Smart Collection of Real-Time Vehicular Mobility Traces

Ibadah, Nisrine; Minaoui, Khalid; Rziza, Mohammed; Oumsis, Mohammed; Benavente-Peces, César

doi:10.3390/fi10080078

Cited by 7 publications

(2 citation statements)

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“…With this purpose, these models are described by equations which take into account physical laws of motion. On the other hand, some models are inspired by real mobility traces in order to model real-life movements (Stegmann et al 2018;Ibadah et al 2018a). Notwithstanding, in all cases, the major challenge is how to achieve the best possible performance for wireless Communications in the presence of frequent mobility (Batabyal and Bhaumik 2015).…”

Section: Related Workmentioning

confidence: 99%

Flexible synthetic mobility modeling to discover trajectories for complex areas of mobile wireless networks

Ibadah

Rziza

Minaoui

et al. 2019

J Ambient Intell Human Comput

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Mobility modeling is one of the most influential pillars in improving the performance of wireless networks. Understanding mobility features is relevant for the design and analysis of proper motion schemes for any network. Up to now, a variety of entity mobility models have been suggested which do not take into account all the characteristics of real-life movements (time, space, environment). In order to obtain an improved model that overcomes such limitations, in this paper a new hybrid synthetic entity mobility model is proposed, called the Maze mobility model (MzMM). It takes into consideration the timeline history, spatial dependencies, and geographical restrictions as well. At the same time, it respects the laws of motion to reflect actual scenarios using a flexible discovery mechanism, where it permits nodes arriving at predefined destinations following the most appropriate trajectories in the presence of several obstacles. This approach allows mobile nodes to move correctly even in the presence of múltiple mobility constraints. The significance of this research is that the new approach considers a realistic combination of parameters to achieve a flexible and robust mobility model that can be applied for autonomous or human mobility, even in complex environments to provide optimized performances for networks, as demonstrated by its high performance results.

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Section: Related Workmentioning

confidence: 99%

Flexible synthetic mobility modeling to discover trajectories for complex areas of mobile wireless networks

Ibadah

Rziza

Minaoui

et al. 2019

J Ambient Intell Human Comput

Self Cite

View full text Add to dashboard Cite

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“…Several traces are previously implemented to model real-life movements. In [14], the authors propose a new procedure aimed at collecting traces, while deployment phase failures are avoided, which improves the reliability of data. The introduced procedure makes possible the complete generation of traces with a minimum of damages without the need to recover mobile devices or losing them.…”

Section: Preliminariesmentioning

confidence: 99%

Deep Validation of Spatial Temporal Features of Synthetic Mobility Models

et al. 2018

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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.

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