Experimental Synthesis of Routing Protocols and Synthetic Mobility Modeling for MANET

Ibadah, Nisrine; Minaoui, Khalid; Rziza, Mohammed; Oumsis, Mohammed

doi:10.5220/0006203601680173

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…Conversely, when the obtained lines have acceptable or big margin fluctuations, the minimum does not equal 0 and the maximum comes closer to the total number of nodes. This reflects the fact that the validated mobility model is more stable and steady with time that will lead to satisfactory outcomes in the performance analysis [3].…”

mentioning

confidence: 89%

“…Royer, Melliar-Smith, and Moser [9] noticed that, in random waypoint models, the density recurrently fluctuates with time, as depicted in Figure 3a, with a moderate fluctuation between 0 and 11 nodes. However, this issue at most affects the performance of other models [3]. This feature shows the independent mobility of mobile nodes.…”

Section: Density Wave Phenomenonmentioning

confidence: 99%

“…Different mobility patterns have in general different impacts on specific network protocols or applications [2]. The performance of mobile networks is remarkably impacted by several factors that further govern network performances: the packet delivery ratio, the average end-to-end delay, and throughput [3]. The mobility model represents one of these influential factors which shape the adopted motion strategy.…”

Section: Introductionmentioning

confidence: 99%

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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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mentioning

confidence: 89%

Section: Density Wave Phenomenonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Validation of Spatial Temporal Features of Synthetic Mobility Models

et al. 2018

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“…The relevance of mobility models relays on the fact that they are used for simulating and evaluating mobile wireless networks overall performance and to design and assess algorithms and protocols which are based on them. Frequent mobility represents one of those challenging issues where it is deeply examined by considering highly dynamic mobile networks, leading the network mobility models to aim at characterizing the nodes movement (Tomasini et al 2017;Ibadah et al 2017). Several researchers have demonstrated that the adopted motion model significantly impaets the whole network performance (Karamshuk et al 2011).…”

Section: Introductionmentioning

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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Experimental Synthesis of Routing Protocols and Synthetic Mobility Modeling for MANET

Cited by 2 publications

References 8 publications

Deep Validation of Spatial Temporal Features of Synthetic Mobility Models

Deep Validation of Spatial Temporal Features of Synthetic Mobility Models

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

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