By Pampa Sadhukhan, Pradip K. Das (auth.), Richard Fuller, Xenofon D. Koutsoukos (eds.)
This e-book constitutes the refereed court cases of the second one overseas Workshop on cellular Entity Localization and monitoring in GPS-less Environnments, soften, held in Orlando, Florida, united states, in September 2009 along with the 11th overseas convention on Ubiquitous Computing (Ubicomp 2009). soften is a discussion board for the state of the art applied sciences in cellular localization and monitoring and novel purposes of location-based providers. The study contributions in those court cases conceal major features of localization and monitoring of cellular units that come with ideas appropriate for shrewdpermanent telephones and cellular sensor networks in either open air and indoor environments utilizing different sensors and radio signs. Novel theoretical tools, algorithmic layout and research, software improvement, and experimental experiences are awarded in 14 papers that have been reviewed rigorously through this system committee. moreover, 3 invited papers, with themes on place selection utilizing RF structures, Cramer-Rao-Bound research for indoor localization and techniques focusing on cellular sensor networks, also are incorporated within the proceedings.
Read or Download Mobile Entity Localization and Tracking in GPS-less Environnments: Second International Workshop, MELT 2009, Orlando, FL, USA, September 30, 2009. Proceedings PDF
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Extra resources for Mobile Entity Localization and Tracking in GPS-less Environnments: Second International Workshop, MELT 2009, Orlando, FL, USA, September 30, 2009. Proceedings
Example text
Cell ID cluster sizes to be exhaustive – there may exist other significant places unlabeled in Figure 5 and need more sophisticated algorithms to discover. In other words, the precision is high but the recall may be low. We leave a complete solution of the issue for future work. Fig. 5. Cell ID clusters that potentially represent significant places to the user 46 G. Yang Table 2. Ground truth of eight cell ID clusters selected from Figure 5 and their corresponding places and visit patterns Label Cluster Size Place Normal Visit Pattern A 21 Home Daily unless out of town B 20 Work Daily on weekdays unless out of office C 5 Grocery Store Once per week unless out of town D 3 Gymnasium Occasionally, approx.
C Springer-Verlag Berlin Heidelberg 2009 Adaptive Motion Model for a Smart Phone Based OLS 51 which combines sensor data available from mobile devices. An adaptive motion model for the particle filter is proposed to seamlessly fuse different technologies based on the availability of accelerometer and GPS data from the smart phone. 1 Related Works and Contribution As shown in [2] a lot of techniques can be used for localization. GSM is a widely used technology for the localization of handheld devices.
They must be able to identify and connect to a cell, and remain connected in operation. The biggest advantages of cell-ID-based localization are the universal availability – in terms of both the device base and network coverage, and near-zero added costs – in terms of both manufacturing and energy consumption. Even with the downsides such as coarse accuracy and lack of easy cell-ID-to-physical-location mapping – a GSM cell may span well over several km2 , and today’s cell location databases are far from open/adequate – we will show in this paper that if handled appropriately, cell IDs alone may still reveal plenty of a user’s location patterns.