Nested Dirichlet process for collaborative mobility modeling

• Published in: 2009 International Conference on Machine Learning and Cybernetics Vol. 5; pp. 3095 - 3101
• Main Authors: Yi-Qun Ding, Zhen Zhang, Bin Xu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2009
• Subjects: Ad hoc networks; Bayesian methods; Cities and towns; Collaboration; Collaborative filtering; Cybernetics; Hidden Markov models; Hierarchical Bayesian model; Land mobile radio cellular systems; Machine learning; Mathematical model; Mobility modeling; Nested Chinese restaurant process; Nonparametric Bayesian model; Predictive models
• ISBN: 9781424437023; 1424437024
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2009.5212623

Mobility modeling is the mathematical modeling of mobile users' (cars, cell phone users) movement patterns. The resulting model not only provides us with an understanding of past mobile user movements, but also enables us to predict how a mobile user might move in the future. It has been found useful in both infrastructure-based wireless network and ad hoc network for protocol evaluation, resource planning, etc. A nonparametric hierarchical Bayesian approach is proposed in this paper for extracting hierarchical mobility patterns from mobile user traces. Experiment results show that the proposed method is able to generate a hierarchical mobility model that better reflect the mobility pattern structure in many scenarios. It also has better future movement prediction compared to the hidden Markov model.