User mobility model in an active office

Teddy Mantoro; Chris Johnson

doi:10.1007/978-3-540-39863-9_4

User mobility model in an active office

Teddy Mantoro, Chris Johnson

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

7 Citations (Scopus)

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.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Emile Aarts, Rene Collier, Evert van Loenen, Boris de Ruyter
Publisher	Springer Verlag
Pages	42-55
Number of pages	14
ISBN (Print)	3540204180
DOIs	https://doi.org/10.1007/978-3-540-39863-9_4
Publication status	Published - 2003

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2875
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-39863-9_4

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Mantoro, T., & Johnson, C. (2003). User mobility model in an active office. In E. Aarts, R. Collier, E. van Loenen, & B. de Ruyter (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 42-55). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2875). Springer Verlag. https://doi.org/10.1007/978-3-540-39863-9_4

Mantoro, Teddy ; Johnson, Chris. / User mobility model in an active office. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Emile Aarts ; Rene Collier ; Evert van Loenen ; Boris de Ruyter. Springer Verlag, 2003. pp. 42-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Mantoro, T & Johnson, C 2003, User mobility model in an active office. in E Aarts, R Collier, E van Loenen & B de Ruyter (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2875, Springer Verlag, pp. 42-55. https://doi.org/10.1007/978-3-540-39863-9_4

User mobility model in an active office. / Mantoro, Teddy; Johnson, Chris.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Emile Aarts; Rene Collier; Evert van Loenen; Boris de Ruyter. Springer Verlag, 2003. p. 42-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2875).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Mantoro, Teddy

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N2 - 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.

AB - 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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DO - 10.1007/978-3-540-39863-9_4

M3 - Chapter

SN - 3540204180

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 42

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer Verlag

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Mantoro T, Johnson C. User mobility model in an active office. In Aarts E, Collier R, van Loenen E, de Ruyter B, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2003. p. 42-55. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-39863-9_4

User mobility model in an active office

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