A Mobility Analytical Framework for Big Mobile Data in Densely Populated Area

Qiao, Yuanyuan; Cheng, Yihang; Yang, Jie; Li, Jiajia; Kato, Nei

doi:10.1109/tvt.2016.2553182

Cited by 82 publications

(23 citation statements)

References 45 publications

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“…Big Data management tools under umbrella of Hadoop ecosystem are potential enablers to deal with the acute dynamicity of the Big Data. The main components of Big data processing platform consist of [107]: 1) Trasmission Module consisting of Flume [108] and Kafka [109] that uploads network data in real time with stable transmission to the cloud platform. 2) Storage Module consisting of Distributed File System (HDFS) [110] and HBase [111] with high fault tolerance capability.…”

Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

“…Although no existing work target proactive CoMP clustering leveraging Big Data explicitly yet, there exist certain works, wherein dynamic CoMP clustering is performed targeted at hotspots, assuming hotspot location are already somehow known by the network. The Big Data processing framework presented above cannot only identify the future hotspots but it can also predict future load, e.g using data of mobility traces and past CDR records [107], [116], [117]. Once a hotspot is characterized, the appropriate CoMP algorithm can be leveraged to cope with high capacity demands for hotspots.…”

Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

“…The work in [107] has performed Big Data collection, storage, and pre-processing of CDRs and has proposed:…”

Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

See 2 more Smart Citations

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

150

109

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Abstract-Mobile data traffic grew by 74% in 2015 and it's expected to grow 8-fold by 2020. Future wireless networks will need to deploy massive number of small cells to cope with this increasing demand. Dense deployment of small cells will require advanced interference mitigation techniques to improve spectral efficiency and enhance much needed capacity. Coordinated multi-point (CoMP) is a key feature for mitigating intercell interference, improve throughput and cell edge performance. However, cooperation will need to be limited to few cells only due to additional overhead required by CoMP due to channel state information (CSI) exchange, scheduling complexity and additional backhaul limitation. Hence small CoMP clusters will need to be formed in the network. This article surveys the stateof-the-art on one of the key challenges of CoMP implementation: CoMP clustering. As a starting point, we present the need for CoMP, the clustering challenge for 5G wireless networks and provide a brief essential background about CoMP and the enabling network architectures. We then provide the key framework for CoMP clustering and introduce self organisation as an important concept for effective CoMP clustering to maximise CoMP gains. Next, we present two novel taxonomies on existing CoMP clustering solutions, based on self organisation and aimed objective function. Strengths and weaknesses of the available clustering solutions in the literature are critically discussed. We then discuss future research areas and potential approaches for CoMP clustering. We present a future outlook on the utilisation of Big Data in cellular context to support proactive CoMP clustering based on prediction modelling. Finally we conclude this paper with a summary of lessons learnt in this field. This article aims to be a key guide for anyone who wants to research on CoMP clustering for future wireless networks.

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Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

See 1 more Smart Citation

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

150

109

View full text Add to dashboard Cite

show abstract

“…Passively collecting human mobile traffic data while users are accessing the mobile Internet has many advantages like low energy consumption. In general, the mobile big data covers a wide range and a great number of populations with fine time granularity, which gives us an opportunity to study human mobility at a scale that other data sources are very hard to reach [152]. Novel offline user mobility models developed based on the mobile big data are expected to benefit many fields, including urban planning, road traffic engineering, telecommunication network construction, and human sociology [145].…”

Section: Analyses Of Human Online and Offline Behavior Based On Mobilmentioning

confidence: 99%

A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

Xie

Song

et al. 2018

Wireless Communications and Mobile Computing

Self Cite

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This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

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“…[10] introduced a mobility analytical framework for big mobile data, based on real data traffic collected from 2G/3G/4G networks cov-ering nearly 7 000 000 people. In order to construct the history trajectories of users, the authors applied different rules to extract users locations from different data sources, and reduce oscillations between the cell towers.…”

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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A Mobility Analytical Framework for Big Mobile Data in Densely Populated Area

Cited by 82 publications

References 45 publications

Coordinated Multi-Point Clustering Schemes: A Survey

Coordinated Multi-Point Clustering Schemes: A Survey

A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

Survey of wireless big data

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