Abstract
User mobility in an Active Office represents human activity in a context awareness and ambient intelligent environment. This paper describes user mobility by detecting their changing locations. We have explored precise, proximate and predicted user location using a variety of sensors (e.g. WiFi and Bluetooth) and investigated how the sensors fit in an Active Office to provide interoperability to detect them. We developed a model to predict and proximate user location using wireless sensors in the Merino layering architecture, i.e. the architecture for scalable context processing in an Intelligent Environment.
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Mantoro, T., Johnson, C. (2003). User Mobility Model in an Active Office. In: Aarts, E., Collier, R.W., van Loenen, E., de Ruyter, B. (eds) Ambient Intelligence. EUSAI 2003. Lecture Notes in Computer Science, vol 2875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39863-9_4
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DOI: https://doi.org/10.1007/978-3-540-39863-9_4
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