe for logical-clustering in crowdsourcing via mediasense. In: IEEE SAI Conference (2014)
Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 7553, 452–459 (2015)
Cohen, W., Ravikumar, P., Fienberg, S.: A comparison of string metrics for matching names and records. In: The International Conference on KDD (2003)
Barberousse, A., Franceschelli, S., Imbert, C.: Computer simulations as experiments. Synthese 169(3), 557–574 (2009)
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Rahman, H. (2018). Supporting IoT Data Similarity at the Edge Towards Enabling Distributed Clustering. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-77703-0_21
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DOI: https://doi.org/10.1007/978-3-319-77703-0_21
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