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| Content Provider | ACM Digital Library |
|---|---|
| Author | Fang, Ting Hong, Xiaoyan |
| Abstract | User-centric WiFi traces1 are those traces that are actively collected by users in WiFi networks. Users carry mobile devices and these devices scan, collect and record the data (such as MAC address) of nearby APs at each timestamp. Missed information in the traces poses challenge for discovering mobility behaviors. Specifically, the real geographic locations of access points (APs) are important information that the data does not offer. However, it does not mean these traces are useless, we still can extract meaningful information related to mobility behaviors. First, we propose to discuss campus life in two behavioral categories: Study-Work (SW) and Entertainment-Recreation (ER). And use a new selection rule to divide APs into the two categories. Then we speculate on the behavior of people (SW or ER) at different times based on the two sets. Second, we establish two types of cells representing SW and ER, and then integrate the traces into different cells. Lastly, we introduce visual analytic methods (ST path and flow map) to reveal both general and detailed mobility behaviors. |
| Starting Page | 76 |
| Ending Page | 80 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450350242 |
| DOI | 10.1145/3077286.3077306 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-04-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | User-centric wifi traces Visual analytic methods Mobility behaviors Campus life |
| Content Type | Text |
| Resource Type | Article |
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