Target RSU Selection with Low Scanning Latency in WiMAX-enabled Vehicular Networks

Ahmed, Syed Hassan; Kim, Dongkyun

doi:10.1007/s11036-015-0578-3

Cited by 15 publications

(3 citation statements)

References 15 publications

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“…These characteristics include: (i) each vehicle is turned into a router or wireless node with the capability to connect and become a part of a network in the range of ∼100–300 m, (ii) network topology changes as the vehicles fall out of range or new vehicles join the network, thus making the network density vary according to traffic density [2]. For example, network density can be very high when in a traffic jam or very low in suburban traffic [3], (iii) mobility in a VANET environment is predictable because vehicles are constrained by road signs and traffic lights and how they can respond to other moving vehicles [4], (iv) as vehicles are equipped with long‐life batteries, power is not a critical challenge as it is in MANET [5], (v) some VANET applications have strict delay requirements to prevent drivers from possible accidents [6] and (vi) VANET leverages different types of wireless technologies (such as wireless fidelity, 802.11p, worldwide interoperability for microwave access, third generation among others), that had been extensively studied [7, 8]. These wireless technologies are an important component of intelligent transportation systems [9].…”

Section: Vanet and Big Datamentioning

confidence: 99%

Solving vehicular ad hoc network challenges with Big Data solutions

2016

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The concept of vehicular ad hoc networks (VANETs) has emerged as an efficient way to improve the performance of transportation systems, enhance travel security and help address traffic control problems that modern society is facing today. Large amounts of VANET data are typically collected from highly heterogeneous sources paving the way for a new era of VANET-Big Data. This data must be processed rapidly, efficiently and cost-effectively by different users and systems to make timely decisions about traffic and transportation. VANET-Big Data is causing a shift from technology-driven to data-driven VANETs. The authors analyse and discuss Big Data solutions that can be leveraged to address some of the emerging challenges of VANETs.

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Section: Vanet and Big Datamentioning

confidence: 99%

Solving vehicular ad hoc network challenges with Big Data solutions

2016

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“…vehicles) when nodes often move from one AP coverage area to another AP along their travel route using WiMAX, Wi‐Fi, DSRC or cellular networks or using any combination of heterogeneous wireless networks [e.g. 29, 30].…”

Section: Related Workmentioning

confidence: 99%

Process for evaluating the data transfer performance of wireless traffic sensors for real‐time intelligent transportation systems applications

Zhou

Dey

Chowdhury

et al. 2016

IET Intelligent Transport Systems

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“…Vehicles move at high speed, and many obstructions frequently occur that cause frequent connection loss [3]. Moreover, lack of infrastructure support for the global IP assignment is also a vital issue in deploying VANETs over IP [4]. For example, the vehicle moves at high speed, and if a vehicle moves from one subnet to another subnet, a new IP address must be assigned within the new subnet.…”

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confidence: 99%

NameCent: Name Centrality-Based Data Broadcast Mitigation in Vehicular Named Data Networks

Ali

Lim

2021

IEEE Access

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Named Data Networking (NDN), an extension of the Content-centric network (CCN), is not an unfamiliar word. It is considered as one of the promising future network architecture. NDN focuses on naming the content regardless of end-to-end device addresses. NDN has been adopted for vehicular ad hoc networks (VANETs), and it has promising results. Though NDN has robust architecture, many challenges still exist, including consumer and producer mobility, naming, and Interest and data packet flooding. In vehicular NDN, the consumer vehicle broadcasts an Interest packet for the required content. The producer vehicle or an intermediate vehicle that contains the required content in its cache replies with the data packet after receiving the Interest packet. The data packet reaches the consumer vehicle via simple broadcasting. This data broadcast creates congestion in the network and needs to be mitigated. In this work, we coin a new term called name centrality, and using it along with received signal strength, we have proposed a novel scheme to control data broadcast storm in vehicular NDN. The scheme uses the mentioned two parameters to allow only a subset of potential forwarding vehicles to rebroadcast the data packet instead of all the vehicles which receive the data packets. This reduces the copies of data packets processed and propagated in the network and hence alleviates congestion. The scheme is evaluated through extensive simulation under different scenarios, and it outperforms the legacy schemes. The comparison has been made in terms of copies of total data packets processed, Interest satisfaction delay, and Interest satisfaction rate. Compared to other approaches, the proposed scheme reduced the average copies of data packets processed by 55% and 20% with varying Interest packet rates and 58% and 19% with varying vehicle speed.

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Target RSU Selection with Low Scanning Latency in WiMAX-enabled Vehicular Networks

Cited by 15 publications

References 15 publications

Solving vehicular ad hoc network challenges with Big Data solutions

Solving vehicular ad hoc network challenges with Big Data solutions

Process for evaluating the data transfer performance of wireless traffic sensors for real‐time intelligent transportation systems applications

NameCent: Name Centrality-Based Data Broadcast Mitigation in Vehicular Named Data Networks

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