Predicting mobility metrics through regression analysis for random, group, and grid-based mobility models in MANETs

Authors:
Elmano Ramalho Cavalcanti;Marco Aurelio Spohn
Affiliations:
Computing and Systems Department, Federal University of Campina Grande, Brazil;Computing and Systems Department, Federal University of Campina Grande, Brazil
Venue:
ISCC '10 Proceedings of the The IEEE symposium on Computers and Communications
Year:
2010

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Degree of node proximity: a spatial mobility metric for manets

Proceedings of the 9th ACM international symposium on Mobility management and wireless access

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Abstract

In Mobile ad hoc Networks (MANETs), some mobility metrics are directly related to the performance of routing protocols. Creating accurate predict models for mobility metrics is an important advance for designing better mobility-adaptive protocols. Through regression analysis, we propose predictive formulas for three mobility metrics: link duration, node degree, and network partitioning, considering a set of random, group, and grid-based mobility models. We propose specific derived parameters for group and grid-based models, and show that they are good predictors for the metric values. The results also show that link duration and node degree are more predictive for random and grid-based, and less predictive for group-based models.