Recent Advances and Challenges in Mobile Big Data

Ahmed, Ejaz; Yaqoob, Ibrar; Hashem, Mohamed; Shuja, Junaid; Imran, Muhammad; Guizani, Nadra; Bakhsh, Sheikh Tahir

doi:10.1109/mcom.2018.1700294

Cited by 55 publications

(17 citation statements)

References 14 publications

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“…Appropriately mining these data can benefit multidisciplinary research fields and the industry in areas such mobile network management, social analysis, public transportation, personal services provision, and so on [36]. Network operators, however, could become overwhelmed when managing and analyzing massive amounts of heterogeneous mobile data [464]. Deep learning is probably the most powerful methodology that can overcoming this burden.…”

Section: A Mobile Big Data As a Prerequisitementioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,273

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Section: A Mobile Big Data As a Prerequisitementioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,273

838

View full text Add to dashboard Cite

show abstract

“…The emerging AI and ML are powerful and scalable to improve the quality of the next-generation wireless network. Mobile big data (MBD) [338,339] is a new research area combining mobile computing and big data, which poses great challenges on the source, analytics, applications, characteristics, and security. One of the core advantages of AI and ML lies in their data-driven characteristics [340].…”

Section: Ai and ML Technologies: An Overviewmentioning

confidence: 99%

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

1,109

495

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The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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“…Mobile computing and big data both have been flourishing fields of research on their own in recent time, however only a few studies explore the convergence of both domains [1]. Analyzing data collected by mobile devices enables businesses to gain novel insights form hitherto unrecognized sources [1]. This explains the existence of the relatively new research field of Mobile Big Data.…”

Section: Mobile Computing and Big Datamentioning

confidence: 99%

Digital Mobile Services for Consumer Banking: Conceptualization and Implementation of Location-based Consumer Credits

Wenzel-Ruelberg¹,

Baiyere²,

Lohmann³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Interactions of banks with their customers are increasingly shifting to web and mobile channels. Being at risk of losing the role of the customer agent to FinTechs and digital challenger banks, incumbent banks are seeking ways to exploit technologies such as mobile phones as channels to generate insights and to sell products that their customers need. For many banks the potential of mobile banking to individualize products and personalize services from information collected by the mobile device' sensory components are largely untapped. In this paper, we draw on design science research in exploiting spatio-temporal information to build an algorithmic model to target customers with credit offers. While past research aimed to solve similar problems mainly through customer segmentation, our approach demonstrates the benefits of having a transparent and interpretable decision model for each individual customer. Our artifact enables the development of digital products and services, without large-scale, often unavailable data.

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Recent Advances and Challenges in Mobile Big Data

Cited by 55 publications

References 14 publications

Deep Learning in Mobile and Wireless Networking: A Survey

Deep Learning in Mobile and Wireless Networking: A Survey

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Digital Mobile Services for Consumer Banking: Conceptualization and Implementation of Location-based Consumer Credits

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