Abstract
Although IoT service and service composition provide effective means to develop IoT applications, the dynamic and time-varying correlation among massive sensors rises up new challenges to the traditional model-based approaches, and the extra uncertainty and complexity of service composition become apparent. This paper proposes a data-driven service composition method based on our previous proactive data service model. We utilize real-time correlation analysis of sensor data to refine model-based service composition at runtime. The correlation among sensor data is usually asynchronous. In this paper, we adopt and improve a Dynamic Time Warping (DTW) algorithm to obtain one-way lag-correlation, and realize dynamic sensor data correlation through refining existing service composition. Based on the real sensor data set in a coal-fired power plant, a series of experiments demonstrate the effectiveness of our service composition method.
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Han, Y.B., Liu, C., Su, S., et al.: A decentralized and service-based approach to proactively correlating stream data. In: International Conference on Internet of Things (2016)
Chu, V.W., Wong, R.K., Liu, W., et al.: Traffic analysis as a service via a unified model. In: IEEE International Conference on Services Computing, pp. 195–202. IEEE (2014)
Zhang, J., Radia, N., Li, Z., et al.: An infrastructure supporting considerate sensor service provisioning. In: The 6th IEEE International Conference on Service Oriented Computing and Applications (SOCA), pp. 69–76. IEEE (2013)
Guilly, T.L., Olsen, P., Ravn, A.P., et al.: HomePort: middleware for heterogeneous home automation networks. In: IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 627–633. IEEE (2013)
Budgaga, W., Malensek, M., Pallickara, S.L., et al.: A framework for scalable real-time anomaly detection over voluminous, geospatial data streams. In: Concurrency & Computation Practice & Experience, pp. 1–24 (2017)
Hibner, A., Zielinski, K. Semantic-based dynamic service composition and adaptation. In: 2007 IEEE Congress on Services, pp. 213–220. IEEE (2007)
Klusch, M., Gerber, A.: Semantic web service composition planning with owls-xplan. In: Proceedings of the 1st International AAAI Fall Symposium on Agents and the Semantic Web, pp. 55–62 (2005)
Peer, J.: A POP-based replanning agent for automatic web service composition. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 47–61. Springer, Heidelberg (2005). https://doi.org/10.1007/11431053_4
Liu, X., Ma, Y., Huang, G., et al.: Data-driven composition for service-oriented situational web applications. IEEE Trans. Serv. Comput. 8(1), 2–16 (2015)
Hossain, M.S., Moniruzzaman, M., Muhammad, G., et al.: Big data-driven service composition using parallel clustered particle swarm optimization in mobile environment. IEEE Trans. Serv. Comput. 9(5), 806–817 (2016)
Wu, S., Lin, H., Wang, W., et al.: RLC: ranking lag correlations with flexible sliding windows in data streams. Pattern Anal. Appl. 1–11 (2016)
Guo, T., Sathe, S., Aberer, K.: Fast distributed correlation discovery over streaming time-series data. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), pp. 1161–1170 (2015)
Liu, X., Yang, M.C.K.: Simultaneous curve registration and clustering for functional data. Comput. Stat. Data Anal. 53(4), 1361–1376 (2009)
Kate, R.J.: Using dynamic time warping distances as features for improved time series classification. Data Min. Knowl. Discov. 30(2), 283–312 (2016)
Sheng, Q.Z., Qiao, X., Vasilakos, A.V., et al.: Web services composition: a decade’s overview. Inf. Sci. 280, 218–238 (2014)
Wu, Z., Ranabahu, A., Gomadam, K., Sheth, A., Miller, J.: Automatic composition of semantic web services using process mediation. In: International Conference on Enterprise Information Systems, pp. 453–461 (2007)
Canfora, G., Di Penta, M., Esposito, R., Villani, M.L.: A framework for QoS-aware binding and re-binding of composite web services. J. Syst. Softw. 81(10), 1754–1769 (2008)
Kalasapur, S., Kumar, M., Shirazi, B.A.: Dynamic service composition in pervasive computing. IEEE Trans. Parallel Distrib. Syst. 18(7), 907–918 (2007)
Zhang, L.J., Li, B.: Requirements driven dynamic services composition for web services and rid solutions. J. Grid Comput. 2, 121–140 (2004)
Sakurai, Y., Papadimitriou, S., Faloutsos, C.: BRAID: stream mining through group lag correlations. In: ACM SIGMOD International Conference on Management of Data, pp. 599–610 (2005)
Lin, Z.Y., Jiang, Y., Lai, Y.X., et al.: A new algorithm on lagged correlation analysis between time series. J. Comput. Res. Dev. 12, 2645–2655 (2012)
Ramsay, J.: Functional Data Analysis. Springer, New York (2006). https://doi.org/10.1007/b98888
Jiang, G.X., Wang, W.J.: Correlation analysis in curve registration of time series. J. Softw. 25(9), 2002–2017 (2014)
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Liu, C., Zhang, Z., Zhang, S., Han, Y. (2019). Runtime Service Composition Modification Supporting Situational Sensor Data Correlation. In: Liu, X., et al. Service-Oriented Computing – ICSOC 2018 Workshops. ICSOC 2018. Lecture Notes in Computer Science(), vol 11434. Springer, Cham. https://doi.org/10.1007/978-3-030-17642-6_15
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DOI: https://doi.org/10.1007/978-3-030-17642-6_15
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