Big Data Analytics in Mobile Cellular Networks

He, Ying; Yu, F. Richard; Zhao, Nan; Yin, Hongxi; Hu, Yao; Qiu, Robert C.

doi:10.1109/access.2016.2540520

Cited by 147 publications

(59 citation statements)

References 37 publications

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“…Some mesh network results exist but generally assume an always working and reachable network, which may not be the case in a general purpose mesh network. Big Data techniques have been proposed to gather data from the access network, especially mobile cellular networks, but these techniques do not apply to the management of the network itself.…”

Section: State Of the Artmentioning

confidence: 99%

A Big Data and machine learning approach for network monitoring and security

Maccari

Passerini

2018

Security and Privacy

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In the last decade the performances of 802.11 (Wi‐Fi) devices skyrocketed. Today it is possible to realize gigabit wireless links spanning across kilometers at a fraction of the cost of the wired equivalent. In the same period, mesh network evolved from being experimental tools confined into university labs, to systems running in several real world scenarios. Mesh networks can now provide city‐wide coverage and can compete on the market of Internet access. Yet, being wireless distributed networks, mesh networks are still hard to maintain and monitor. This paper explains how today we can perform monitoring, anomaly detection and root cause analysis in mesh networks using Big Data techniques. It first describes the architecture of a modern mesh network, it justifies the use of Big Data techniques and provides a design for the storage and analysis of Big Data produced by a large‐scale mesh network. While proposing a generic infrastructure, we focus on its application in the security domain.

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Section: State Of the Artmentioning

confidence: 99%

A Big Data and machine learning approach for network monitoring and security

Maccari

Passerini

2018

Security and Privacy

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“…In Ref. [7], a big data analytic unified data model based on the random matrix theory and machine learning in mobile cellular networks is studied. Several examples of data types have been presented to clarify the performance of big data analytic based on random matrix theory, such as big signaling data, big traffic data, big location data, big radio waveforms data, and big heterogeneous data, in which the high dimensionality of the spatial-temporal datasets is exploited, and the interrelationship and unique characteristics between big data and mobile cellular networks is addressed.…”

Section: Data Modelmentioning

confidence: 99%

Survey of wireless big data

Qian

Zhu

Zhang

2017

J. Commun. Inf. Netw.

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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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“…257 Government and corporate regulations for privacy and data protection continue to play a fundamental role in protecting the sensitive aspects of big data. 258 According to Marjani et al, most people are reluctant to rely on systems, which do not provide solid service level agreement conditions regarding theft or misuse of personal information. 259 However, the cost to consumers who choose not to disclose their personal information in today's highly networked world can be substantial .…”

Section: Dependencymentioning

confidence: 99%

“…268 For instance, mobile cellular networks have a large amount of sensitive personal information, such as subscriber's names, ID numbers, physical locations, images files, top contacts and passwords. 269 If operators fail to leverage big data in a proper way, big data analytics will bring privacy and security issues to areas such as mobile cellular networks.…”

Section: Dependencymentioning

confidence: 99%

“…328 He et al state that more advanced algorithms are needed to extract correlations from the data, while allowing different levels of privacy. 329 An alleged cause may be correlated with desirable outcomes, but the correlation could also be due to omitted variables or even reversed causality. If the alleged cause does not affect the outcome, then changing it will not produce the desired change in the outcome variable and the resources spent on the action will be wasted.…”

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confidence: 99%

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Lists of Ethical, Legal, Societal and Economic Issues of Big Data Technologies

et al. 2017

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Data-driven innovation is deeply transforming society and the economy. Although there are potentially enormous economic and social benefits this innovation also brings new challenges for individual and collective privacy, security, as well as democracy and participation. The main objective of the CSA e-SIDES is to complement the research on privacy-preserving big data technologies, by analyzing, mapping and clearly identifying the main societal and ethical challenges emerging from the adoption of big data technologies, conforming to the principles of responsible research and innovation; setting up and organizing a sustainable dialogue between industry, research and social actors, as well as networking with the main Research and Innovation Actions and Large Scale Pilots and other framework program projects interested in these issues. It will investigate stakeholders' concerns, and collect their input, framing these results in a clear conceptual framework showing the potential trade-offs between conflicting needs and providing a basis to validate privacy-preserving technologies. It will prepare and widely disseminate community shared conclusions and recommendations highlighting the best way to ultimately build confidence of citizens and businesses towards big data and the data economy. Second, the legal perspective focuses on the lists of human rights derived from the European Convention on Human Rights (ECHR) and the EU Charter of Fundamental Rights (the EU Charter), which together constitute the main legal framework for the EU in the field of human rights. The rights of particular relevance in the context of big data technologies are the rights to private and family life, personal data protection, freedom of expression and information, freedom of assembly and association, nondiscrimination, fair trial and consumer protection. By analysing the normative scope of each of these human rights, looking at both legislation and case law of the European courts and the way in which big data technologies challenge different aspects of each human right at stake, the legal part distils the list of the most relevant issues at the nexus of big data technologies and human rights in the EU. The legal issues identified are: lack of transparency, vagueness of the concept of harm, accountability, proportionality, establishing a regulatory framework and the role of private actors in applying fundamental rights.Third, the societal perspective makes use of the extensive literature on Societal Impact Assessments (SIA). The analysis of literature was combined with a review of research project propositions and complemented by discussions at two workshops. Societal impact is very generally understood as changes to one or more of a number of elements of social life: people's way of life, their culture, their community, their political systems, their environment, their health and well-being, their personal and property and their fears and aspirations. The societal issues are mapped by examining different actors and distinctions between thes...

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Big Data Analytics in Mobile Cellular Networks

Cited by 147 publications

References 37 publications

A Big Data and machine learning approach for network monitoring and security

A Big Data and machine learning approach for network monitoring and security

Survey of wireless big data

Lists of Ethical, Legal, Societal and Economic Issues of Big Data Technologies

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