| Full text | |
| Author(s): |
Campolina, Andre
;
Boukerche, Azzedine
;
Loureiro, Antonio A. F.
;
Assoc Comp Machinery
Total Authors: 4
|
| Document type: | Journal article |
| Source: | MSWIM'19: PROCEEDINGS OF THE 22ND INTERNATIONAL ACM CONFERENCE ON MODELING, ANALYSIS AND SIMULATION OF WIRELESS AND MOBILE SYSTEMS; v. N/A, p. 5-pg., 2019-01-01. |
| Abstract | |
In this work, we apply information theory metrics to car trips logged by volunteers around the world and use quantifiers such as location entropy to reveal aspects of users' mobility, like the context in which trips happened. The dataset used in this work was collected from the enviroCar project and contains not only location logs but also sensor readings associated with each location. Information theory measurements can also reveal relationships between sensor measurements in order to reveal rare occurrences and reduce uncertainty. This work shows that it is possible to differentiate driving contexts and capture relationships among sensors using location entropy and mutual information, respectively. These contributions pave the way for developing new features that may ultimately improve traffic context classification results. (AU) | |
| FAPESP's process: | 15/24494-8 - Communications and processing of big data in cloud and fog computing |
| Grantee: | Nelson Luis Saldanha da Fonseca |
| Support Opportunities: | Research Projects - Thematic Grants |