survey

Big Data Analytics for Large-scale Wireless Networks: Challenges and Opportunities

Authors:
Hong-Ning Dai

Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau

Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau

0000-0001-6165-4196
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,
Raymond Chi-Wing Wong

The Hong Kong University of Science and Technology (HKUST), Clear Water Bay, Kowloon, Hong Kong

The Hong Kong University of Science and Technology (HKUST), Clear Water Bay, Kowloon, Hong Kong
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,
Hao Wang

Norwegian University of Science and Technology, Norway

Norwegian University of Science and Technology, Norway
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,
Zibin Zheng

Sun Yat-sen University, Guangzhou, P. R. China

Sun Yat-sen University, Guangzhou, P. R. China
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,
Athanasios V. Vasilakos

Lulea University of Technology, Lulea, Sweden

Lulea University of Technology, Lulea, Sweden
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Authors Info & Claims

ACM Computing Surveys Volume 52 Issue 5Article No.: 99pp 1–36https://doi.org/10.1145/3337065

Published:13 September 2019Publication History

ACM Computing Surveys

Abstract

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.

References

Cisco. 2017. Cisco Visual Networking Index: Forecast and Methodology, 2016-2021. Technical Report.Google Scholar
Hervé Abdi and Lynne J. Williams. 2010. Principal component analysis. Wiley Interdisc. Rev.: Comput. Stat. 2, 4 (2010), 433--459. Google ScholarDigital Library
G. Abdul-Salaam, A. Hanan Abdullah, and M. Hossein Anisi. 2017. Energy-efficient data reporting for navigation in position-free hybrid wireless sensor networks. IEEE Sensors J. 17, 7 (2017), 2289--2297.Google ScholarCross Ref
Karim Abouelmehdi, Abderrahim Beni-Hessane, and Hayat Khaloufi. 2018. Big healthcare data: Preserving security and privacy. J. Big Data 5, 1 (Jan. 2018), 1.Google ScholarCross Ref
Charu C. Aggarwal. 2006. Data Streams: Models and Algorithms (Advances in Database Systems). Springer, Secaucus, NJ. Google ScholarDigital Library
Charu C. Aggarwal and Tarek Abdelzaher. 2011. Integrating Sensors and Social Networks. Springer.Google Scholar
Nauman Ahad, Junaid Qadir, and Nasir Ahsan. 2016. Neural networks in wireless networks: Techniques, applications and guidelines. J. Netw. Comput. Appl. 68 (2016), 1--27. Google ScholarDigital Library
Louai Al-Awami and Hossam S. Hassanein. 2017. Robust decentralized data storage and retrieval for wireless networks. Comput. Netw. 128 (2017), 41--50. Google ScholarDigital Library
C. S. Alemán, N. Pissinou, S. Alemany, K. Boroojeni, J. Miller, and Z. Ding. 2018. Context-aware data cleaning for mobile wireless sensor networks: A diversified trust approach. In Proceedings of the International Conference on Computing, Networking and Communications (ICNC’18). IEEE, 226--230.Google Scholar
M. A. Alsheikh, Y. Jiao, D. Niyato, P. Wang, D. Leong, and Z. Han. 2017. The accuracy-privacy trade-off of mobile crowdsensing. IEEE Commun. Mag. 55, 6 (2017), 132--139.Google ScholarDigital Library
M. A. Alsheikh, S. Lin, D. Niyato, and H. P. Tan. 2014. Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Commun. Surveys Tutor. 16, 4 (2014), 1996--2018.Google ScholarCross Ref
Sattam et al. Alsubaiee. 2014. AsterixDB: A scalable, open source BDMS. Proc. VLDB Endow. 7, 14 (Oct. 2014), 1905--1916. Google ScholarDigital Library
Peter Alvaro, Tyson Condie, Neil Conway, Khaled Elmeleegy, Joseph M. Hellerstein, and Russell Sears. 2010. Boom analytics: Exploring data-centric, declarative programming for the cloud. In Proceedings of the 5th European Conference on Computer Systems (EuroSys’10). ACM. Google ScholarDigital Library
J. Chris Anderson, Jan Lehnardt, and Noah Slater. 2010. CouchDB: The Definitive Guide Time to Relax (1st ed.). O’Reilly Media, Inc. Google ScholarDigital Library
Apache. 2014. Hadoop mapreduce. Retrieved from https://hadoop.apache.org/.Google Scholar
Apache. 2016. Apache HBase. Retrieved from https://hbase.apache.org/.Google Scholar
Remzi H. Arpaci-Dusseau and Andrea C. Arpaci-Dusseau. 2015. Operating Systems: Three Easy Pieces (0.91 ed.). Arpaci-Dusseau Books. Google ScholarDigital Library
Man Ho Au, Kaitai Liang, Joseph K. Liu, Rongxing Lu, and Jianting Ning. 2018. Privacy-preserving personal data operation on mobile cloud—Chances and challenges over advanced persistent threat. Future Gen. Comput. Syst. 79 (2018), 337--349. Google ScholarDigital Library
Asif Iqbal Baba, Hua Lu, Torben Bach Pedersen, and Manfred Jaeger. 2017. Cleansing indoor RFID tracking data. SIGSPATIAL Spec. 9, 1 (July 2017), 11--18. Google ScholarDigital Library
Prasanta S. Bandyopadhyay and Malcolm R. Forster. 2011. Philosophy of Statistics. Elsevier.Google Scholar
Dominic Battré, Stephan Ewen, Fabian Hueske, Odej Kao, Volker Markl, and Daniel Warneke. 2010. Nephele/PACTs: A programming model and execution framework for web-scale analytical processing (SoCC). In Proceedings of the 1st ACM Symposium on Cloud Computing. ACM. Google ScholarDigital Library
Sibghat Ullah Bazai, Julian Jang-Jaccard, and Xuyun Zhang. 2017. A privacy preserving platform for mapreduce. In Applications and Techniques in Information Security. Springer, Singapore, 88--99.Google Scholar
Doug Beaver, Sanjeev Kumar, Harry C. Li, Jason Sobel, and Peter Vajgel. 2010. Finding a needle in haystack: Facebook’s photo storage. In Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation (OSDI’10). USENIX Associatation. Google ScholarDigital Library
Siddhartha Bhandari, Neil Bergmann, Raja Jurdak, and Branislav Kusy. 2017. Time series data analysis of wireless sensor network measurements of temperature. Sensors 17, 6 (2017).Google Scholar
Sourjya et al. Bhaumik. 2012. CloudIQ: A framework for processing base stations in a data center. In Proceedings of ACM Annual International Conference on Mobile Computing and Networking (MobiCom’12). ACM. Google ScholarDigital Library
S. Bi, C. K. Ho, and R. Zhang. 2015a. Wireless powered communication: Opportunities and challenges. IEEE Commun. Mag. 53, 4 (2015), 117--125.Google ScholarDigital Library
S. Bi, R. Zhang, Z. Ding, and S. Cui. 2015b. Wireless communications in the era of big data. IEEE Commun. Mag. 53, 10 (Oct. 2015), 190--199.Google ScholarDigital Library
Andrea Biral, Marco Centenaro, Andrea Zanella, Lorenzo Vangelista, and Michele Zorzi. 2015. The challenges of M2M massive access in wireless cellular networks. Digital Commun. Netw. 1, 1 (2015), 1--19.Google ScholarCross Ref
Bouziane Brik, Nasreddine Lagraa, Abderrahmane Lakas, and Abbas Cheddad. 2016. DDGP: Distributed data gathering protocol for vehicular networks. Vehic. Commun. 4 (2016), 15--29. Google ScholarDigital Library
Yingyi Bu, Bill Howe, Magdalena Balazinska, and Michael D. Ernst. 2010. HaLoop: Efficient iterative data processing on large clusters. Proc. VLDB Endow. 3, 1–2 (Sept. 2010). Google ScholarDigital Library
A. L. Buczak and E. Guven. 2016. A survey of data mining and machine-learning methods for cyber security intrusion detection. IEEE Commun. Surveys Tutor. 18, 2 (2016), 1153--1176.Google ScholarDigital Library
Rubén Casado and Muhammad Younas. 2015. Emerging trends and technologies in big data processing. Concurr. Comput.: Pract. Exper. 27, 8 (2015), 2078--2091. Google ScholarDigital Library
Prabhakar Chaganti and Rich Helms. 2010. Amazon SimpleDB Developer Guide (1st ed.). Packt Publishing. Google ScholarDigital Library
Ronnie Chaiken, Bob Jenkins, Per-Åke Larson, Bill Ramsey, Darren Shakib, Simon Weaver, and Jingren Zhou. 2008. SCOPE: Easy and efficient parallel processing of massive data sets. Proc. VLDB Endow. 1, 2 (Aug. 2008), 1265--1276. Google ScholarDigital Library
Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber. 2008. Bigtable: A distributed storage system for structured data. ACM Trans. Comput. Syst. 26, 2 (June 2008). Google ScholarDigital Library
A. Checko, H. L. Christiansen, Y. Yan, L. Scolari, G. Kardaras, M. S. Berger, and L. Dittmann. 2015. Cloud RAN for mobile networks—A technology overview. IEEE Commun. Surveys Tutor. 17, 1 (2015), 405--426.Google ScholarDigital Library
C. A. Chen, M. Won, R. Stoleru, and G. G. Xie. 2015. Energy-efficient fault-tolerant data storage and processing in mobile cloud. IEEE Trans. Cloud Comput. 3, 1 (2015), 28--41.Google ScholarCross Ref
F. Chen, T. Xiang, Y. Yang, and S. S. M. Chow. 2016. Secure cloud storage meets with secure network coding. IEEE Trans. Comput. 65, 6 (2016), 1936--1948.Google ScholarDigital Library
Min Chen, Shiwen Mao, and Yunhao Liu. 2014. Big data: A survey. Mobile Netw. Appl. 19, 2 (2014), 171--209. Google ScholarDigital Library
P. Y. Chen, S. Yang, and J. A. McCann. 2015. Distributed real-time anomaly detection in networked industrial sensing systems. IEEE Trans. Industr. Electron. 62, 6 (2015), 3832--3842.Google ScholarCross Ref
Zhuangbin Chen, Anfeng Liu, Zhetao Li, Young-June Choi, Hiroo Sekiya, and Jie Li. 2017. Energy-efficient broadcasting scheme for smart industrial wireless sensor networks. Mobile Info. Syst. (2017), 1--17.Google Scholar
Yue Cheng, M. Safdar Iqbal, Aayush Gupta, and Ali R. Butt. 2015. CAST: Tiering storage for data analytics in the cloud. In Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing (HPDC’15). ACM. Google ScholarDigital Library
Y. Cheng, D. Wang, P. Zhou, and T. Zhang. 2018. Model compression and acceleration for deep neural networks: The principles, progress, and challenges. IEEE Signal Process. Mag. 35, 1 (Jan. 2018), 126--136.Google ScholarCross Ref
Kristina Chodorow and Michael Dirolf. 2010. MongoDB: The Definitive Guide (1st ed.). O’Reilly Media, Inc. Google ScholarDigital Library
James C. Corbett. 2013. Spanner: Google’s globally distributed database. ACM Trans. Comput. Syst. 31, 3 (Aug. 2013), 8:1--8:22. Google ScholarDigital Library
L. Cui, F. R. Yu, and Q. Yan. 2016. When big data meets software-defined networking: SDN for big data and big data for SDN. IEEE Netw. 30, 1 (Jan. 2016), 58--65.Google ScholarDigital Library
Cihan H. Dagli, Nijat Mehdiyev, Julian Krumeich, David Enke, Dirk Werth, and Peter Loos. 2015. Determination of rule patterns in complex event processing using machine-learning techniques. Procedia Comput. Sci. 61 (2015), 395--401.Google ScholarCross Ref
Jeffrey Dean and Sanjay Ghemawat. 2008. MapReduce: Simplified data processing on large clusters. Commun. ACM 51, 1 (Jan. 2008), 107--113. Google ScholarDigital Library
Giuseppe et al. DeCandia. 2007. Dynamo: Amazon’s highly available key-value store. In Proceedings of the ACM Symposium on Operating Systems Principles (SOSP’07). ACM. Google ScholarDigital Library
Changyi Deng, Ruifeng Guo, Chao Liu, Ray Y. Zhong, and Xun Xu. 2018. Data cleansing for energy-saving: A case of cyber-physical machine tools health monitoring system. Int. J. Prod. Res. 56, 1–2 (2018), 1000--1015.Google ScholarCross Ref
O. Diallo, J. J. P. C. Rodrigues, M. Sene, and J. Lloret. 2015. Distributed database management techniques for wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 26, 2 (2015), 604--620.Google ScholarDigital Library
T. Diekmann, A. Melski, and M. Schumann. 2007. Data-on-network vs. data-on-tag: Managing data in complex RFID environments. In Proceedings of the 40th Annual Hawaii International Conference on System Sciences. IEEE. Google ScholarDigital Library
K. Ding, P. Jiang, P. Sun, and C. Wang. 2017. RFID-Enabled physical object tracking in process flow based on an enhanced graphical deduction modeling method. IEEE Trans. Syst., Man, Cybernet.: Syst. 47, 11 (Nov. 2017), 3006--3018.Google Scholar
Jaliya Ekanayake, Hui Li, Bingjing Zhang, Thilina Gunarathne, Seung-Hee Bae, Judy Qiu, and Geoffrey Fox. 2010. Twister: A runtime for iterative mapreduce. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (HPDC’10). ACM. Google ScholarDigital Library
Ibtissam El Khayat, Pierre Geurts, and Guy Leduc. 2010. Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning. Wireless Netw. 16, 2 (2010), 273--290. Google ScholarDigital Library
E. Elnikety, T. Elsayed, and H. E. Ramadan. 2011. iHadoop: Asynchronous iterations for mapreduce. In Proceedings of the IEEE International Conference on Cloud Computing Technology and Science (CloudCom’11). IEEE. Google ScholarDigital Library
G. Ertek, X. Chi, and A. N. Zhang. 2017. A framework for mining RFID data from schedule-based systems. IEEE Trans. Syst., Man, Cybernet.: Syst. 47, 11 (2017), 2967--2984.Google ScholarCross Ref
Jason Baker et al. 2011. Megastore: Providing scalable, highly available storage for interactive services. In Proceedings of the Conference on Innovative Data System Research (CIDR’11). Retrieved from www.cidrdb.org.Google Scholar
Z. M. Fadlullah, F. Tang, B. Mao, N. Kato, O. Akashi, T. Inoue, and K. Mizutani. 2017. State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun. Surveys Tutor. 19, 4 (2017), 2432--2455.Google ScholarCross Ref
Hossam Mahmoud Ahmad Fahmy. 2016. Wireless Sensor Networks: Concepts, Applications, Experimentation and Analysis. Springer. Google ScholarDigital Library
B. Fan, S. Leng, and K. Yang. 2016b. A dynamic bandwidth allocation algorithm in mobile networks with big data of users and networks. IEEE Netw. 30, 1 (Jan. 2016), 6--10.Google ScholarDigital Library
Y. C. Fan, Y. C. Chen, K. C. Tung, K. C. Wu, and A. L. P. Chen. 2016a. A framework for enabling user preference profiling through wi-fi logs. IEEE Trans. Knowl. Data Eng. 28, 3 (2016), 592--603. Google ScholarDigital Library
Dan Feldman, Andrew Sugaya, and Daniela Rus. 2012. An effective coreset compression algorithm for large-scale sensor networks. In Proceedings of the 11th International Conference on Information Processing in Sensor Networks (IPSN’12). ACM. Google ScholarDigital Library
M. Fogue, P. Garrido, F. J. Martinez, J. C. Cano, C. T. Calafate, and P. Manzoni. 2014. A system for automatic notification and severity estimation of automotive accidents. IEEE Trans. Mobile Comput. 13, 5 (2014), 948--963. Google ScholarDigital Library
Keke Gai, Meikang Qiu, Lixin Tao, and Yongxin Zhu. 2016. Intrusion detection techniques for mobile cloud computing in heterogeneous 5G. Secur. Commun. Netw. 9, 16 (2016), 3049--3058. Google ScholarDigital Library
Sorabh Gandhi, Suman Nath, Subhash Suri, and Jie Liu. 2009. GAMPS: Compressing multi sensor data by grouping and amplitude scaling. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’09). ACM. Google ScholarDigital Library
Inga Gerlach, Volker C. Hass, and Carl-Fredrik Mandenius. 2015. Conceptual design of an operator training simulator for a bio-ethanol plant. Processes 3, 3 (2015), 664--683.Google ScholarCross Ref
Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. 2003. The google file system. In Proceedings of ACM Symposium on Operating Systems Principles (SOSP’03). ACM. Google ScholarDigital Library
K. Goda and M. Kitsuregawa. 2012. The history of storage systems. Proc. IEEE 100 (May 2012), 1433--1440.Google Scholar
J. Granjal, E. Monteiro, and J. Silva. 2015. Security for the internet of things: A survey of existing protocols and open research issues. IEEE Commun. Surveys Tutor. 17, 3 (2015), 1294--1312.Google ScholarDigital Library
M. Gubanov. 2017. PolyFuse: A large-scale hybrid data fusion system. In Proceedings of the IEEE 33rd International Conference on Data Engineering (ICDE’17). IEEE, 1575--1578.Google ScholarCross Ref
Jorge Guerra, Himabindu Pucha, Joseph Glider, Wendy Belluomini, and Raju Rangaswami. 2011. Cost effective storage using extent-based dynamic tiering. In Proceedings of the 9th USENIX Conference on File and Stroage Technologies (FAST’11). USENIX Association. Google ScholarDigital Library
L. M. Haas, E. T. Lin, and M. A. Roth. 2002. Data integration through database federation. IBM Syst. J. 41, 4 (2002), 578--596. Google ScholarDigital Library
Mohammad Hajarian, Azam Bastanfard, Javad Mohammadzadeh, and Madjid Khalilian. 2017. Introducing fuzzy like in social networks and its effects on advertising profits and human behavior. Comput. Hum. Behav. 77 (2017), 282--293. Google ScholarDigital Library
G. Han, L. Liu, S. Chan, R. Yu, and Y. Yang. 2017. HySense: A hybrid mobile crowdsensing framework for sensing opportunities compensation under dynamic coverage constraint. IEEE Commun. Mag. 55, 3 (Mar. 2017), 93--99. Google ScholarDigital Library
Jiawei Han, Micheline Kamber, and Jian Pei. 2012. Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann, Boston, MA. Google Scholar
F. Hao, G. Min, Z. Pei, D. S. Park, and L. T. Yang. 2017. -Clique community detection in social networks based on formal concept analysis. IEEE Syst. J. 11, 1 (Mar. 2017), 250--259.Google ScholarCross Ref
Y. He, F. R. Yu, N. Zhao, H. Yin, H. Yao, and R. C. Qiu. 2016. Big data analytics in mobile cellular networks. IEEE Access 4 (2016), 1985--1996.Google ScholarCross Ref
Martin Hilbert. 2016. Big data for development: A review of promises and challenges. Dev. Policy Rev. 34, 1 (2016), 135--174.Google ScholarCross Ref
H. Hu, Y. Wen, T. S. Chua, and X. Li. 2014. Toward scalable systems for big data analytics: A technology tutorial. IEEE Access 2 (2014), 652--687.Google ScholarCross Ref
H. Hu, Y. Wen, and D. Niyato. 2017. Public cloud storage-assisted mobile social video sharing: A supermodular game approach. IEEE J. Select. Areas Commun. 35, 3 (Mar. 2017), 545--556. Google ScholarDigital Library
L. Hu, H. Wen, B. Wu, F. Pan, R. Liao, H. Song, J. Tang, and X. Wang. 2018. Cooperative jamming for physical layer security enhancement in internet of things. IEEE Internet Things J. 5, 1 (Feb. 2018), 219--228.Google ScholarCross Ref
K. Huang, Q. Zhang, C. Zhou, N. Xiong, and Y. Qin. 2017. An efficient intrusion detection approach for visual sensor networks based on traffic pattern learning. IEEE Trans. Syst., Man, Cybernet.: Syst. 47, 10 (2017), 2704--2713.Google ScholarCross Ref
Felix Hupfeld, Toni Cortes, Björn Kolbeck, Jan Stender, Erich Focht, Matthias Hess, Jesus Malo, Jonathan Marti, and Eugenio Cesario. 2008. The xtreemFS architecture—A case for object-based file systems in grids. Concurr. Comput.: Pract. Exper. 20, 17 (Dec. 2008), 2049--2060. Google ScholarDigital Library
S. Ilarri, T. Delot, and R. Trillo-Lado. 2015. A data management perspective on vehicular networks. IEEE Commun. Surveys Tutor. 17, 4 (2015), 2420--2460.Google ScholarDigital Library
A. Imran, A. Zoha, and A. Abu-Dayya. 2014. Challenges in 5G: How to empower SON with big data for enabling 5G. IEEE Netw. 28, 6 (Nov. 2014), 27--33.Google ScholarCross Ref
N. Iqbal, S. Al-Dharrab, A. Muqaibel, W. Mesbah, and G. Stüber. 2018. Analysis of wireless seismic data acquisition networks using Markov chain models. In Proceedings of the IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’18). IEEE, 1--5.Google Scholar
Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly. 2007. Dryad: Distributed data-parallel programs from sequential building blocks. SIGOPS Oper. Syst. Rev. 41, 3 (Mar. 2007), 59--72. Google ScholarDigital Library
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li. 2018. Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. In Proceedings of the IEEE Symposium on Security and Privacy (SP’18). IEEE, 19--35.Google ScholarCross Ref
C. Jiang, H. Zhang, Y. Ren, Z. Han, K. C. Chen, and L. Hanzo. 2017. Machine-learning paradigms for next-generation wireless networks. IEEE Wireless Commun. 24, 2 (Apr. 2017), 98--105. Google ScholarDigital Library
D. Jiang, L. Huo, Z. Lv, H. Song, and W. Qin. 2018. A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transport. Syst. 19, 10 (2018), 3305--3319.Google ScholarDigital Library
Terrence D. Jorgensen, K. Jean Forney, Jeffrey A. Hall, and Steven M. Giles. 2018. Using modern methods for missing data analysis with the social relations model: A bridge to social network analysis. Soc. Netw. 54 (2018), 26--40.Google ScholarCross Ref
J. Joung. 2016. Machine-learning-based antenna selection in wireless communications. IEEE Commun. Lett. 20, 11 (2016), 2241--2244.Google ScholarCross Ref
Sang-Woo Jun, Ming Liu, Sungjin Lee, Jamey Hicks, John Ankcorn, Myron King, Shuotao Xu, and Arvind. 2016. BlueDBM: Distributed flash storage for big data analytics. ACM Trans. Comput. Syst. 34, 3 (2016), 7:1--7:31. Google ScholarDigital Library
Min-Joo Kang and Je-Won Kang. 2016. Intrusion detection system using deep neural network for in-vehicle network security. PloS One 11, 6 (2016), 1--17.Google ScholarCross Ref
T. F. Kennedy, R. S. Provence, J. L. Broyan, P. W. Fink, P. H. Ngo, and L. D. Rodriguez. 2017. Topic models for RFID data modeling and localization. In Proceedings of the IEEE International Conference on Big Data (BigData’17). IEEE, 1438--1446.Google Scholar
E. J. Khatib, R. Barco, P. Munoz, I. D. La Bandera, and I. Serrano. 2016. Self-healing in mobile networks with big data. IEEE Commun. Mag. 54, 1 (Jan. 2016), 114--120.Google ScholarDigital Library
Mirza Golam Kibria, Kien Nguyen, Gabriel Porto Villardi, Kentaro Ishizu, and Fumihide Kojima. 2018. Big data analytics and artificial intelligence in next-generation wireless networks. IEEE Access 6 (2018), 32328--32338.Google ScholarCross Ref
P. V. Klaine, M. A. Imran, O. Onireti, and R. D. Souza. 2017. A survey of machine-learning techniques applied to self-organizing cellular networks. IEEE Commun. Surveys Tutor. 19, 4 (2017), 2392--2431.Google ScholarCross Ref
Eunjeong Ko, Jinyoung Ahn, and Eun Yi Kim. 2016. 3D Markov process for traffic flow prediction in real-time. Sensors 16, 2 (2016).Google Scholar
Jannis Koch, Christian L. Staudt, Maximilian Vogel, and Henning Meyerhenke. 2016. An empirical comparison of big graph frameworks in the context of network analysis. Social Netw. Anal. Min. 6, 1 (Sep. 2016), 84.Google ScholarCross Ref
C. Kolias, G. Kambourakis, A. Stavrou, and S. Gritzalis. 2016. Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset. IEEE Commun. Surveys Tutor. 18, 1 (2016), 184--208.Google ScholarDigital Library
Georg Krempl. 2014. Open challenges for data stream mining research. SIGKDD Explor. Newsl. 16, 1 (Sept. 2014), 1--10. Google ScholarDigital Library
Dirk P. Kroese, Thomas Taimre, and Zdravko I. Botev. 2013. Handbook of Monte Carlo Methods. Vol. 706. John Wiley 8 Sons. Google ScholarDigital Library
L. Kuang, L. T. Yang, X. Wang, P. Wang, and Y. Zhao. 2016. A tensor-based big data model for QoS improvement in software defined networks. IEEE Netw. 30, 1 (Jan. 2016), 30--35.Google ScholarDigital Library
Abhishek Kumar, Bikash Sah, Arvind R. Singh, Yan Deng, Xiangning He, Praveen Kumar, and R. C. Bansal. 2017. A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew. Sustain. Energy Rev. 69 (2017), 596--609.Google ScholarCross Ref
Hsu-Yang Kung, Sumalee Chaisit, and Nguyen Thi Mai Phuong. 2015. Optimization of an RFID location identification scheme based on the neural network. Int. J. Commun. Syst. 28, 4 (2015), 625--644.Google ScholarCross Ref
Griffin Lacey, Graham W. Taylor, and Shawki Areibi. 2016. Deep learning on FPGAs: Past, present, and future. CoRR abs/1602.04283 (2016). http://arxiv.org/abs/1602.04283Google Scholar
Wei Kuang Lai, Mei-Tso Lin, and Yu-Hsuan Yang. 2015. A machine-learning system for routing decision-making in urban vehicular ad hoc networks. Int. J. Distrib. Sensor Netw. 11, 3 (2015). Google ScholarDigital Library
Avinash Lakshman and Prashant Malik. 2009. Cassandra: A structured storage system on a P2P network. In Proceedings of the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA’09). ACM. Google ScholarDigital Library
J. Landt. 2005. The history of RFID. IEEE Potentials 24, 4 (Oct. 2005), 8--11.Google ScholarCross Ref
Cong Leng, Hao Li, Shenghuo Zhu, and Rong Jin. 2018. Extremely low bit neural network: Squeeze the last bit out with ADMM. In Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI’18). AAAI Press.Google Scholar
Maurizio Lenzerini. 2002. Data integration: A theoretical perspective. In Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS’02). ACM, 233--246. Google ScholarDigital Library
Jure Leskovec and Rok Sosič. 2016. SNAP: A general-purpose network analysis and graph-mining library. ACM Trans. Intell. Syst. Technol. 8, 1 (2016), 1. Google ScholarDigital Library
Ming Li, Deepak Ganesan, and Prashant Shenoy. 2009. PRESTO: Feedback-driven data management in sensor networks. IEEE/ACM Trans. Netw. 17, 4 (Aug. 2009), 1256--1269. Google ScholarDigital Library
Zhichao Li, Ming Chen, Amanpreet Mukker, and Erez Zadok. 2015. On the trade-offs among performance, energy, and endurance in a versatile hybrid drive. ACM Trans. Storage 11, 3 (2015), 13:1--13:27. Google ScholarDigital Library
J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao. 2017. A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4, 5 (2017), 1125--1142.Google ScholarCross Ref
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J. Dally. 2018. Deep gradient compression: Reducing the communication bandwidth for distributed training. In Proceedings of the International Conference on Learning Representations (ICLR’18).Google Scholar
H. Liu, F. Eldarrat, H. Alqahtani, A. Reznik, X. de Foy, and Y. Zhang. 2018. Mobile edge cloud system: Architectures, challenges, and approaches. IEEE Syst. J. 12, 3 (2018), 2495--2508.Google ScholarCross Ref
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang. 2018. Trojaning attack on neural networks. In Proceedings of the Network and Distributed System Security Symposium (NDSS’18). Internet Society, 1--15.Google ScholarCross Ref
Z. Liu, X. Huang, Z. Hu, M. K. Khan, H. Seo, and L. Zhou. 2017. On emerging family of elliptic curves to secure internet of things: ECC comes of age. IEEE Trans. Depend. Secure Comput. 14, 3 (2017), 237--248. Google ScholarDigital Library
Yucheng Low, Danny Bickson, Joseph Gonzalez, Carlos Guestrin, Aapo Kyrola, and Joseph M. Hellerstein. 2012. Distributed graphlab: A framework for machine learning and data mining in the cloud. Proc. VLDB Endow. 5, 8 (Apr. 2012), 716--727. Google ScholarDigital Library
R. Lu, H. Zhu, X. Liu, J. K. Liu, and J. Shao. 2014. Toward efficient and privacy-preserving computing in big data era. IEEE Netw. 28, 4 (July 2014), 46--50.Google ScholarCross Ref
Haishu Ma, Yi Wang, and Kesheng Wang. 2018. Automatic detection of false positive RFID readings using machine-learning algorithms. Expert Syst. Appl. 91 (2018), 442--451. Google ScholarDigital Library
P. Mach and Z. Becvar. 2017. Mobile edge computing: A survey on architecture and computation offloading. IEEE Commun. Surveys Tutor. 19, 3 (2017), 1628--1656.Google ScholarDigital Library
Grzegorz et al. Malewicz. 2010. Pregel: A system for large-scale graph processing. In Proceedings of ACM SIGMOD. ACM. Google ScholarDigital Library
Louiza Mansour and Samira Moussaoui. 2015. CDCP: Collaborative data collection protocol in vehicular sensor networks. Wireless Personal Commun. 80, 1 (2015), 151--165. Google ScholarDigital Library
Marshall Kirk McKusick and Sean Quinlan. 2009. GFS: Evolution on fast-forward. ACM Queue 7, 7 (Aug. 2009), 10:10--10:20. Google ScholarDigital Library
F. Mehmeti and T. Spyropoulos. 2017. Performance analysis of mobile data offloading in heterogeneous networks. IEEE Trans. Mobile Comput. 16, 2 (2017), 482--497. Google ScholarDigital Library
Abhinav Mehrotra, Veljko Pejovic, and Mirco Musolesi. 2014. SenSocial: A middleware for integrating online social networks and mobile sensing data streams. In Proceedings of the 15th International Middleware Conference (Middleware). ACM. Google ScholarDigital Library
Kais Mekki, Eddy Bajic, Frederic Chaxel, and Fernand Meyer. 2019. A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express 5, 1 (2019), 1--7.Google ScholarCross Ref
R. Mendes and J. P. Vilela. 2017. Privacy-preserving data mining: Methods, metrics, and applications. IEEE Access 5 (2017), 10562--10582.Google ScholarCross Ref
Amin Mohajer, Morteza Barari, and Houman Zarrabi. 2017. Big data-based self-optimization networking: A novel approach beyond cognition. Intell. Autom. Soft Comput. (2017), 1--7.Google Scholar
Carl H. Mooney and John F. Roddick. 2013. Sequential pattern mining -- Approaches and algorithms. ACM Comput. Surv. 45, 2 (Mar. 2013), 19:1--19:39. Google ScholarDigital Library
Luiz H. Nunes, Júlio C. Estrella, Alexandre N. Delbem, Charith Perera, and Stephan Reiff-Marganiec. 2016. The effects of relative importance of user constraints in cloud of things resource discovery: A case study. In Proceedings of the 9th International Conference on Utility and Cloud Computing (UCC’16). ACM, 245--250. Google ScholarDigital Library
David A. Patterson and John L. Hennessy. 2013. Computer Organization and Design: The Hardware/Software Interface (5th ed.). Morgan Kaufmann. Google ScholarDigital Library
Sancheng Peng, Aimin Yang, Lihong Cao, Shui Yu, and Dongqing Xie. 2017. Social influence modeling using information theory in mobile social networks. Info. Sci. 379 (2017), 146--159.Google Scholar
Songyut Phoemphon, Chakchai So-In, and Tri Gia Nguyen. 2018. An enhanced wireless sensor network localization scheme for radio irregularity models using hybrid fuzzy deep extreme learning machines. Wireless Netw. 24, 3 (Apr. 2018), 799--819. Google ScholarDigital Library
Martin Placek and Rajkumar Buyya. 2006. A Taxonomy of Distributed Storage Systems. Technical Report GRIDS-TR- 2006-11. Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia.Google Scholar
Bartłomiej Płaczek. 2017. Efficient data collection for self-organising traffic signal systems based on vehicular sensor networks. Int. J. Ad Hoc Ubiq. Comput. 26, 1 (2017), 56--69. Google ScholarDigital Library
Nicholas G. Polson and Vadim O. Sokolov. 2017. Deep learning for short-term traffic flow prediction. Transport. Res. Part C: Emerg. Technol. 79 (2017), 1--17.Google ScholarCross Ref
Tejas Puranik and Lata Narayanan. 2017. Community detection in evolving networks. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’17). ACM, 385--390. Google ScholarDigital Library
Lijun Qian, Jinkang Zhu, and Sihai Zhang. 2017. Survey of wireless big data. J. Commun. Info. Netw. 2, 1 (Mar. 2017), 1--18.Google ScholarCross Ref
Junfei Qiu, Qihui Wu, Guoru Ding, Yuhua Xu, and Shuo Feng. 2016. A survey of machine learning for big data processing. EURASIP J. Adv. Signal Process. 2016, 1 (2016), 1--16.Google Scholar
Saeed K. Rahimi and Frank S. Haug. 2010. Distributed Database Management Systems: A Practical Approach. John Wiley 8 Sons. Google ScholarDigital Library
P. Ram Mohan Rao, S. Murali Krishna, and A. P. Siva Kumar. 2018. Privacy preservation techniques in big data analytics: A survey. J. Big Data 5, 1 (22 Sep. 2018), 33.Google Scholar
C. Richthammer, M. Netter, M. Riesner, J. Sänger, and G. Pernul. 2014. Taxonomy of social network data types. EURASIP J. Info. Secur. 2014, 1 (2014), 11.Google ScholarCross Ref
Rodrigo Roman, Jianying Zhou, and Javier Lopez. 2013. On the features and challenges of security and privacy in distributed internet of things. Comput. Netw. 57, 10 (July 2013), 2266--2279. Google ScholarDigital Library
Giulio Rossetti, Luca Pappalardo, Dino Pedreschi, and Fosca Giannotti. 2017. Tiles: An online algorithm for community discovery in dynamic social networks. Mach. Learn. 106, 8 (Aug. 2017), 1213--1241. Google ScholarDigital Library
Stuart Russell and Peter Norvig. 2009. Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. Google ScholarDigital Library
Ahmad-Reza Sadeghi, Christian Wachsmann, and Michael Waidner. 2015. Security and privacy challenges in industrial internet of things. In Proceedings of the 52nd Annual Design Automation Conference (DAC’15). ACM. Google ScholarDigital Library
Hossein Saeedi Emadi and Sayyed Majid Mazinani. 2018. A novel anomaly detection algorithm using DBSCAN and SVM in wireless sensor networks. Wireless Personal Commun. 98, 2 (Jan. 2018), 2025--2035. Google ScholarDigital Library
J. Sahoo, S. Cherkaoui, A. Hafid, and P. K. Sahu. 2017. Dynamic hierarchical aggregation for vehicular sensing. IEEE Trans. Intell. Transport. Syst. 18, 9 (2017), 2539--2556.Google ScholarDigital Library
M. De Sanctis, I. Bisio, and G. Araniti. 2016. Data mining algorithms for communication networks control: Concepts, survey and guidelines. IEEE Netw. 30, 1 (Jan. 2016), 24--29.Google ScholarDigital Library
M. Sathiamoorthy, A. G. Dimakis, B. Krishnamachari, and F. Bai. 2014. Distributed storage codes reduce latency in vehicular networks. IEEE Trans. Mobile Comput. 13, 9 (2014), 2016--2027.Google ScholarCross Ref
M. Scalabrin, M. Gadaleta, R. Bonetto, and M. Rossi. 2017. A Bayesian forecasting and anomaly detection framework for vehicular monitoring networks. In Proceedings of the IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP’17). IEEE, 1--6.Google Scholar
S. K. Sharma and X. Wang. 2017. Live data analytics with collaborative edge and cloud processing in wireless IoT networks. IEEE Access 5 (2017), 4621--4635.Google ScholarCross Ref
Konstantin Shvachko, Hairong Kuang, Sanjay Radia, and Robert Chansler. 2010. The hadoop distributed file system. In Proceedings of the IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST’10). IEEE. Google ScholarDigital Library
John D. Strunk. 2012. Hybrid aggregates: Combining SSDs and HDDs in a single storage pool. SIGOPS Oper. Syst. Rev. 46, 3 (2012). Google ScholarDigital Library
Z. Su and Q. Xu. 2015. Content distribution over content centric mobile social networks in 5G. IEEE Commun. Mag. 53, 6 (June 2015), 66--72.Google Scholar
Z. Su, Q. Xu, and Q. Qi. 2016. Big data in mobile social networks: A QoE-oriented framework. IEEE Netw. 30, 1 (Jan. 2016), 52--57.Google ScholarDigital Library
V. Sze, Y. H. Chen, T. J. Yang, and J. S. Emer. 2017. Efficient processing of deep neural networks: A tutorial and survey. Proc. IEEE 105, 12 (2017), 2295--2329.Google ScholarCross Ref
D. Takaishi, H. Nishiyama, N. Kato, and R. Miura. 2014. Toward energy efficient big data gathering in densely distributed sensor networks. IEEE Trans. Emerg. Topics Comput. 2, 3 (Sept. 2014), 388--397.Google ScholarCross Ref
S. Tasnim, N. Pissinou, and S. S. Iyengar. 2017. A novel cleaning approach of environmental sensing data streams. In Proceedings of the 14th IEEE Annual Consumer Communications Networking Conference (CCNC’17). IEEE, 632--633.Google Scholar
A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, N. Zhang, S. Antony, H. Liu, and R. Murthy. 2010. Hive—A petabyte scale data warehouse using hadoop. In Proceedings of the IEEE 26th International Conference on Data Engineering (ICDE’10). IEEE.Google Scholar
T. X. Tran, A. Hajisami, P. Pandey, and D. Pompili. 2017. Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges. IEEE Communications Magazine 55, 4 (Apr. 2017), 54--61. Google ScholarDigital Library
Martin Treiber and Arne Kesting. 2013. Traffic Flow Dynamics. Springer.Google Scholar
William M. K. Trochim, Jim Donnelly, and Kanika Arora. 2016. Research Methods The Essential Knowledge Base (2nd ed.). Cengage Learning.Google Scholar
Binghui Wang and Neil Zhenqiang Gong. 2018. Stealing hyperparameters in machine learning. In Proceedings of IEEE Symposium on Security and Privacy (SP’18). IEEE, 36--52.Google ScholarCross Ref
B. Wang, B. Li, and H. Li. 2015b. Panda: Public auditing for shared data with efficient user revocation in the cloud. IEEE Trans. Services Comput. 8, 1 (Jan. 2015), 92--106.Google ScholarCross Ref
F. Wang and J. Liu. 2011. Networked wireless sensor data collection: Issues, challenges, and approaches. IEEE Commun. Surveys Tutor. 13, 4 (2011), 673--687.Google ScholarCross Ref
G. Wang, J. Xin, L. Chen, and Y. Liu. 2012. Energy-efficient reverse skyline query processing over wireless sensor networks. IEEE Trans. Knowl. Data Eng. 24, 7 (July 2012), 1259--1275. Google ScholarDigital Library
Jiangtao Wang, Yasha Wang, Daqing Zhang, and Sumi Helal. 2018a. Energy saving techniques in mobile crowd sensing: Current state and future opportunities. IEEE Commun. Mag. (2018), 1--7.Google Scholar
Ning Wang, Xiaokui Xiao, Yin Yang, Ta Duy Hoang, Hyejin Shin, Junbum Shin, and Ge Yu. 2018b. PrivTrie: Effective frequent term discovery under local differential privacy. In Proceedings of the IEEE International Conference on Data Engineering (ICDE’18). IEEE.Google ScholarCross Ref
T. Wang, N. S. Nguyen, J. Wang, T. Li, X. Zhang, N. Mi, B. Zhao, and B. Sheng. 2018. RoVEr: Robust and verifiable erasure code for hadoop distributed file systems. In Proceedings of the 27th International Conference on Computer Communication and Networks (ICCCN’18). IEEE, 1--9.Google Scholar
X. Wang, M. Chen, T. Taleb, A. Ksentini, and V. C. M. Leung. 2014. Cache in the air: Exploiting content caching and delivery techniques for 5G systems. IEEE Commun. Mag. 52, 2 (Feb. 2014), 131--139.Google ScholarCross Ref
X. Wang, X. Li, and V. C. M. Leung. 2015a. Artificial intelligence-based techniques for emerging heterogeneous network: State of the arts, opportunities, and challenges. IEEE Access 3 (2015), 1379--1391.Google ScholarCross Ref
Z. Wang, L. Duan, and R. Zhang. 2016. Adaptively directional wireless power transfer for large-scale sensor networks. IEEE J. Select. Areas Commun. 34, 5 (May 2016), 1785--1800.Google ScholarDigital Library
R. Want. 2006. An introduction to RFID technology. IEEE Pervas. Comput. 5, 1 (Jan. 2006), 25--33. Google ScholarDigital Library
Hakim Weatherspoon and John Kubiatowicz. 2002. Erasure coding vs. replication: A quantitative comparison. In Proceedings of the 1st International Workshop on Peer-to-Peer Systems (IPTPS’02). IEEE, 328--338. Google ScholarDigital Library
Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, and Dan Steinberg. 2008. Top 10 algorithms in data mining. Knowl. Info. Syst. 14, 1 (2008), 1--37. Google ScholarDigital Library
Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. 2014. Data mining with big data. IEEE Trans. Knowl. Data Eng. 26, 1 (2014), 97--107. Google ScholarDigital Library
Zhuoqun Xia, Zhenzhen Hu, and Junpeng Luo. 2017. UPTP vehicle trajectory prediction based on user preference under complexity environment. Wireless Personal Commun. 97, 3 (2017), 4651--4665. Google ScholarDigital Library
F. Xu, Y. Li, M. Chen, and S. Chen. 2016. Mobile cellular big data: Linking cyberspace and the physical world with social ecology. IEEE Netw. 30, 3 (May 2016), 6--12.Google ScholarDigital Library
J. Xu, J. Yao, L. Wang, Z. Ming, K. Wu, and L. Chen. 2018. Narrowband internet of things: Evolutions, technologies and open issues. IEEE Internet Things J. 5, 3 (2018), 1449--1462.Google ScholarCross Ref
W. Xu, S. Jha, and W. Hu. 2019. LoRa-Key: Secure key generation system for LoRa-based network. IEEE Internet Things J. (2019), 1--10.Google Scholar
G. Yang, S. He, Z. Shi, and J. Chen. 2017b. Promoting cooperation by the social incentive mechanism in mobile crowdsensing. IEEE Commun. Mag. 55, 3 (Mar. 2017), 86--92. Google ScholarDigital Library
S. Yang, U. Adeel, Y. Tahir, and J. A. McCann. 2017a. Practical opportunistic data collection in wireless sensor networks with mobile sinks. IEEE Trans. Mobile Comput. 16, 5 (May 2017), 1420--1433. Google ScholarDigital Library
Y. Yang, L. Wu, G. Yin, L. Li, and H. Zhao. 2017c. A survey on security and privacy issues in internet-of-things. IEEE Internet Things J. 4, 5 (Oct. 2017), 1250--1258.Google ScholarCross Ref
H. Ye, L. Liang, G. Y. Li, J. Kim, L. Lu, and M. Wu. 2018. Machine learning for vehicular networks: Recent advances and application examples. IEEE Vehic. Technol. Mag. 13, 2 (2018), 94--101.Google ScholarCross Ref
Bin Yu, Xiaolin Song, Feng Guan, Zhiming Yang, and Baozhen Yao. 2016. -Nearest neighbor model for multiple-time-step prediction of short-term traffic condition. J. Transport. Eng. 142, 6 (2016), 1--10.Google ScholarCross Ref
X. Yu, H. Zhao, L. Zhang, S. Wu, B. Krishnamachari, and V. O. K. Li. 2010. Cooperative sensing and compression in vehicular sensor networks for urban monitoring. In Proceedings of the IEEE International Conference on Communications (ICC’10). IEEE.Google ScholarCross Ref
Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2010. Spark: Cluster computing with working sets. In Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing (HotCloud’10). USENIX Association. Google ScholarDigital Library
Chen Zhang, Peng Li, Guangyu Sun, Yijin Guan, Bingjun Xiao, and Jason Cong. 2015. Optimizing FPGA-based accelerator design for deep convolutional neural networks. In Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA’15). ACM. Google ScholarDigital Library
J. Zhang, X. Chen, Y. Xiang, W. Zhou, and J. Wu. 2015. Robust network traffic classification. IEEE/ACM Trans. Netw. 23, 4 (2015), 1257--1270. Google ScholarDigital Library
Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao. 2017. PrivBayes: Private data release via Bayesian networks. ACM Trans. Database Syst. 42, 4 (Oct. 2017), 25:1--25:41. Google ScholarDigital Library
Yi Zhang, Jamie Callan, and Thomas Minka. 2002. Novelty and redundancy detection in adaptive filtering. In Proceedings of ACM SIGIR. ACM. Google ScholarDigital Library
Yanfeng Zhang, Qixin Gao, Lixin Gao, and Cuirong Wang. 2012. iMapReduce: A distributed computing framework for iterative computation. J. Grid Comput. 10, 1 (2012), 47--68. Google ScholarDigital Library
Y. Zhang, M. Qiu, C. W. Tsai, M. M. Hassan, and A. Alamri. 2017. Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11, 1 (2017), 88--95.Google ScholarCross Ref
Zheng Zhao, Weihai Chen, Xingming Wu, Peter C. Y. Chen, and Jingmeng Liu. 2017. LSTM network: A deep-learning approach for short-term traffic forecast. IET Intell. Transport Syst. 11, 2 (2017), 68--75.Google ScholarCross Ref
K. Zheng, Z. Yang, K. Zhang, P. Chatzimisios, K. Yang, and W. Xiang. 2016. Big data-driven optimization for mobile networks toward 5G. IEEE Netw. 30, 1 (Jan. 2016), 44--51.Google ScholarDigital Library
Vincent W. Zheng, Yu Zheng, Xing Xie, and Qiang Yang. 2010. Collaborative location and activity recommendations with GPS history data. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). ACM. Google ScholarDigital Library
Zibin Zheng, Shaoan Xie, Hong-Ning Dai, Xiangping Chen, and Huaimin Wang. 2018a. Blockchain challenges and opportunities: A survey. Int. J. Web Grid Serv. 14, 4 (2018), 352--375. Google ScholarCross Ref
Z. Zheng, Y. Yang, X. Niu, H. N. Dai, and Y. Zhou. 2018b. Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans. Industr. Info. 14, 4 (2018), 1606--1615.Google ScholarCross Ref
Y. Zhu, J. Lin, P. P. C. Lee, and Y. Xu. 2015. Boosting degraded reads in heterogeneous erasure-coded storage systems. IEEE Trans. Comput. 64, 8 (2015), 2145--2157.Google ScholarDigital Library
Paul Zikopoulos and Chris Eaton. 2011. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data (1st ed.). McGraw-Hill Osborne Media. Google ScholarDigital Library
Yi Zuo. 2016. Prediction of consumer purchase behaviour using Bayesian network: An operational improvement and new results based on RFID data. Int. J. Knowl. Eng. Soft Data Paradigm. 5, 2 (Apr. 2016), 85--105. Google ScholarDigital Library

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Published in
ACM Computing Surveys Volume 52, Issue 5
September 2020
791 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3362097
Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering
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Copyright © 2019 ACM
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Publication History
- Published: 13 September 2019
- Accepted: 1 May 2019
- Revised: 1 March 2019
- Received: 1 May 2017
Published in csur Volume 52, Issue 5

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Big Data Analytics for Large-scale Wireless Networks: Challenges and Opportunities

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