Survey of wireless big data

Review Paper


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.


wireless big data data driven wireless networks data aided network optimization 


  1. [1]
    V. D. Blondel., A. Decuyper, G. Krings. A survey of results on mobile phone datasets analysis [J]. EPJ data science, 2015, 4(1): 1.CrossRefGoogle Scholar
  2. [2]
    M. Lin and W. Hsu. Mining GPS data for mobility patterns: A survey [J]. Pervasive and mobile computing, 2014, 12: 1–16.CrossRefGoogle Scholar
  3. [3]
    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.MathSciNetGoogle Scholar
  4. [4]
    X. Zhang, Z. Yi, Z. Yan, et al. Social computing for mobile big data [J]. Computer, 2016, 49(9): 86–90.CrossRefGoogle Scholar
  5. [5]
    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.CrossRefGoogle Scholar
  6. [6]
    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.CrossRefGoogle Scholar
  7. [7]
    Y. He, F. R. Yu, N. Zhao, et al. Big data analytics in mobile cellular networks[J]. IEEE access, 2016, 4: 1985–1996.CrossRefGoogle Scholar
  8. [8]
    C. Zhang, R. C. Qiu. Massive mimo as a big data system: random matrix models and testbed [J]. IEEE access, 2015, 3: 837–851.CrossRefGoogle Scholar
  9. [9]
    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.CrossRefGoogle Scholar
  10. [10]
    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.Google Scholar
  11. [11]
    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.Google Scholar
  12. [12]
    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.CrossRefGoogle Scholar
  13. [13]
    K. Murphy. Machine Learning: A Probabilistic Perspective [M]. Cambridge: MIT Press, 2012.MATHGoogle Scholar
  14. [14]
    I. Goodfellow, Y. Bengio, A. Courville. Deep Learning [M]. Cambridge: MIT Press, 2016.Google Scholar
  15. [15]
    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.Google Scholar
  16. [16]
    Y. Le Cun, Y. Bengio, G. Hinton. deep learning[J]. Nature, 2015, 521(7553): 436–444.Google Scholar
  17. [17]
    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.CrossRefGoogle Scholar
  18. [18]
    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.Google Scholar
  19. [19]
    C. Yang. Learning methodologies for wireless big data networks: a Markovian game-theoretic perspective [J]. Neurocomputing, 2016, 174: 431–438.CrossRefGoogle Scholar
  20. [20]
    J. H. Zhang. The interdisciplinary research of big data and wireless channel: a cluster-nuclei based channel model [J](Accepted). China communication, 2016.Google Scholar
  21. [21]
    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.Google Scholar
  22. [22]
    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.Google Scholar
  23. [23]
    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.Google Scholar
  24. [24]
    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.CrossRefGoogle Scholar
  25. [25]
    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.Google Scholar
  26. [26]
    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.CrossRefGoogle Scholar
  27. [27]
    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.CrossRefGoogle Scholar
  28. [28]
    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.CrossRefGoogle Scholar
  29. [29]
    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.CrossRefGoogle Scholar
  30. [30]
    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.CrossRefGoogle Scholar
  31. [31]
    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.CrossRefGoogle Scholar
  32. [32]
    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.CrossRefGoogle Scholar
  33. [33]
    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.CrossRefGoogle Scholar
  34. [34]
    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.CrossRefGoogle Scholar
  35. [35]
    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.CrossRefGoogle Scholar
  36. [36]
    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.Google Scholar
  37. [37]
    C. Song, Z. Qu, N. Blumm, et al. Limits of predictability in human mobility [J]. Science, 2010, 327(5968): 1018–1021.MathSciNetCrossRefMATHGoogle Scholar
  38. [38]
    X. Lu, E. Wetter, N. Bharti, et al. Approaching the limit of predictability in human mobility [J]. Scientific reports, 2013, 3(2923): 1–9.Google Scholar
  39. [39]
    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.CrossRefGoogle Scholar
  40. [40]
    Y. Zhang. User mobility from the view of cellular data networks [C]//IEEE INFOCOM 2014-IEEE Conference on Computer Communications, Toronto, 2014: 1348–1356.CrossRefGoogle Scholar
  41. [41]
    X. Zhou, Z. Zhao, R. Li, et al. Human mobility patterns in cellular networks[J]. IEEE communications letters, 2013, 17(10): 1877–1880.CrossRefGoogle Scholar
  42. [42]
    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.CrossRefGoogle Scholar
  43. [43]
    C. Song, T. Koren, P.Wang, et al. Modelling the scaling properties of human mobility [J]. Nature physics, 2010, 6(10): 818–823.CrossRefGoogle Scholar
  44. [44]
    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.CrossRefGoogle Scholar
  45. [45]
    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.CrossRefGoogle Scholar
  46. [46]
    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.CrossRefGoogle Scholar
  47. [47]
    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.Google Scholar
  48. [48]
    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.Google Scholar
  49. [49]
    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.CrossRefGoogle Scholar
  50. [50]
    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.CrossRefGoogle Scholar
  51. [51]
    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.CrossRefGoogle Scholar
  52. [52]
    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.CrossRefGoogle Scholar
  53. [53]
    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.Google Scholar
  54. [54]
    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.CrossRefGoogle Scholar
  55. [56]
    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.Google Scholar
  56. [57]
    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.CrossRefGoogle Scholar
  57. [58]
    N. Ramdhan, M. Sliti, N. Boudriga. Codeword-based data collection protocol for optical Unmanned Aerial Vehicle networks [C]//HONET-ICT IEEE, 2016: 35–39.Google Scholar
  58. [59]
    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.Google Scholar
  59. [60]
    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.Google Scholar
  60. [61]
    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.CrossRefGoogle Scholar
  61. [62]
    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.Google Scholar
  62. [63]
    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.Google Scholar
  63. [64]
    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.CrossRefGoogle Scholar
  64. [65]
    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.Google Scholar

Copyright information

© Posts & Telecom Press and Springer Singapore 2017

Authors and Affiliations

  1. 1.CREDIT Research Center, Prairie View A&M UniversityTexas A&M University SystemPrairie ViewUSA
  2. 2.PCNSSUniversity of Science and Technology of ChinaHefeiChina
  3. 3.Key Laboratory of Wireless-Optical CommunicationsChinese Academy of Sciences, University of Science and Technology of ChinaHefeiChina

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