Comparison Survey of Mobility Models in Vehicular Ad-Hoc Network (VANET)

Tian, Junfang; Meng, Fudong

doi:10.1109/auteee50969.2020.9315583

Cited by 9 publications

(9 citation statements)

References 16 publications

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“…Each environment has its own mobility challenges. This is the reasons why there is an important number of research solutions [45,46,47] trying to handle the different mobility issues. There are many mobility models that are classified into A high data rate is required for advanced VANET applications, such as cooperative collision warning, traffic management, and entertainment [50,51].…”

Section: Architecturementioning

confidence: 99%

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Ameur

Lakas

Bachir

et al. 2022

Vehicular Communications

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Section: Architecturementioning

confidence: 99%

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Ameur

Lakas

Bachir

et al. 2022

Vehicular Communications

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“…We consider a randomly and uniformly distributed multisource multi-destination VN, with the aim to analyze the proposed routing techniques and compare them with the state-of-the-art. Each vehicle node follows the Random Waypoint Mobility Model (RWP) [24]. More specifically, vehicles are generated at random starting coordinates and then choose a target destination position.…”

Section: B Routing Problem Descriptionmentioning

confidence: 99%

Multi-Source Multi-Destination Hybrid Infrastructure-Aided Traffic Aware Routing in V2V/I Networks

et al. 2022

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The concept of the "connected car" offers the potential for safer, more enjoyable and more efficient driving and eventually autonomous driving. However, in urban Vehicular Networks (VNs) , the high mobility of vehicles along roads poses major challenges to the routing protocols needed for a reliable and flexible vehicular communications system. Thus, urban VNs rely on static Road-Side-Units (RSUs) to forward data and to extend coverage across the network. In this paper, we first propose a new Q-learning-based routing algorithm, namely Infrastructure-aided Traffic-Aware Routing (I-TAR), which leverages the static wired RSU infrastructure for packet forwarding. Then, we focus on the multi-source, multi-destination problem and the effect this imposes on node availability, as nodes also participate in other communications paths.

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“…Therefore, we should evaluate those protocols under realistic conditions, such as a mobility model capable of representing human behavior in a reasonable way. 4…”

Section: Related Workmentioning

confidence: 99%

“…Otherwise, the observations made from the simulation may be incorrect. 4 In addition, there are recent efforts to increase the accuracy of Wireless Sensor Networks (WSN) in combination with cloud computing, such as SensorCloudSim. 5 In consequence, it is important to also improve the accuracy in scenarios where sensors are attached to humans or other mobile entities using a scalable mobility model capable of representing the movement of real users.…”

Section: Introductionmentioning

confidence: 99%

TRAILS mobility model

Sarmiento

Förster

2022

SIMULATION

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According to state-of-the-art research, mobile network simulation is preferred over real testbeds, especially to evaluate communication protocols used in Opportunistic Networks (OppNet) or Mobile Ad hoc NETworks (MANET). The main reason behind it is the difficulty of performing experiments in real scenarios. However, in a simulation, a mobility model is required to define users’ mobility patterns. Trace-based models can be used for this purpose, but they are difficult to obtain, and they are not flexible or scalable. Another option is TRAce-based ProbabILiStic (TRAILS). TRAILS mimics the spatial dependency, geographic restrictions, and temporal dependency from real scenarios. In addition, with TRAILS, it is possible to scale the number of mobile users and simulation time. In this paper, we dive into the algorithms used by TRAILS to generate mobility graphs from real scenarios and simulate human mobility. In addition, we compare mobility metrics of TRAILS simulations, real traces, and another synthetic mobility model such as Small Worlds in Motion (SWIM). Finally, we analyze the performance of an implementation of the TRAILS model in computation time and memory consumption. We observed that TRAILS simulations represent the interaction among users of real scenarios with higher accuracy than SWIM simulations. Furthermore, we found that a simulation with TRAILS requires less computation time than a simulation with real traces and that a TRAILS graph consumes less memory than traces.

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Comparison Survey of Mobility Models in Vehicular Ad-Hoc Network (VANET)

Cited by 9 publications

References 16 publications

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Multi-Source Multi-Destination Hybrid Infrastructure-Aided Traffic Aware Routing in V2V/I Networks

TRAILS mobility model

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