Semantic Space: An Infrastructure for Smart Spaces
IEEE Pervasive Computing
Practical robust localization over large-scale 802.11 wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
On location models for ubiquitous computing
Personal and Ubiquitous Computing
A Locating Method for WLAN based Location Service
ICEBE '05 Proceedings of the IEEE International Conference on e-Business Engineering
Indoor tracking in WLAN location with TOA measurements
Proceedings of the 4th ACM international workshop on Mobility management and wireless access
Everyware: The Dawning Age of Ubiquitous Computing
Everyware: The Dawning Age of Ubiquitous Computing
A unified semantics space model
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
PowerLine positioning: a practical sub-room-level indoor location system for domestic use
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
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This work addresses the gap regarding existing indoor location-awareness support solutions and their integration in real-world smart spaces composed of commodity mobile/fixed devices. The work describes experiences and lessons learnt in designing and implementing support for indoor location-based services within the framework of our research about smart space application platforms. The extensive usage of web technologies in our smart space application platform enables easy creation and mash-up of location based services in both ad hoc settings and interconnected environments. As for the indoor location sensing, the solution relies on a novel technique that is suitable for easy large-scale deployments on top of existing IEEE 802.11 wireless network infrastructures. The technique, which is based on received signal strength (RSS), is suitable for RSS measurements and location calculation to take place at the mobile terminal with minimum setup work. In addition, it does not require any proprietary hardware devices to be installed in the target environment. Experimental results, referring to different categories of mobile devices (commercial smartphones, PDAs and laptops), show that the technique performs quite well on commodity mobile devices in terms of power consumption and time required to calculate the indoor location.