Making Location-Aware Computing Working Accurately in Smart Spaces

Making Location-Aware Computing Working Accurately in Smart Spaces

Teddy Mantoro, Media Ayu, Maarten Weyn
ISBN13: 9781609600426|ISBN10: 1609600428|EISBN13: 9781609600433
DOI: 10.4018/978-1-60960-042-6.ch035
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MLA

Mantoro, Teddy, et al. "Making Location-Aware Computing Working Accurately in Smart Spaces." Handbook of Research on Mobility and Computing: Evolving Technologies and Ubiquitous Impacts, edited by Maria Manuela Cruz-Cunha and Fernando Moreira, IGI Global, 2011, pp. 539-557. https://doi.org/10.4018/978-1-60960-042-6.ch035

APA

Mantoro, T., Ayu, M., & Weyn, M. (2011). Making Location-Aware Computing Working Accurately in Smart Spaces. In M. Cruz-Cunha & F. Moreira (Eds.), Handbook of Research on Mobility and Computing: Evolving Technologies and Ubiquitous Impacts (pp. 539-557). IGI Global. https://doi.org/10.4018/978-1-60960-042-6.ch035

Chicago

Mantoro, Teddy, Media Ayu, and Maarten Weyn. "Making Location-Aware Computing Working Accurately in Smart Spaces." In Handbook of Research on Mobility and Computing: Evolving Technologies and Ubiquitous Impacts, edited by Maria Manuela Cruz-Cunha and Fernando Moreira, 539-557. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-042-6.ch035

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Abstract

In smart environment, making a location-aware personal computing working accurately is a way of getting close to the pervasive computing vision. The best candidate to determine a user location in indoor environment is by using IEEE 802.11 (Wi-Fi) signals, since it is more and more widely available and installed on most mobile devices used by users. Unfortunately, the signal strength, signals quality and noise of Wi-Fi, in worst scenario, it fluctuates up to 33% because of the reflection, refraction, temperature, humidity, the dynamic environment, etc. We present our current development on a light-weight algorithm, which is easy, simple but robust in producing the determination of user location using WiFi signals. The algorithm is based on “multiple observers” on ?k-Nearest Neighbour. We extend our approach in the estimation indoor-user location by using combination of different technologies, i.e. WiFi, GPS, GSM and Accelerometer. The algorithm is based on opportunistic localization algorithm and fuse different sensor data in order to be able to use the data which is available at the user position and processable in a mobile device.

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