A group mobility model for ad hoc wireless networks
MSWiM '99 Proceedings of the 2nd ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
On the minimum node degree and connectivity of a wireless multihop network
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Credible mobile ad hoc network simulation-based studies
Credible mobile ad hoc network simulation-based studies
Designing mobility models based on social network theory
ACM SIGMOBILE Mobile Computing and Communications Review
Mobility pattern recognition in mobile ad-hoc networks
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
Using mobile phones to determine transportation modes
ACM Transactions on Sensor Networks (TOSN)
BonnMotion: a mobility scenario generation and analysis tool
Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques
Handbook of Mobile Ad Hoc Networks for Mobility Models
Handbook of Mobile Ad Hoc Networks for Mobility Models
SMOOTH: a simple way to model human mobility
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Degree of node proximity: a spatial mobility metric for manets
Proceedings of the 9th ACM international symposium on Mobility management and wireless access
On improving temporal and spatial mobility metrics for wireless ad hoc networks
Information Sciences: an International Journal
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In this paper we propose a set of mobility metrics, which are employed in the generation of supervised classification learning methods through the decision tree algorithm, with the goal to recognize user movement patterns in mobile ad hoc networks. Hundreds of scenarios produced by several well-known mobility models were employed for training and testing the supervised algorithms. The most suitable classification model showed an accuracy of 99.20% and Kappa index of 0.991, which indicates a high level of agreement between the classification model and real classification.