Name
Abstract | ||
|---|---|---|
Fine-grained dynamic population estimation is in an increasingly high demand as it has numerous applications in wireless network engineering, urban planning, location-based services and mobile applications, and advertisement. In this paper, we introduce a framework that dynamically estimates the wireless subscriber population of an arbitrary fine-grained area based on the current cellular phone usage. This framework takes advantage of strong regularities, low variance, and low information entropy in human mobility and phone usage patterns; thus simplifying the estimation for wireless carriers and other big entities while maintaining a high accuracy. We implemented our `regularity-based' framework using empirical data. Comparison with experimentally collected data shows a significant improvement in the accuracy of population estimation compared to population count based on cellular phone usage. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/GLOCOM.2012.6503942 | Global Communications Conference |
Keywords | Field | DocType |
cellular radio,subscriber loops,cellular phone,location-based services,mobile applications,regularity-based wireless subscriber population estimation,urban planning,wireless network engineering,cellular networks,population estimation,wireless usage | Mobile identification number,Population,Wireless network,Wireless,Computer science,Computer network,Wireless WAN,Real-time computing,Phone,Wi-Fi array,Entropy (information theory) | Conference |
ISSN | ISBN | Citations |
1930-529X E-ISBN : 978-1-4673-0919-6 | 978-1-4673-0919-6 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sara Gatmir-Motahari | 1 | 2 | 0.70 |
| Kosol Jintaseranee | 2 | 0 | 0.34 |
| Phyllis Reuther | 3 | 2 | 0.70 |
| Hui Zang | 4 | 1052 | 77.25 |