Abstract
This paper proposes a framework to model dynamically changing situations in real world environments. In the real world, situations naturally vary in how they occur. Understanding these variations is essential to establish accurate knowledge of the environment and provide situation aware services. Current approaches to situation modeling may benefit from the inclusion of a dedicated method of adapting to changes in how situations occur over time. The proposed framework is introduced and a description of its components and their relations is provided. The proposed implementation of the framework is described in a smart office based scenario.
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References
Nwiabu, N., Allison, I., Holt, P., Lowit, P., Oyeneyin, B.: Case-Based Situation Awareness. In: IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support, pp. 22–29 (2012)
Li, Z.Y., Park, J.C., Lee, B., Youn, H.Y.: Situation Awareness Based on Dempster-Shafer Theory and Semantic Similarity. In: IEEE 16th International Conference on Computational Science and Engineering, pp. 545–552 (2013)
Pereira, I., Costa, P.D., Almeida, J.P.: A Rule-Based Platform for Situation Management. In: IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), pp. 83–90 (2013)
Leida, M., Gusmini, A., Davies, J.: Semantics-Aware Data Integration for Heterogeneous Data Sources. Journal of Ambient Intelligence and Humanized Computing 4(4), 471–491 (2012)
Fischer, Y., Beyerer, J.: Defining Dynamic Bayesian Networks for Probabilistic Situation Assessment. In: IEEE 15th Conference on Information Fusion (FUSION), pp. 888–895 (2012)
Baumgartner, N., Gottesheim, W., Mitsch, S., Retschitzegger, W., Schwinger, W.: BeAware!—Situation Awareness, the Ontology-Driven way. Data & Knowledge Engineering 69(11), 1181–1193 (2010)
Roy, N., Gu, T., Das, S.K.: Supporting Pervasive Computing Applications with Active Context Fusion and Semantic Context Delivery. Pervasive and Mobile Computing 6(1), 21–42 (2010)
Chen, L., Nugent, C., Okeyo, G.: An Ontology-Based Hybrid Approach to Activity Modeling for Smart Homes. IEEE Transactions on Human-Machine Systems 44(1), 92–105 (2014)
Moradi-Pari, E., Tahmasbi-Sarvestani, A.F., Yaser, P.: Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation. Proceedings of the IEEE 8(1), 1–10 (2014)
Luo, R.C., Chang, C.C., Lai, C.C.: Multisensor Fusion and Integration: Theories, Applications, and its Perspectives. IEEE Sensors Journal 11(12), 3122–3138 (2011)
Ye, J., Dobson, S.: Exploring Semantics in Activity Recognition using Context Lattices. Journal of Ambient Intelligence and Smart Environments 2(4), 1–18 (2010)
Lee, C.H., Birch, D., Wu, C., Silva, D., Tsinalis, O., Li, Y., Yan, S., Ghanem, M., Guo, Y.: Building a Generic Platform for Big Sensor Data Application. In: IEEE International Conference on Big Data Building, pp. 94–102 (2013)
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Pearson, R., Donnelly, M., Liu, J., Galway, L. (2014). A Framework for Situation Awareness Based upon Dynamic Situation Modeling. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds) Ambient Assisted Living and Daily Activities. IWAAL 2014. Lecture Notes in Computer Science, vol 8868. Springer, Cham. https://doi.org/10.1007/978-3-319-13105-4_15
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DOI: https://doi.org/10.1007/978-3-319-13105-4_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13104-7
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