A stable clustering algorithm based on affinity propagation for VANETs

Shahwani, Hamayoun; Bui, Toan Duc; Jeong, Jaehoon; Shin, Jitae

doi:10.23919/icact.2017.7890140

Cited by 14 publications

(6 citation statements)

References 7 publications

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“…In this study, a clustering analysis based on the Affinity Propagation (AP) algorithm [30] is employed to cluster similar HRTF sets together using the results shown in Fig. 5(d gorithm has been shown to provide a robust engineering solution to clustering problems in audio signal processing, such as speaker clustering [31] and acoustic control of crosstalk [32] and in VANETs for mobile telecommunications [33].…”

Section: Hrtf Clusteringmentioning

confidence: 99%

HRTF Clustering for Robust Training of a DNN for Sound Source Localization

O’Dwyer¹,

Boland²

2022

J. Audio Eng. Soc.

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This study shows how spherical sound source localization of binaural audio signals in the mismatched head-related transfer function (HRTF) condition can be improved by implementing HRTF clustering when using machine learning. A new feature set of cross-correlation function, interaural level difference, and Gammatone cepstral coefficients is introduced and shown to outperform state-of-the-art methods in vertical localization in the mismatched HRTF condition by up to 5%. By examining the performance of Deep Neural Networks trained on single HRTF sets from the CIPIC database on other HRTFs, it is shown that HRTF sets can be clustered into groups of similar HRTFs. This results in the formulation of central HRTF sets representative of their specific cluster. By training a machine learning algorithm on these central HRTFs, it is shown that a more robust algorithm can be trained capable of improving sound source localization accuracy by up to 13% in the mismatched HRTF condition. Concurrently, localization accuracy is decreased by approximately 6% in the matched HRTF condition, which accounts for less than 9% of all test conditions. Results demonstrate that HRTF clustering can vastly improve the robustness of binaural sound source localization to unseen HRTF conditions.

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Section: Hrtf Clusteringmentioning

confidence: 99%

HRTF Clustering for Robust Training of a DNN for Sound Source Localization

O’Dwyer¹,

Boland²

2022

J. Audio Eng. Soc.

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“…In contrast to those conventional clustering schemes, an advanced, fully distributed VANET clustering scheme was proposed [23] by Hassanabadi et al [24] and by Shahwani et al [25] based on a mathematically well-proven concept called Affinity Propagation, which was originally published in Science by Frey and Dueck [26]. In these initial Affinity Propagation Clustering (APC) applications [24], however, the metrics used in the similarity function are vehicles' distances in the future prediction time point set to 30 sec, which means individual vehicles' positions are forecasted in 30 sec later.…”

Section: Related Work a Vanet Clustering Techniquesmentioning

confidence: 99%

Multi-Dimensional Affinity Propagation Clustering Applying a Machine Learning in 5G-Cellular V2X

et al. 2020

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Cellular systems are facing the ever-increasing demand for vehicular communication aimed at applications such as advanced driving assistance and ultimately fully autonomous driving. Cellular Vehicle to Anything (C-V2X) has become more applicable with the release of the first sets of 5G (5 th Generation) system specifications. The highly capable 5G systems will therefore support even a larger number of moving objects. This study aims to present a sophisticated clustering mechanism that enables cellular systems to accommodate a massive number of moving Machine Type Communication (MTC) objects with a minimum set of connections while maintaining system scalability. Specifically, we proposed Normalized Multi Dimension-Affinity Propagation Clustering (NMDP-APC) scheme and applied it for Vehicular Ad hoc Network (VANET) clustering. For VANET clustering formation, our study employed Machine Learning (ML) to determine the granularity, i.e., the size and span of clusters desirable for use in dynamic motion environments. The study achieved a sufficient level of prediction accuracy with fewer training data through a learned prediction function based on the selected key criteria. This paper also proposes a system sequence designed with a series of procedures fully compliant with C-V2X systems. We demonstrated substantial simulations and numerical experiments with theoretical analysis, specifically applying soft-margin-based Support Vector Machine (SVM) algorithm. The simulation results confirmed that the granularity parameter we applied fairly controls the size of VANET clusters although vehicles are in motion and that the prediction performance has been adjusted through controlling of key SVM parameters. INDEX TERMS 5G mobile communication, vehicular ad hoc networks, machine-to-machine communications, machine learning, mobile computing, clustering methods, heterogeneous networks.

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“…Thirdly, communication speeds have increased significantly because of the fifthgeneration era of technology. All these aspects have enhanced the feasibility of undertaking a significant amount of research in the area of VANETs in general and in the subarea of routing protocols for VANETs and VANETs clustering in particular [7,8].…”

Section: Introductionmentioning

confidence: 99%

Reliability-Aware Multi-Objective Optimization-Based Routing Protocol for VANETs Using Enhanced Gaussian Mutation Harmony Searching

et al. 2022

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Vehicular Ad Hoc Networks (VANETs) is an emerging technology that can be applied in intelligent transportation systems. Routing protocols are essential for obtaining reliable VANET networks. This article proposes a novel framework, designated as Reliability Aware Multi-Objective Optimization Based VANETs Routing (RAMO). The framework includes three levels: the first is the simulation of the VANET system; the second is the routing criteria, based on reliability and geometrics; the third level is the routing algorithm. The actual network is the next stage. Furthermore, the framework includes an optimization block that controls the parameters of each of the reliability, geometrical and routing blocks. The optimization is presented under the multi-objective perspective and based on the development of a novel variant of multiobjective harmony searching. This has been designated as Enhanced Gaussian Mutation Harmony Searching (EGMHS), which includes Gaussian mutation, objective decomposition and a harmony memory extraction algorithm. The evaluation was performed based on two levels. The first was the EGMHS evaluation using nine benchmarking mathematical functions, while the second was the RAMO evaluation based on the network simulator. The metrics obtained, including set coverage, delta metric, hyper-volume, packet delivery ratio (PDR) and end-to-end (E2E) delay, demonstrate the superiority over the baseline approaches of both EGMHS and RAMO with EGMHS.

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A stable clustering algorithm based on affinity propagation for VANETs

Cited by 14 publications

References 7 publications

HRTF Clustering for Robust Training of a DNN for Sound Source Localization

HRTF Clustering for Robust Training of a DNN for Sound Source Localization

Multi-Dimensional Affinity Propagation Clustering Applying a Machine Learning in 5G-Cellular V2X

Reliability-Aware Multi-Objective Optimization-Based Routing Protocol for VANETs Using Enhanced Gaussian Mutation Harmony Searching

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