Download Mobile Entity Localization and Tracking in GPS-less by Pampa Sadhukhan, Pradip K. Das (auth.), Richard Fuller, PDF

By Pampa Sadhukhan, Pradip K. Das (auth.), Richard Fuller, Xenofon D. Koutsoukos (eds.)

This booklet constitutes the refereed lawsuits 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 together 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 prone. The examine contributions in those lawsuits disguise major features of localization and monitoring of cellular units that come with concepts compatible for shrewdpermanent telephones and cellular sensor networks in either open air and indoor environments utilizing various sensors and radio indications. Novel theoretical tools, algorithmic layout and research, program improvement, and experimental reviews are offered in 14 papers that have been reviewed rigorously by way of this system committee. moreover, 3 invited papers, with issues on place decision utilizing RF platforms, Cramer-Rao-Bound research for indoor localization and ways focusing on cellular sensor networks, also are incorporated within the proceedings.

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Additional info for Mobile Entity Localization and Tracking in GPS-less Environnments: Second International Workshop, MELT 2009, Orlando, FL, USA, September 30, 2009. Proceedings

Sample text

Google Latitude [20]) over the network, but this requires user cooperation. Cell association of passive, non-communicating users, is beyond the reach of majority of methods, as those users are reporting their location only sporadically using a procedure called location update. A location update is done when a user crosses boundaries of the so called “location areas” (those are geographically large, consisting of hundreds of cells) or after a significant time (order of hours for the network studied in this work).

As of June 2, 2009, the Open Cell ID database has information on 131 cell IDs out of a total of 596, a mere coverage of 22%. It shows that crowd-sourced cell-ID-to-physical-location mapping databases are still in an early stage. Among the eight significant places Discovering Significant Places from Mobile Phones 47 Fig. 7. Number of significant places – daily statistics Fig. 8. A typical weekday (left)/weekend (right) with places and transit in Table 2, “Work” has the best coverage with six cell IDs, plotted on the map in Figure 6.

For this purpose we introduce a function QualifiedSet() in Algorithm 2. It counts how many times each cell ID appears, on a daily basis, and returns those of which the number is above a given threshold Q for at least one day in the data set. Cell IDs returned from QualifiedSet() have demonstrated sufficient exposure to be considered for clustering. Algorithm 1 is then amended as in Algorithm 3. 2 Handling Missing Data A second problem with the naive algorithm is its way of handling missing data. e.

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