Mask Based Mobility Model A new mobility model with smooth trajectories

Joumaa, Chibli; Caminada, Alexandre; Lamrous, Sid

doi:10.1109/wiopt.2007.4480072

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…Since SRM does not consider the geographical features of the zones, the waypoints could Thus, the SRM model is not an appropriate option for outdoor IoT applications [180]. In this regard, the authors in [182] have came up with a novel mobility model, which tries to increase the precision of the walker's movement in comparison with the SRM. Furthermore, in the SRM model, the mobile nodes may change their velocity during the speed transition.…”

Section: ) Smooth Mobility Model [71]mentioning

confidence: 99%

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

et al. 2020

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With the widespread use of IoT applications and the increasing trend in the number of connected smart devices, the concept of routing has become very challenging. In this regard, the IPv6 Routing Protocol for Low-power and Lossy Networks (PRL) was standardized to be adopted in IoT networks. Nevertheless, while mobile IoT domains have gained significant popularity in recent years, since RPL was fundamentally designed for stationary IoT applications, it could not well adjust with the dynamic fluctuations in mobile applications. While there have been a number of studies on tuning RPL for mobile IoT applications, but still there is a high demand for more efforts to reach a standard version of this protocol for such applications. Accordingly, in this survey, we try to conduct a precise and comprehensive experimental study on the impact of various mobility models on the performance of a mobility-aware RPL to help this process. In this regard, a complete and scrutinized survey of the mobility models has been presented to be able to fairly justify and compare the outcome results. A significant set of evaluations has been conducted via precise IoT simulation tools to monitor and compare the performance of the network and its IoT devices in mobile RPL-based IoT applications under the presence of different mobility models from different perspectives including power consumption, reliability, latency, and control packet overhead. This will pave the way for researchers in both academia and industry to be able to compare the impact of various mobility models on the functionality of RPL, and consequently to design and implement application-specific and even a standard version of this protocol, which is capable of being employed in mobile IoT applications.

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Section: ) Smooth Mobility Model [71]mentioning

confidence: 99%

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

et al. 2020

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“…The area is divided into square cells, each characterized by the types of structure located in the cell (roads, buildings, forest...), cell altitude and cell attraction power for vehicles. The mobility model we used is V-MBMM (VehicularMask Based Mobility Model) [8] which is a combination of MBMM [9][10] and IDM (Intelligent Driver Model). This model simulates vehicles displacement in urban area taking into consideration the characteristics of the environment.…”

Section: Propagation Modelmentioning

confidence: 99%

Vehicular radio connectivity in urban environment

Ali

Gondran

Caminada

et al. 2010

2010 7th International Symposium on Wireless Communication Systems

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-In this paper we present a study of mobile connectivity for vehicular network in urban environment. The considered area is a real map divided into equally sized cells, each characterized by a set of information including the type of structure in the cell, cell altitude and cell attraction for vehicles. These characteristics are taken into account in two models: vehicular mobility model and electromagnetic wave propagation model. We study the impact of nodes density and obstacles present in the environment on network connectivity based on radio links. Graphs representing radio connections between vehicles are periodically generated. We examine four metrics that are nodes degree, connections duration, dominated nodes rate and cluster formation. I. INTRODUCTIONIn recent years, several researches have been conducted on inter-vehicle communication systems. According to that one infrastructure is used or not, two communication types may be distinguished: Vehicle-to-Infrastructure (V2I) communications where vehicles are connected to stations located on roadsides for information acquisition, information transmission and internet access; and Vehicle-to-Vehicle (V2V) communications, in which vehicles exchange information without relying on any given infrastructure, they collaborate to form at each time a dynamic distributed system called VANET (Vehicular Ad hoc NETwork).In ad hoc networks, the possibility of establishing communications and the quality of exchanged messages depend on the radio connectivity between nodes. One factor that affects considerably this connectivity is nodes motion, particularly when nodes move at high speeds as it is the case for vehicular networks. Several models have been proposed to simulate nodes mobility in MANET [1]. These models ignore the key characteristics of vehicular traffic, mainly the constraints on nodes movement and the interactions between neighboring nodes; they are thus not suitable for VANET. Aiming to reflect as closely as possible the real behavior of vehicular traffic, new models have been proposed [2]. Depending on the level of detail considered, these models may be classified into two categories: macroscopic models that take into account parameters such as road topology, roads attributes, traffic signs and traffic characteristics (density, flow etc.) and microscopic models that focus on the individual behavior of each vehicle and try to model features such as speed, acceleration, braking and interactions between vehicles.

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“…Random Waypoint Mobility Model [1,2], Random Walk Mobility Model [3], Normal Walk Mobility Model [4,5], Smooth Mobility Model [6,7], are the random models. And the terrain aware models are the Markovian Movement Model [8,9], Normal Markovian Mobility Model and the Mask Based [10,11,12] Mobility Model. Based on these simulations an evaluation of each model is done by quantifying its characteristics and evaluating them in metrics.…”

Section: Introductionmentioning

confidence: 99%

A Simulation Based Mobility Models Comparative Study

Joumaa

Moalic

Lamrous

et al. 2009

Proceedings of the Second International ICST Conference on Simulation Tools and Techniques

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This paper presents a comparative study between several mobility models. Simulation environment is a time variant real representation of a city based on GIS, integrating all geographical and socio-economical information relative to this city. Simulation results are the basis of the comparative analysis between the models. This comparison is made by setting metrics evaluating individual and population displacement and that by quantifying each model's degree of realism.

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Mask Based Mobility Model A new mobility model with smooth trajectories

Cited by 10 publications

References 13 publications

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

Vehicular radio connectivity in urban environment

A Simulation Based Mobility Models Comparative Study

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