The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.
Bibliographical noteFunding Information:
The research of Hong-Ning Dai and Hao Wang is supported by Macao Science and Technology Development Fund under Grant No. 0026/2018/A1, National Natural Science Foundation of China (NFSC) under Grant No. 61672170, NSFC-Guangdong Joint Fund under Grant No. U1401251, the Science and Technology Planning Project of Guangdong Province under Grants No. 2015B090923004 and No. 2017A050501035, Science and Technology Program of Guangzhou under Grant No. 201807010058. The research of Raymond Chi-Wing Wong is supported by HKRGC GRF 16214017. The research of Zibin Zheng is supported by the National Key Research and Development Program under Grant No. 2016YFB1000101, National Natural Science Foundation of China under Grant No. U1811462, the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant No. 2016ZT06D211. The authors would like to thank Gordon K.-T. Hon for his constructive comments. Authors’ addresses: H.-N. Dai (corresponding author), Room A320, Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau; email: firstname.lastname@example.org; R. Chi-Wing Wong, Department of Computer Science and Engineering, the Hong Kong University of Science and Technology (HKUST), Clear Water Bay, Kowloon, Hong Kong; email: email@example.com; H. Wang, Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, postboks 191, No-2802 Gjøvik, Norway; email: firstname.lastname@example.org; Z. Zheng, School of Data and Computer Science, Sun Yat-sen University, Xiaoguwei Island, Panyu District, Guangzhou, 510006, P. R. China; email: email@example.com; A. V. Vasilakos, Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Lulea, 97187, Sweden. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. © 2019 Association for Computing Machinery. 0360-0300/2019/09-ART99 $15.00 https://doi.org/10.1145/3337065
© 2019 Association for Computing Machinery.
- Big data
- Machine learning
- Wireless networks