A vehicular ad hoc network (VANET) is a mobile ad hoc network in which network nodes are vehicles—most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organized nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, information dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming—the lack of realistic vehicular channel modeling—is identified. The importance of a rigorous and standardized performance evaluation regime utilizing realistic vehicular channel models is demonstrated.

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A Comparative Survey of VANET Clustering Techniques

As a hierarchical network architecture, the cluster architecture can improve the routing performance greatly for vehicular ad hoc networks (VANETs) by grouping the vehicle nodes However, the existing clustering algorithms only consider the mobility of a vehicle when selecting the cluster head The rapid mobility of vehicles makes the link between nodes less reliable in cluster A slight change in the speed of cluster head nodes has a great influence on the cluster members and even causes the cluster head to switch frequently These problems make the traditional clustering algorithms perform poorly in the stability and reliability of the VANET A novel passive multi-hop clustering algorithm (PMC) is proposed to solve these problems in this paper The PMC algorithm is based on the idea of a multi-hop clustering algorithm that ensures the coverage and stability of cluster In the cluster head selection phase, a priority-based neighbor-following strategy is proposed to select the optimal neighbor nodes to join the same cluster This strategy makes the inter-cluster nodes have high reliability and stability By ensuring the stability of the cluster members and selecting the most stable node as the cluster head in the N-hop range, the stability of the clustering is greatly improved In the cluster maintenance phase, by introducing the cluster merging mechanism, the reliability and robustness of the cluster are further improved In order to validate the performance of the PMC algorithm, we do many detailed comparison experiments with the algorithms of N-HOP, VMaSC, and DMCNF in the NS2 environment

New Multi-Hop Clustering Algorithm for Vehicular Ad Hoc Networks

Clustering in vehicular ad hoc networks

Quality of Experience, Mean Opinion, Score, Metrics, Statistics, Quantiles, Acceptability, Rating, distribution

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QoE beyond the MOS: an in-depth look at QoE via better metrics and their relation to MOS

Due to the widespread use of encryption in Over-The-Top video streaming traffic, network operators generally lack insight into application-level quality indicators (e.g., video quality levels, buffer underruns, stalling duration). They are thus faced with the challenge of finding solutions for monitoring service performance and estimating customer Quality of Experience (QoE) degradations based solely on passive monitoring solutions deployed within their network. We address this challenge by considering the concrete case of YouTube, whereby we present a methodology for the classification of end users' QoE when watching YouTube videos, based only on statistical properties of encrypted network traffic. We have developed a system called YouQ which includes tools for monitoring and analysis of application-level quality indicators and corresponding traffic traces. Collected data is then used for the development of machine learning models for QoE classification based on computed traffic features per video session. To test the YouQ system and methodology, we collected a dataset corresponding to 1060 different YouTube videos streamed across 39 different bandwidth scenarios, and tested various classification models. Classification accuracy was found to be up to 84% when using three QoE classes ("low", "medium" or "high") and up to 91% when using binary classification (classes "low" and "high"). To improve the models in the future, we discuss why and when prediction errors occur. Moreover, we have analysed YouTube's adaptation algorithm, thus providing valuable insight into the logic behind the quality level selection strategy, which may also be of interest in improving future QoE estimation algorithms.

A machine learning approach to classifying YouTube QoE based on encrypted network traffic

This paper proposes a robust Criticality-based Clustering Algorithm (CCA) for Vehicular Ad Hoc NETworks (VANETs) based on the concept of network criticality. Network criticality is a global metric on an undirected graph, that quantifies the robustness of the graph against environmental changes such as topology. In this paper, we localize the notion of network criticality and apply it to control cluster formation in the vehicular wireless network. We use the localized notion of node criticality together with a universal link measure, Link Expiration Time (LET), to derive a distributed multi-hop clustering algorithm for VANETs. Simulation results show that the proposed CCA forms robust cluster structures.

Robust clustering for connected vehicles using local network criticality

Consumer acceptance is of great interest in the adoption of novel multimedia products and services. A number of factors can greatly influence the customer experience during a video session, impacting the acceptability of the product or service. Factors such as the Technical Quality (TQ) which covers the technical aspects of the signal quality can be controlled from the network provider. On the other hand, the network provider has no control on the subject's level of interest in a video, the Content Quality (CQ). Together TQ and CQ influence the Overall eXperience (OX) of the subject and, in the case of Over The Top (OTT) video, in a video session. In this paper, we present results from a user subjective study in which the impact of TQ and OX on the acceptability was investigated for OTT video sessions. To minimize the CQ impact on OX, the videos were carefully selected to have relatively neutral content. We assess the impact of TQ and OX on acceptability for videos containing impairment and failure events during lifecycle, thus affecting Accessibility, Retainability and Integrity. Our experimental results indicate that TQ and OX have a strong impact on acceptability.

Acceptability and Quality of Experience in over the top video

Quality of Experience (QoE) is a crucial guiding factor for network management of an end-to-end service session. The network provider can control the resources allocated to sessions and in doing so, influence the Technical Quality (TQ), which covers the technical aspects of signal quality during the session. On the other hand, the network provider has no control over the Content Quality (CQ), which pertains to the user's level of interest in a particular video. Together TQ and CQ influence the Overall eXperience (OX) in a session. In this paper, we present results from a user subjective study in which the impact of TQ and CQ on OX was investigated for Over-The-Top (OTT) video sessions from the perspective of a network provider. This perspective places a focus on those elements of QoE that can be controlled by the provider. Various studies have shown that very high interest in a content can strongly influence QoE independent of other factors, so our study uses videos that are neutral with respect to content. We assess the TQ, CQ and OX for video sessions that contain Integrity impairments (in the form of image freezing) and failures in terms of session Accessibility and Retainability. Our findings indicate that TQ and CQ have a strong impact on OX in the presence of impairments, but no failures. On the other hand, TQ is the main determinant of OX when failures are present.

Impact of technical and Content Quality on Overall Experience of OTT video

Accurate Quality of Experience measurement for streaming video has become more crucial with the increase in demand for online video viewing. Quantifying video Quality of Experience is a challenging task. Significant efforts to quantify video Quality of Experience have primarily focused on the measurement of Quality of Experience for videos with network and compression related impairments. These impairments, however, may not always be the only main factors affecting Quality of Experience in an entire video viewing session. In this paper, we evaluate Quality of Experience for entire video viewing sessions, from the beginning to the end. In doing so, we evaluate videos with temporary interruptions as well as those with permanent interruptions or failures. We consider two types of failures, namely Accessibility and Retainability failures, and present the results of two subjective studies. These results indicate: (a) Accessibility and Retainability failures are rated lower compared to temporary interruption impairments; (b) Accessibility failures are rated close to the lowest value on the rating scale; and (c) the traditionally used 5-point scale to measure video Quality of Experience is not sufficient in the presence of Accessibility and Retainability failures.

Video quality of experience in the presence of accessibility and retainability failures

Measurement of video Quality of Experience is needed to enable telecommunication service operators to provide acceptable video viewing experience to their customers Without accurate Quality of Experience measurement, it is hard to predict customer satisfaction Accessibility and Retain ability failures are important factors that can affect the experience of viewing video on the Internet Most of the published work in the assessment of Video Quality of Experience has focused on the quality of video viewing sessions without considering the impact of Accessibility and Retain ability failures In this paper, we focus on the Quality of Experience for video sessions in the presence of Retain ability failures We primarily evaluate video Quality of Experience for videos with temporary interruptions, called Integrity impairments, as well as with permanent interruptions called Retain ability failures A subjective study was conducted to observe the effect of Retain ability failure on video Quality of Experience The results of the subjective study suggest that videos with Retain ability failures are rated much lower than videos without any failure An initial linear model for QoE of videos subject to Retain ability failures is introduced

Weiwei Li

Papers

Robust clustering for connected vehicles using local network criticality

Acceptability and Quality of Experience in over the top video

Impact of technical and Content Quality on Overall Experience of OTT video

Video quality of experience in the presence of accessibility and retainability failures

Impact of Retainability Failures on Video Quality of Experience