Evaluation of the IEEE 802.11p multi-channel operation in vehicular networks

Behravesh, Ehsun; Butler, Andrew

doi:10.7287/peerj.preprints.2633v1

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…In the early phases of VANET research, RSUs became an essential part of the VANET architecture (Behravesh & Butler, 2016). Models (Alagar & Wan, 2015;Biswas, Misic & Misic, 2011;Biswas, Sanzgiri & Upadhyaya, 2016;Li et al, 2013;Monir, Abdel-Hamid & El Aziz, 2013;Sedjelmaci & Senouci, 2015;Wu, Ma & Zhang, 2011) that used the RSU as the main authority for TM were proposed.…”

Section: Rsu-based Approachesmentioning

confidence: 99%

State-of-the-art IoV trust management a meta-synthesis systematic literature review (SLR)

Rehman

Hassan

Hooi

et al. 2020

PeerJ Computer Science

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In the near future, the Internet of Vehicles (IoV) is foreseen to become an inviolable part of smart cities. The integration of vehicular ad hoc networks (VANETs) into the IoV is being driven by the advent of the Internet of Things (IoT) and high-speed communication. However, both the technological and non-technical elements of IoV need to be standardized prior to deployment on the road. This study focuses on trust management (TM) in the IoV/VANETs/ITS (intelligent transport system). Trust has always been important in vehicular networks to ensure safety. A variety of techniques for TM and evaluation have been proposed over the years, yet few comprehensive studies that lay the foundation for the development of a “standard” for TM in IoV have been reported. The motivation behind this study is to examine all the TM models available for vehicular networks to bring together all the techniques from previous studies in this review. The study was carried out using a systematic method in which 31 papers out of 256 research publications were screened. An in-depth analysis of all the TM models was conducted and the strengths and weaknesses of each are highlighted. Considering that solutions based on AI are necessary to meet the requirements of a smart city, our second objective is to analyze the implications of incorporating an AI method based on “context awareness” in a vehicular network. It is evident from mobile ad hoc networks (MANETs) that there is potential for context awareness in ad hoc networks. The findings are expected to contribute significantly to the future formulation of IoVITS standards. In addition, gray areas and open questions for new research dimensions are highlighted.

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Section: Rsu-based Approachesmentioning

confidence: 99%

State-of-the-art IoV trust management a meta-synthesis systematic literature review (SLR)

Rehman

Hassan

Hooi

et al. 2020

PeerJ Computer Science

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“…Page 5 of 20 [27] similar to Distributed Coordination Function (DCF) in IEEE 802.11. One important difference between EDCA and DCF is that the inter-frame spacing interval in EDCA can be arbitrary (AIFS), allowing EDCA to shorten the frame spacing interval for delay-critical applications such as video streaming [6]. Two types of channels are specified for dedicated shortrange communications frequency band in WAVE: CCH (control channel) and SCH (service channel).…”

Section: Wave Enabling Communications In a Vfc Infrastructurementioning

confidence: 99%

Evaluating TCP performance of routing protocols for traffic exchange in street-parked vehicles based fog computing infrastructure

et al. 2020

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As most vehicles remain parked 95% of its time, this suggests that leveraging the use of On-board Units (OBUs) in parked vehicles would provide communication and computation services to other mobile and fixed nodes for delivery of services such as multimedia streaming, data storage and data processing. The nearby vehicles can form an infrastructure using IEEE 802.11p communication interface, facilitating communication, computation and storage services to the end users. We refer to this as a Vehicular Fog Computing (VFC) infrastructure. In this study, using NS-2 simulator, we investigate how six routing protocols consisting of two proactive routing protocols, Destination Sequence Destination Vector (DSDV) and Fisheye State Routing (FSR); two reactive routing protocols, Ad Hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR); and two geographic routing protocols, Distance Routing Effect Algorithm for Mobility (DREAM) and Location Aided Routing (LAR) perform when forwarding TCP traffic among the parked vehicles that form a VFC infrastructure in an urban street parking scenario. In order to reflect an urban street parking scenario, we consider a traffic mobility traces that are generated using SUMO in our simulation. To the best of our knowledge, this work is the first effort to understand how vehicle density, vehicle speed and parking duration can influence TCP in an urban street parking scenario when packet forwarding decision is made using proactive, reactive and geographic routing protocols. In our performance evaluation, positive results are observed on the influence of parking duration in parked vehicles as TCP performance in all routing protocols increases with longer parking duration. However, variable speed in parked vehicles and moving vehicles in an urban street parking scenario may not have significant influence on TCP performance, especially in case of reactive and proactive routing protocols. Further, our findings reveal that vehicle density in a VFC infrastructure can noticeably influence TCP performance. Towards the end of the paper, we delineate some important future research issues in order to improve routing performance in a street-parked vehicle based VFC infrastructure.

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“…Short safety messages are exchanged during CCH intervals, and infotainment data is transmitted during SCH intervals. Each interval is 50 ms. V2X devices only operate CCH or switch between CCH and SCH [Behravesh and Butler (2016); Chen, Jiang and Delgrossi (2009)]. The WLAN protocol is modified for DSRC, which is an IEEE 802.11p standard.…”

Section: V2x Communication V2x Communication Allows Vehicles To Exchamentioning

confidence: 99%

“…The connected vehicles use wireless V2X communication. The V2X suite includes Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Nomadic device (V2N), and Vehicle-to-Pedestrian (V2P) algorithms [Shrestha, Bajracharya and Nam (2018); Filippi, Moerman, Daalderop et al (2016); Behravesh and Butler (2016); Song (2017)]. This connectivity is crucially important for an intelligent vehicle.…”

Section: Introductionmentioning

confidence: 99%

A Data Download Method from RSUs using Fog Computing in Connected Vehicles

Kim¹,

Kim²

2019

Computers, Materials &Amp; Continua

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Communication is important for providing intelligent services in connected vehicles. Vehicles must be able to communicate with different places and exchange information while driving. For service operation, connected vehicles frequently attempt to download large amounts of data. They can request data downloading to a road side unit (RSU), which provides infrastructure for connected vehicles. The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU. Therefore, it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU. If the mobile network between a connected vehicle and an RSU has poor connection quality, the efficiency and speed of the data download from the RSU is decreased. This problem affects the quality of the user experience. Therefore, it is important for a connected vehicle to connect to an RSU with consideration of the network conditions in order to try to maximize download speed. The proposed method maximizes download speed from an RSU using a machine learning algorithm. To collect and learn from network data, fog computing is used. A fog server is integrated with the RSU to perform computing. If the algorithm recognizes that conditions are not good for mass data download, it will not attempt to download at high speed. Thus, the proposed method can improve the efficiency of high speed downloads. This conclusion was validated using extensive computer simulations.

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Evaluation of the IEEE 802.11p multi-channel operation in vehicular networks

Cited by 4 publications

References 12 publications

State-of-the-art IoV trust management a meta-synthesis systematic literature review (SLR)

State-of-the-art IoV trust management a meta-synthesis systematic literature review (SLR)

Evaluating TCP performance of routing protocols for traffic exchange in street-parked vehicles based fog computing infrastructure

A Data Download Method from RSUs using Fog Computing in Connected Vehicles

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