TY - JOUR
T1 - Big Data Analytics for Large-scale Wireless Networks
T2 - Challenges and Opportunities
AU - Dai, Hong Ning
AU - Wong, Raymond Chi Wing
AU - Wang, Hao
AU - Zheng, Zibin
AU - Vasilakos, Athanasios V
N1 - Funding 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, NSFCGuangdong 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.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Big data
KW - Machine learning
KW - Wireless networks
UR - http://www.scopus.com/inward/record.url?scp=85072409619&partnerID=8YFLogxK
U2 - 10.1145/3337065
DO - 10.1145/3337065
M3 - Journal article
AN - SCOPUS:85072409619
SN - 0360-0300
VL - 52
JO - ACM Computing Surveys
JF - ACM Computing Surveys
IS - 5
M1 - 99
ER -