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Mobile Information Systems
Volume 2017, Article ID 2409830, 10 pages
https://doi.org/10.1155/2017/2409830
Research Article

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

1College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China
2Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, Shijiazhuang, China
4Department of Mechanical Engineering Technology, New York City College of Technology, City University of New York, Brooklyn, NY 11201, USA
5IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA

Correspondence should be addressed to Li Zhou; nc.ude.tdun@5302iluohz

Received 16 February 2017; Accepted 4 May 2017; Published 30 May 2017

Academic Editor: Jun Cheng

Copyright © 2017 Jiao Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.