Efficient and Robust Cluster Identification for Ultra-wideband Propagations Inspired byBiological Ant Colony Clustering

Li, Bin; Zhao, Chenglin; Zhang, Haijun; Zhou, Zheng; Nallanathan, Arumugam

doi:10.1109/tcomm.2014.2377120

Cited by 28 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ant colony clustering (ACC) combines the decaying amplitude and the time of arrivals of MPCs (28). Clusters are identified based on the population and the positive-feedback collaboration of the evolution of the ant-agents.…”

Section: Figure 2 Multipath Clusters In Mobile Wireless Communications (27)mentioning

confidence: 99%

Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

Blanza

2021

Baghdad Sci.J

View full text Add to dashboard Cite

This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed to solve this problem by determining the membership of the clusters. The cluster and the cluster count were then computed through the cluster-wise Jaccard index of the membership of the multipaths to their clusters. The multipaths generated by C2CM were transformed using the directional cosine transform (DCT) and the whitening transform (WT). The transformed dataset was clustered using SC and 3CAM-SC. The clustering performance was validated using the Jaccard index by comparing the reference multipath dataset with the calculated multipath clusters. The results show that the effectiveness of SC is similar to the state-of-the-art clustering approaches. However, 3CAM-SC outperforms SC in all channel scenarios. SC can be used in indoor scenarios based on accuracy, while 3CAM-SC is applicable in indoor and semi-urban scenarios. Thus, the clustering approaches can be applied as alternative clustering techniques in the field of channel modeling.

show abstract

Section: Figure 2 Multipath Clusters In Mobile Wireless Communications (27)mentioning

confidence: 99%

Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

Blanza

2021

Baghdad Sci.J

View full text Add to dashboard Cite

show abstract

“…A more efficient ant-based data clustering is given in the following formulation [10]. First, every MPC is modeled as virtual ant-agent and being plotted onto a 2-D amplitude-time plane ( , ), which is considered to be a virtual workspace.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Comparative Study of KPower Means, Ant Colony Optimization, Kernel Power Density-based Estimation, and Gaussian Mixture Model for Wireless Propagation Multipath Clustering

Teologo¹

2020

IJETER

View full text Add to dashboard Cite

In radio communications, channel modeling has a very significant impact. Most of the system's performance relies on the behavior and characteristics of a radio channel. Due to its mobility features, radio channels are time-variant that change over time, making channel characterizations to be dynamic and very challenging. To ensure maximum data rate and reliable communication, accurate channel models are necessary, which requires a correct grouping of wireless multipaths. Accurate clustering of wireless multipath components (MPCs) is essential for cluster-based channel modeling resulting in a more reliable wireless channel system. Currently, determining the best clustering technique is still a challenge as there is no standard way of evaluating and comparing the performance of the various clustering algorithms. This work presents the comparative study on the accuracy performance of the four clustering algorithms namely K-Power Means (KPM), Ant Colony Optimization (ACO), Kernel Power Density-based Estimation (KPD), and Gaussian Mixture Model (GMM) in grouping the wireless MPCs using datasets generated from COST 2100 channel model (C2CM) which represent different Indoor and Semi-urban channel scenarios. Using the Jaccard index, an evaluation metric that compares the calculated data with the reference data, the accuracy of each algorithm is determined as well as their corresponding computational duration. A comparison of their performance is presented, and results show that KPM outperforms other clustering techniques in all channel scenarios of Indoor and Semi-urban environments, making it a right candidate for further improvements to develop a more accurate and computationally efficient clustering technique for wireless propagation multipaths.

show abstract

“…Ant colony algorithm is usually used in data mining and analysis [26] [27]. Additionally, it has been adapted for some cluster schemes in communication and network systems [28] [29]. The basic idea behind the ant colony algorithm includes the following features.…”

Section: Ant Colony Algorithmmentioning

confidence: 99%

Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

Dong

Ota

et al. 2018

IEEE Trans. Netw. Serv. Manage.

179

View full text Add to dashboard Cite

In software-defined networks (SDN), the abstracted control plane is its symbolic characteristic, whose core component is the software-based controller. The control plane is logically centralized, but the controllers can be physically distributed and composed of multiple nodes. To meet the service management requirements of large-scale network scenarios, the control plane is usually implemented in the form of distributed controller clusters. Cluster management technology monitors all types of events and must maintain a consistent global network status, which usually leads to big data in SDNs. Simultaneously, the cluster security is an open issue because of the programmable and dynamic features of SDNs. To address the above challenges, this paper proposes a big data analysis-based secure cluster management architecture for the optimized control plane. A security authentication scheme is proposed for cluster management. Moreover, we propose an ant colony optimization approach that enables big data analysis scheme and the implementation system that optimizes the control plane. Simulations and comparisons show the feasibility and efficiency of the proposed scheme. The proposed scheme is significant in improving the security and efficiency SDN control plane.

show abstract

Efficient and Robust Cluster Identification for Ultra-wideband Propagations Inspired byBiological Ant Colony Clustering

Cited by 28 publications

References 26 publications

Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

Comparative Study of KPower Means, Ant Colony Optimization, Kernel Power Density-based Estimation, and Gaussian Mixture Model for Wireless Propagation Multipath Clustering

Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

Contact Info

Product

Resources

About