(Downloads - 0)
For more info about our services contact : help@bestpfe.com
Table of contents
1 introduction
1.1 Context and motivation
1.2 Problem statement
1.2.1 What about investigating routine instead of purely mobility?
1.2.2 Can we identify common traffic behavior among mobile subscribers?
1.2.3 And so what?
1.3 Contributions of this thesis
1.3.1 Routine characterization of human mobility
1.3.2 Mobile data traffic profiling and synthetic generation
1.3.3 Traffic-and-mobility-aware hotspot deployment for data offloading
1.4 Thesis outline
2 on the mobility and content analysis
2.1 Dataset knowledge extraction
2.2 Mobility insights
2.3 Data traffic insights
2.4 Conclusions
3 context analysis
3.1 Introduction
3.2 Rationale
3.2.1 System model
3.3 Mobility Dynamics
3.3.1 Visit behavior
3.3.2 Displacement behavior
3.3.3 Spatiotemporal behavior
3.4 Conclusions
4 content analysis
4.1 Introduction
4.2 Dataset
4.2.1 Traffic dynamics
4.2.2 Temporal dynamics
4.2.3 Age and gender dynamics
4.3 Subscriber profiling methodology
4.3.1 Similarity computation
4.3.2 Subscriber clustering and classification
4.3.3 Subscriber profiles
4.3.4 Profile’s age and gender
4.4 Measurement-driven traffic modeling
4.4.1 Fitting empirical distributions
4.4.2 Synthetic subscriber generation
4.4.3 Synthetic traffic model evaluation
4.5 Discussion
4.6 Conclusions
5 study case
5.1 Introduction
5.2 Related work
5.3 Proposal
5.3.1 Graph creation
5.3.2 Metrics
5.3.3 Synthetic traffic model
5.3.4 Objective formalization
5.4 Performance evaluation
5.4.1 Comparison
5.4.2 Offloaded traffic
5.5 Discussion
5.6 Conclusions
6 conclusions and future horizons
6.1 Summary
6.2 Future horizons
6.2.1 Short-term
6.2.2 Long-term
a appendix
a.1 Classes and categories for Points of Interest
a.2 CDFs of the traffic parameters in peak and non-peak hours
a.3 Synthetic traffic generator algorithm
bibliography
