Abstract
Wireless big data describes a wide range of massive data that is generated, collected and stored in wireless networks by wireless devices and users. While these data share some common properties with traditional big data, they have their own unique characteristics and provide numerous advantages for academic research and practical applications. This article reviews the recent advances and trends in the field of wireless big data. Due to space constraints, this survey is not intended to cover all aspects in this field, but to focus on the data aided transmission, data driven network optimization and novel applications. It is expected that the survey will help the readers to understand this exciting and emerging research field better. Moreover, open issues and promising future directions are also identified.
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References
V. D. Blondel., A. Decuyper, G. Krings. A survey of results on mobile phone datasets analysis [J]. EPJ data science, 2015, 4(1): 1.
M. Lin and W. Hsu. Mining GPS data for mobility patterns: A survey [J]. Pervasive and mobile computing, 2014, 12: 1–16.
K. Chen, H. Zhou. Research on realization mode of telecom operators' big data resource and its strategy [J]. Mobile communications, 2016, 40(1): 63–67.
X. Zhang, Z. Yi, Z. Yan, et al. Social computing for mobile big data [J]. Computer, 2016, 49(9): 86–90.
X. Ding, Y. Tian, Y. Yu. A real-time big data gathering algorithm based on indoor wireless sensor networks for risk analysis of industrial operations [J]. IEEE transactions on industrial informatics, 2016, 12(3): 1232–1242.
L. Kong, D. Zhang, Z. He, et al. Embracing big data with compressive sensing: a green approach in industrial wireless networks [J]. IEEE communications magazine, 2016, 54(10): 53–59.
Y. He, F. R. Yu, N. Zhao, et al. Big data analytics in mobile cellular networks[J]. IEEE access, 2016, 4: 1985–1996.
C. Zhang, R. C. Qiu. Massive mimo as a big data system: random matrix models and testbed [J]. IEEE access, 2015, 3: 837–851.
L. Kuang, F. Hao, L. T. Yang, et al. A tensor-based approach for big data representation and dimensionality reduction [J]. IEEE transactions on emerging topics in computing, 2014, 2(3): 280–291.
Y. Qiao, Y. Cheng, J. Yang, et al. A mobility analytical framework for big mobile data in densely populated area[J]. IEEE transactions on vehicular technology, 2016, PP(99): 1–13.
R. K. Lomotey, R. Deters. Towards knowledge discovery in big data [C]//The 8th International Symposium on Service Oriented System Engineering (SOSE), 2014: 181–191.
F. Xu, Y. Lin, J. Huang, et al. Big data driven mobile traffic understanding and forecasting: a time series approach[J]. IEEE transactions on services computing, 2016, 9(5): 796–805.
K. Murphy. Machine Learning: A Probabilistic Perspective [M]. Cambridge: MIT Press, 2012.
I. Goodfellow, Y. Bengio, A. Courville. Deep Learning [M]. Cambridge: MIT Press, 2016.
J. Donahue, L. Hendricks, S. Guadarrama, et al. Longterm recurrent convolutional networks for visual recognition and description [C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2015: 2625–2634.
Y. Le Cun, Y. Bengio, G. Hinton. deep learning[J]. Nature, 2015, 521(7553): 436–444.
M. A. Alsheikh, D. Niyato, S. Lin, et al. Mobile big data analytics using deep learning and apache spark [J]. IEEE network, 2016, 30(3): 22–29.
Q. Ma, S. Zhang, W. Zhou, et al. When will you have a new mobile phone? an empirical answer from big data [J]. IEEE access, 2016.
C. Yang. Learning methodologies for wireless big data networks: a Markovian game-theoretic perspective [J]. Neurocomputing, 2016, 174: 431–438.
J. H. Zhang. The interdisciplinary research of big data and wireless channel: a cluster-nuclei based channel model [J](Accepted). China communication, 2016.
S. GVK, S. R. Dasari. Big spectrum data analysis in dsa enabled lte-a networks: A system architecture [C]//The IEEE 6th International Conference on Advanced Computing (IACC), 2016: 655–660.
Q. Zhu, X. Zhang. Effective-capacity based gaming for optimal power and spectrum allocations over big-data virtual wireless networks [C]//The IEEE Global Communications Conference (GLOBECOM), 2015: 1–6.
A. Omar. Improving data extraction efficiency of cache nodes in cognitive radio networks using big data analysis [C]//The 9th International Conference on Next Generation Mobile Applications, Services and Technologies, 2015, 2015: 305–310.
Q. Wu, G. Ding, Z. Du, et al. A cloud-based architecture for the internet of spectrum devices over future wireless networks [J]. IEEE access, 2016, 4: 2854–2862.
Y. Li. Grass-root based spectrummap database for selforganized cognitive radio and heterogeneous networks: Spectrum measurement, data visualization, and user participating model [C]//The IEEE Wireless Communications and Networking Conference (WCNC), 2015: 117–122.
F. Z. Kaddour, E. Vivier, L. Mroueh, et al. Green opportunistic and efficient resource block allocation algorithm for lte uplink networks [J]. IEEE transactions on vehicular technology, 2015, 64(10): 4537–4550.
J. Zhu, Y. Song, D. Jiang, et al. Multi-armed bandit channel access scheme with cognitive radio technology in wireless sensor networks for the internet of things [J]. IEEE access, 2016, 4: 4609–4617.
A. Alsohaily and E. S. Sousa. Dynamic spectrum access for multi-radio access technology, multi-operator autonomous small cell communication systems [C]//The IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), 2014: 1778–1782.
P. Chaichana, P. Uthansakul, and M. Uthansakul. Gpsaided opportunistic space-division multiple access for 5g communications [C]//The 20th Asia-Pacific Conference on Communication (APCC2014), 2014: 468–472.
L. Cui, F. R. Yu, Q. Yan. When big data meets softwaredefined networking: SDN for big data and big data for SDN [J]. IEEE network, 2016, 30(1): 58–65.
K. Yang, Q. Yu, S. Leng, et al. Data and energy integrated communication networks for wireless big data [J]. IEEE access, 2016, 4: 713–723.
J. Liu, F. Liu, N. Ansari. Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop [J]. IEEE network, 2014, 28(4): 32–39.
S. H. Zhang, D. D. Yin, Y. Q. Zhang, et al. Computing on base station behavior using erlang measurement and call detail record [J]. IEEE transactions on emerging topics in computing, 2015, 3(3): 444–453.
J. Yang, Y. Qiao, X. Zhang, et al. Characterizing user behavior in mobile internet [J]. IEEE transactions on emerging topics in computing, 2015, 3(1): 95–106.
K. Zheng, Z. Yang, K. Zhang, et al. Big data-driven optimization for mobile networks toward 5G [J]. IEEE network, 2016, 30(1): 44–51.
T. Louail, M. Lenormand, O. G. C. Ros, et al. From mobile phone data to the spatial structure of cities [J]. Scientific reports, 2014, 4(5276): 1–12.
C. Song, Z. Qu, N. Blumm, et al. Limits of predictability in human mobility [J]. Science, 2010, 327(5968): 1018–1021.
X. Lu, E. Wetter, N. Bharti, et al. Approaching the limit of predictability in human mobility [J]. Scientific reports, 2013, 3(2923): 1–9.
B. C. Csi, A. Browet, V. A. Traag, et al. Exploring the mobility of mobile phone users [J]. Physica A: statistical mechanics and its applications, 2013, 392(6): 1459–1473.
Y. Zhang. User mobility from the view of cellular data networks [C]//IEEE INFOCOM 2014-IEEE Conference on Computer Communications, Toronto, 2014: 1348–1356.
X. Zhou, Z. Zhao, R. Li, et al. Human mobility patterns in cellular networks[J]. IEEE communications letters, 2013, 17(10): 1877–1880.
F. Xu, Y. Li, M. Chen, et al. Mobile cellular big data: linking cyberspace and the physical world with social ecology [J]. IEEE network, 2016, 30(3): 6–12.
C. Song, T. Koren, P.Wang, et al. Modelling the scaling properties of human mobility [J]. Nature physics, 2010, 6(10): 818–823.
Y. Zhang, M. Chen, S. Mao, et al. Cap: community activity prediction based on big data analysis [J]. IEEE network, 2014, 28(4): 52–57.
W. Chen, I. Paik, P. C. K. Hung. Constructing a global social service network for better quality of Web service discovery [J]. IEEE transactions on services computing, 2015, 8(2): 284–298.
P. Zhou, Y. Zhou, D. Wu, et al. Differentially private online learning for cloud-based video recommendation with multimedia big data in social networks [J]. IEEE transactions on multimedia, 2016, 18(6): 1217–1229.
C. Li, P. Zhou, Y. Zhou, et al. Distributed private online learning for social big data computing over data center networks [C]//2016 IEEE International Conference on Communications (ICC), 2016: 1–6.
C. K. Leung, H. Zhang.Management of distributed big data for social networks [C]//The 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2016: 639–648.
J. Peppanen, M. J. Reno, M. Thakkar, et al. Leveraging ami data for distribution system model calibration and situational awareness [J]. IEEE transactions on smart grid, 2015, 6(4): 2050–2059.
Y. Wang, Q. Chen, C. Kang, et al. Clustering of electricity consumption behavior dynamics toward big data applications [J]. IEEE transactions on smart grid, 2016, 7(5): 2437–2447.
E. Pan, D. Wang, Z. Han. Analyzing big smart metering data towards differentiated user services: A sublinear approach [J]. IEEE transactions on big data, 2016, 2(3): 249–261.
S. Haben, C. Singleton, P. Grindrod. Analysis and clustering of residential customers energy behavioral demand using smart meter data [J]. IEEE transactions on smart grid, 2016, 7(1): 136–144.
X. He, Q. Ai, R. C. Qiu, et al. A big data architecture design for smart grids based on random matrix theory [J]. IEEE transactions on smart Grid, 2015.
A. Hakiri, P. Berthou, A. Gokhale, et al. Publish/ subscribe-enabled software defined networking for efficient and scalable iot communications [J]. IEEE communications magazine, 2015, 53(9): 48–54 [55]_A. Ahmad, M. M. Rathore, A. Paul, et al. Defining human behaviors using big data analytics in social internet of things [C]}//The IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016: 1101–1107.
V. P. Ka e, Y. Fukushima, H. Harai. Id-based communication for realizing iot and m2m in future heterogeneous mobile networks [C]//2015 International Conference on Recent Advances in Internet of Things (RIoT), 2015: 1–6.
M. A. Kader, E. Bastug, M. Bennis, et al. Leveraging big data analytics for cache-enabled wireless networks [C]//The IEEE Globecom Workshops (GC Wkshps), 2015: 1–6.
N. Ramdhan, M. Sliti, N. Boudriga. Codeword-based data collection protocol for optical Unmanned Aerial Vehicle networks [C]//HONET-ICT IEEE, 2016: 35–39.
D. Wu, D. I. Arkhipov, M. Kim, et al. Addsen: Adaptive data processing and dissemination for drone swarms in urban sensing [J]. IEEE transactions on computers, 2016.
A. Jaziri, R. Nasri, T. Chahed. Congestion mitigation in 5g networks using drone relays [C]//The International Wireless Communications and Mobile Computing Conference (IWCMC), 2016: 233–238.
N. Mohamed, H. AlDhaheri, K. Almurshidi, M. AlHammoudi, et al. Using uavs to secure linear wireless sensornetworks [C]//The IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), 2016: 424–429.
J. Hua, Y. Gao, S. Zhong. Differentially private publication of general time-serial trajectory data [C]//The IEEE Conference on Computer Communications (INFOCOM), 2015: 549–557.
K. Mano, K. Minami, H. Maruyama. Pseudonym exchange for privacy-preserving publishing of trajectory data set [C]//The IEEE 3rd Global Conference on Consumer Electronics (GCCE), 2014: 691–695.
V. Primault, S. B. Mokhtar, C. Lauradoux, et al. Time distortion anonymization for the publication of mobility data with high utility [C]//The IEEE Trustcom/BigDataSE/ISPA, 2015, 1: 539–546.
J. Furtak, Z. Zieliski, and J. Chudzikiewicz. Security techniques for the wsn link layer within military IoT [C]//The IEEE 3rd World Forum on Internet of Things (WF-IoT), 2016: 233–238.
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This research work is supported in part by the U.S. OASD (R&E) (Office of the Assistant Secretary of Defense for Research and Engineering) (No. FA8750-15-2-0119), and by the U.S. Army Research Office(No. W911NF-16-1-0496). The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Office of the Assistant Secretary of Defense for Research and Engineering (OASD (R&E)), the Army Research Office, or the U.S. Government. Sihai Zhang received support from Key Program of National Natural Science Foundation of China(No. 61631018), the Fundamental Research Funds for the Central Universities and Huawei Technology Innovative Research on Wireless Big Data.
Lijun Qian is a professor in the Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU), a member of the Texas A&M University System located near Houston Texas, USA. He is also the director of the Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT Center) and the Wireless Communications Lab (WiComLab). Before joining PVAMU, he was a MTS in the Networks and Systems Research Department of Bell-Labs at Murray Hill, New Jersey, USA. He is a visiting professor of Aalto University, Finland. He received his B.E. from Tsinghua University in China, M.S.E.E. from Technion-Israel Institute of Technology, and Ph.D. from Rutgers University. His research interests are in big data analytics, wireless communications and mobile networks, network security and intrusion detection, and computational systems biology. His research is supported by NSF, DOE and DOD.
Jinkang Zhu has joined in University of Science and Technology of China (USTC) since 1966, and is a professor of USTC from 1992. He has been loyal to research on wireless mobile communications and networks, communication signal processing, and the future wireless technologies. Prof. Zhu was a member of the Expert Group of High Technology Communication Subject (863), director of the Expert Group of Wireless communications (863). He was director of the School of Information Science and Technology of USTC. He had been China delegate of Mobile Communication Forum of Asia-Pacific Region, the keynote speaker of IEEE ISSSC1992, the general chair of international conference of WCDMA technology, the general co-chair of international conference of WCSP2014, the general chair of Symposium of Green Wireless Communication Technologies, and the general chair of Symposium of 1st and 2nd Wireless Networks for Big Data. Recently, he studies with great interest in wireless big data, green wireless communications, and emerging technologies in wireless communications and networks.
Sihai Zhang [corresponding author] earned his B.E. in computer science from Ocean University of China, Qingdao, China, in 1996. He received M.S. and Ph.D. degree of Computer Science at University of Science and Technology of China (USTC) in 2002 and 2006, respectively. He worked as guest researcher in Department of Electronic Engineering of KAIST, South Korea during 2007–2008. His main research interests focus on wireless networks, wireless big data and intelligent algorithm. He is now an associate professor in school of information science and technology at University of Science and Technology of China in Hefei, China, where he is leading the wireless big data research on wireless user behavior modeling and wireless network optimization technologies for future wireless systems. He is involved in China 5G mobile communication project, NSFC Sino-Finland MTC project, NSFC Key Program on wireless big data. He has published more than 60 research articles in high-impact IEEE international journals and conferences of wireless communication fields. He has also served as peer Reviewers of IEEE Wireless Communication Magazine, IEEE Transactions on Wireless Communication (TWC), IEEE Transactions on Vehicular Technology (TVT), IEEE Communications Letter (CL), and Technical Program Committee (TPC) Members and session chairs of some International Conferences including IEEE ICC/GC/PIMRC/WCSP/VTC, etc. In 2016, he co-organized two technical sessions, on mobile big data in WCSP2016 and machine type communications in WPMC 2016, respectively. He served as a guest editor of special issue on wireless big data in Journal of Communications and Information Networks in 2016.
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Qian, L., Zhu, J. & Zhang, S. Survey of wireless big data. J. Commun. Inf. Netw. 2, 1–18 (2017). https://doi.org/10.1007/s41650-017-0001-2
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DOI: https://doi.org/10.1007/s41650-017-0001-2