Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO)

Aadil, Farhan; Ahsan, Waleed; Rehman, Zahoor Ur; Shah, Peer Azmat; Rho, Seungmin; Mehmood, Irfan

doi:10.1007/s11227-018-2305-x

Cited by 91 publications

(60 citation statements)

References 53 publications

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“…According to the available literature, there are two main cluster formation strategies: distributed and centralized strategies. Distributed strategy: Many clustering protocols have used this strategy to form the clusters. After receiving the necessary information from the neighborhood, each node in the network seeks its affiliation locally.…”

Section: Clustering In Vanets: An Overviewmentioning

confidence: 99%

Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

Senouci

Harous

Aliouat

2020

Int J Communication

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Summary Vehicular ad hoc networks (VANETs) have recently attracted considerable attention owing to their wide range of applications. However, there are several challenges, such as mobility, routing, scalability, quality of services, and security. Clustering is an important control mechanism in high‐mobility networks and has been verified to be a promising approach in VANETs as well, as it ensures a basic level of network performance. Accordingly, several clustering algorithms have been proposed for these networks, and different protocols typically focus on various performance metrics. In this study, we provide a thorough review of clustering algorithms in VANETs. First, we present background material regarding the clustering process. Secondly, we propose a new taxonomy that categorizes clustering algorithms in VANETs based on different design aspects and provides a description of the algorithms in each category. Thirdly, an analysis of the algorithms in each category is provided according to various comparison metrics. Fourthly, we highlight the main challenges for each category and discuss some open research issues. Finally, we provide a general comparison of different clustering algorithms according to selected key parameters. Thus, this study provides a more thorough understanding of VANET clustering algorithms and the research trends in this area.

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Section: Clustering In Vanets: An Overviewmentioning

confidence: 99%

Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

Senouci

Harous

Aliouat

2020

Int J Communication

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“…However, there are some gaps between application and research [5]. In practical applications, some conditions such as low power consumption [6], portable, space constraints [7] and extensive sEMG data with multiple channels and high sample rate [8] must be considered. Besides those, sEMG-based classification techniques have been extensively researched [9].…”

Section: Introductionmentioning

confidence: 99%

sEMG-Based Gesture Recognition with Convolution Neural Networks

et al. 2018

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Abstract:The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy.

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“…From the study, it is observed that different researchers introduced different solutions to address the problems that arose in different AI techniques regarding parameter tuning. However, the approaches used have been similar where most existing works adopted a metaheuristic algorithm in optimizing the AI technique parameter (Alwee, 2014;Chen et al, 2005;Hou and Li, 2009;Zhao et al, 2012;Ebrahimi et al, 2016;Hou et al, 2018;Aadil et al, 2018;Ramadas et al, 2018;Xiao et al, 2018). Motivated by this, the main objective of this study is to propose an improved crime forecasting model that is able to predict crime rates efficiently by properly tuning the required parameters of an AI technique using a metaheuristic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Gradient Tree Boosting Using Dragonfly Algorithm in Crime Forecasting and Analysis

Khairuddin¹,

Ali²,

Alwee³

et al. 2019

Journal of Computer Science

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Crime forecasting and analysis are very important in predicting future crime patterns and beneficial to the authorities in planning effective crime prevention measures. One of the challenges found in crime analysis is the crime data itself as its form, representation and distribution are varied and unpredictable. To handle such data, most researchers have been focusing on applying various Artificial Intelligence (AI) techniques as an analytical tool. Among them, Gradient Tree Boosting (GTB) is a newly emerged AI technique for forecasting especially in crime analysis. GTB possesses a unique feature among other AI techniques which is its robustness towards any data representation and distribution. Subsequently, this study would like to adopt GTB in modelling crime rates based on 8 defined crime types. Similar to other AI techniques, GTB's overall performance is heavily influenced by its input parameter configuration. To assess such a challenge, this study would like to propose a hybrid DA-GTB crime forecasting model that is equipped with a metaheuristic optimization algorithm called Dragonfly Algorithm (DA) in optimizing GTB's three main parameters namely number of trees, size of individual trees and learning rate. From the experimental result obtained, the application of DA for parameter optimization yielded a positive impact in enhancing GTB forecasting performance as it produced the smallest error compared to nonoptimized GTB. This indicates that the proposed model is able to perform well using time series data with a limited and small sample size.

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Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO)

Cited by 91 publications

References 53 publications

Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

sEMG-Based Gesture Recognition with Convolution Neural Networks

Parameter Optimization of Gradient Tree Boosting Using Dragonfly Algorithm in Crime Forecasting and Analysis

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