Sliding Window Based Feature Extraction and Traffic Clustering for Green Mobile Cyberphysical Systems

Joint Authors

Wei, Jibo
Zhang, Jiao
Zhou, Li
Xiao, Angran
Zeng, Sai
Zhao, Haitao

Source

Mobile Information Systems

Issue

Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2017-05-30

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Telecommunications Engineering

Abstract EN

Both the densification of small base stations and the diversity of user activities bring huge challenges for today’s heterogeneous networks, either heavy burdens on base stations or serious energy waste.

In order to ensure coverage of the network while reducing the total energy consumption, we adopt a green mobile cyberphysical system (MCPS) to handle this problem.

In this paper, we propose a feature extraction method using sliding window to extract the distribution feature of mobile user equipment (UE), and a case study is presented to demonstrate that the method is efficacious in reserving the clustering distribution feature.

Furthermore, we present traffic clustering analysis to categorize collected traffic distribution samples into a limited set of traffic patterns, where the patterns and corresponding optimized control strategies are used to similar traffic distributions for the rapid control of base station state.

Experimental results show that the sliding window is more superior in enabling higher UE coverage over the grid method.

Besides, the optimized control strategy obtained from the traffic pattern is capable of achieving a high coverage that can well serve over 98% of all mobile UE for similar traffic distributions.

American Psychological Association (APA)

Zhang, Jiao& Zhou, Li& Xiao, Angran& Zeng, Sai& Zhao, Haitao& Wei, Jibo. 2017. Sliding Window Based Feature Extraction and Traffic Clustering for Green Mobile Cyberphysical Systems. Mobile Information Systems،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1188962

Modern Language Association (MLA)

Zhang, Jiao…[et al.]. Sliding Window Based Feature Extraction and Traffic Clustering for Green Mobile Cyberphysical Systems. Mobile Information Systems No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1188962

American Medical Association (AMA)

Zhang, Jiao& Zhou, Li& Xiao, Angran& Zeng, Sai& Zhao, Haitao& Wei, Jibo. Sliding Window Based Feature Extraction and Traffic Clustering for Green Mobile Cyberphysical Systems. Mobile Information Systems. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1188962

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1188962