Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm

Taherkhani, Nasrin; Pierre, Samuel

doi:10.1109/tits.2016.2546555

Cited by 178 publications

(102 citation statements)

References 40 publications

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“…To guarantee a reliable and timely delivery of various delay-sensitive safety-critical messages, such as BSMs, the vehicular networks need to have carefully designed congestion control strategies. Traditionally, there are five major categories of congestion control methods, namely rate-based, powerbased, carrier-sense multiple access/collision avoidance based, prioritizing and scheduling-based, and hybrid strategies [70], which adjust communications parameters, such as transmission power, transmission rates, and contention window sizes, etc., to meet the congestion control purposes.…”

Section: B Network Congestion Controlmentioning

confidence: 99%

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Liang

Hao

2019

IEEE Internet Things J.

224

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As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the heart of high mobility networks, are increasingly equipped with multiple advanced onboard sensors and keep generating large volumes of data. Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. In this article, we first identify the distinctive characteristics of high mobility vehicular networks and motivate the use of machine learning to address the resulting challenges. After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance. In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. Finally, some open issues worth further investigation are highlighted.

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Section: B Network Congestion Controlmentioning

confidence: 99%

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Liang

Hao

2019

IEEE Internet Things J.

224

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“…Application Methodology Yao et al [62] Location prediction based scheduling and routing Hidden Markov models Xue et al [63] Variable-order Markov models Zeng et al [64] Recursive least squares Karami et al [65] Network congestion control Feed forward neural network Taherkhani et al [66] k-means clustering Li et al [67] Load balancing Reinforcement learning Taylor et al [68] Network security LSTM Zheng et al [69] Virtual resource allocation Reinforcement learning Atallah et al [70], [71] Resource management Reinforcement learning Ye et al [57] Distributed resource management Reinforcement learning Kim et al [72] Vehicle trajectory prediction Reinforcement learning 1) Perception: assists in perceiving the nearby environment and recognizing objects; 2) Prediction: predicting the actions of perceived objects, i.e., how environmental actors such as vehicles and pedestrians will move; 3) Planning: route planning of vehicle, i.e., how to reach from point A to B; 4) Decision Making & Control: making decisions relating to vehicle movement, i.e., how to make the longitudinal and lateral decisions to control and steer the vehicle. These components are combined to develop a feedback system for enabling the phenomenon of self-driving without any human intervention.…”

Section: Authorsmentioning

confidence: 99%

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

Qayyum

Usama

Qadir

et al. 2020

IEEE Commun. Surv. Tutorials

182

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Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation-which will be fuelled by concomitant advances in technologies for machine learning (ML) and wireless communications-will enable a future vehicular ecosystem that is better featured and more efficient. However, there are lurking security problems related to the use of ML in such a critical setting where an incorrect ML decision may not only be a nuisance but can lead to loss of precious lives. In this paper, we present an in-depth overview of the various challenges associated with the application of ML in vehicular networks. In addition, we formulate the ML pipeline of CAVs and present various potential security issues associated with the adoption of ML methods. In particular, we focus on the perspective of adversarial ML attacks on CAVs and outline a solution to defend against adversarial attacks in multiple settings.

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“…Taherkhani and Pierre proposed a centralized and localized data congestion control strategy that includes detecting congestion, clustering messages, and controlling data congestion. The quality of transceiver channel is estimated to perceive the congested data in the channels.…”

Section: Analysis Of Handover Failures In Vanetmentioning

confidence: 99%

“…Taherkhani and It proposed a centralized and localized It spent long handover latency in Pierre 52,53 data congestion control strategy that dynamic VANET due to the includes detecting congestion, high mobility of the vehicles and clustering messages, and controlling high frequency of topology variations. data congestion.…”

Section: Approaches Features Limitationsmentioning

confidence: 99%

A comprehensive survey on handover management for vehicular ad hoc network based on 5G mobile networks technology

Ahmed

Alzahrani

2018

Trans Emerging Tel Tech

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Vehicular ad hoc network (VANET) is a style of high‐mobility mobile ad hoc network where the mobile nodes are vehicles traveling across a road or a street. VANET was proposed to provide safety, comfortability, reliability, and security of vehicles driving. One of the serious challenges is handover (HO) problem that occurs when a vehicle moves from one network area covered by a roadside unit (RSU) to another network area covered by a different RSU. The fast mobility of vehicles and the limited coverage area of the RSU (without overlapping signals) should be considered in providing a seamless HO. This paper presents a comprehensive survey on HO management for VANET based on the new features of fifth‐generation (5G) mobile network technologies. The 5G mobile network technology provides enhanced characteristics that will fit the requirements of real‐time video streaming and assist to design an effective solution for the HO problem. In addition, this paper presents the reasons for HO failures and the limitations of available HO failure solutions. Furthermore, it highlights the potential proposed solutions that will tackle the HO failures. This survey will open up the gate of developing an optimal solution based on 5G for HO problem on VANET that might be used in vehicles manufacturing.

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Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm

Cited by 178 publications

References 40 publications

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

A comprehensive survey on handover management for vehicular ad hoc network based on 5G mobile networks technology

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