Name
Abstract | ||
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Deployment and management of large-scale mobile edge computing infrastructure in 5G networks has created a major challenge for mobile operators. The ability to extract common users' trajectories (i.e., corridors) in mobile networks helps mobile operators to better manage and orchestrate the allocation of network resources. However, compared with other types of trajectories, mobile trajectories are coarse, and their granularity varies due to the inconsistent density of cell towers. To identify the underlying geographical corridors of users in mobile networks, we propose a hierarchical multi-scale trajectory clustering algorithm for corridor identification by analyzing the non-homogeneity of the spatial distribution of cell towers and users' movements. To measure trajectory similarity on different scales we propose a distance measure based on Hausdorff distance that considers the cell density distribution. Common corridors are represented as weighted graphs as the final results, which can not only highlight users' frequent paths but also users' movement pattern between cell towers. The proposed method is validated using real-life datasets provided by China Mobile. Results show that by considering the heterogeneity of mobile networks, our method can achieve the best performance with more than 10% improvement in clustering quality compared with state-of-the-art algorithms.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357384.3358157 | Proceedings of the 28th ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
distance measure, mobile networks, trajectory clustering | Data mining,Computer science,Trajectory clustering | Conference |
ISBN | Citations | PageRank |
978-1-4503-6976-3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Li | 1 | 581 | 109.68 |
| Sarah M. Erfani | 2 | 3 | 3.07 |
| Chien Aun Chan | 3 | 18 | 6.52 |
| Christopher Leckie | 4 | 2422 | 155.20 |