Name
Abstract | ||
|---|---|---|
The development of wireless technology enables collecting massive human movement data based on mobile terminals, due to the close relation between the mobile terminal and human. In this paper, we design a new data collection way which based on wireless detection to capture the smart phone's Wi-Fi signal in our campus, and according to the new data collection method, a location prediction model T-PST based on Probabilistic Suffix Tree (PST) is proposed. The prediction model considers not only the spatial historical trajectories but also the corresponding probabilities about the time when objects appear. To evaluate our proposed prediction algorithm, the experiment was conducted along several months, using data collected from thousands of users that freely moved inside the numerous buildings existent in our campus. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-42553-5_38 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Location prediction,Suffix tree,Trajectory | Data mining,Data collection,Wireless,Computer science,Probabilistic suffix tree,Artificial intelligence,Suffix tree,Smart phone,Location prediction,Machine learning,Trajectory | Conference |
Volume | ISSN | Citations |
9784 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Li | 1 | 0 | 0.68 |
| Xinning Zhu | 2 | 57 | 10.74 |
| Jiansong Miao | 3 | 2 | 2.41 |