Deep Validation of Spatial Temporal Features of Synthetic Mobility

Benavente Peces, César and Ibadah, Nisrine and Minaoui, Khalid and Rziza, Mohammed and Oumsis, Mohammed (2018). Deep Validation of Spatial Temporal Features of Synthetic Mobility. "Computers", v. 7 (n. 71); pp. 1-19. ISSN 2073-431X. https://doi.org/10.3390/computers7040071.

Description

Title: Deep Validation of Spatial Temporal Features of Synthetic Mobility
Author/s:
  • Benavente Peces, César
  • Ibadah, Nisrine
  • Minaoui, Khalid
  • Rziza, Mohammed
  • Oumsis, Mohammed
Item Type: Article
Título de Revista/Publicación: Computers
Date: 2018
ISSN: 2073-431X
Volume: 7
Subjects:
Freetext Keywords: mobility model; mobility metric; spatial node distribution; speed decay problem; density wave phenomenon; average neighbor percentage; mobile neighbors range
Faculty: E.T.S.I. y Sistemas de Telecomunicación (UPM)
Department: Teoría de la Señal y Comunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview

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.

More information

Item ID: 54981
DC Identifier: http://oa.upm.es/54981/
OAI Identifier: oai:oa.upm.es:54981
DOI: 10.3390/computers7040071
Official URL: https://www.mdpi.com/2073-431X/7/4/71
Deposited by: Memoria Investigacion
Deposited on: 16 Jan 2020 15:28
Last Modified: 16 Jan 2020 15:28
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM