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Computers 2018, 7(4), 71; https://doi.org/10.3390/computers7040071

Deep Validation of Spatial Temporal Features of Synthetic Mobility Models

1
LRIT Laboratory, Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of Sciences, Mohammed V University in Rabat, Rabat 1014 RP, Morocco
2
High School of Technology, Mohammed V University in Rabat, Sale 11000, Morocco
3
ETS Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madria, Ctra Valencia, km 7, 28031 Madrid, Spain
*
Author to whom correspondence should be addressed.
Received: 30 October 2018 / Revised: 12 December 2018 / Accepted: 13 December 2018 / Published: 16 December 2018
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Abstract

This paper analyzes the most relevant spatial-temporal stochastic properties of benchmark synthetic mobility models. Each pattern suffers from various mobility flaws, as will be shown by the models’ validation. A set of metrics is used to describe mobility features, such as the speed decay problem, the density wave phenomenon, the spatial node distribution, and the average neighbor percentage. These metrics have already been validated for the random waypoint mobility model (RWPMM), but they have not yet been verified for other mobility patterns that are most frequently used. For this reason, this investigation attempts to deeply validate those metrics for other mobility models, namely the Manhattan Grid mobility, the Reference Point Group mobility, the Nomadic Community mobility, the Self-Similar Least Action Walk, and SMOOTH models. Moreover, we propose a novel mobility metric named the “node neighbors range”. The relevance of this new metric is that it proves at once the set of outcomes of previous metrics. It offers a global view of the overall range of mobile neighbors during the experimental time. The current research aims to more rigorously understand mobility features in order to conduct a precise assessment of each mobility flaw, given that this fact further impacts the performance of the whole network. These validations aim to summarize several parameters into 18,126 different scenarios with an average of 486 validated files. An exhaustive analysis with details like those found in this paper leads to a good understanding of the accurate behaviors of mobility models by displaying the ability of every pattern to deal with certain topology changes, as well as to ensure network performances. Validation results confirm the effectiveness and robustness of our novel metric. View Full-Text
Keywords: mobility model; mobility metric; spatial node distribution; speed decay problem; density wave phenomenon; average neighbor percentage; mobile neighbors range mobility model; mobility metric; spatial node distribution; speed decay problem; density wave phenomenon; average neighbor percentage; mobile neighbors range
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ibadah, N.; Minaoui, K.; Rziza, M.; Oumsis, M.; Benavente-Peces, C. Deep Validation of Spatial Temporal Features of Synthetic Mobility Models. Computers 2018, 7, 71.

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