Big Data Processing, Analysis and Applications in Mobile Cellular Networks

Brdar, Sanja; Novovic, Olivera; Grujić, Nastasija; González–Vélez, Horacio; Truică, Ciprian-Octavian; Benkner, Siegfried; Bajrovic, Enes; Papadopoulos, Apostolos N.

doi:10.1007/978-3-030-16272-6_6

Cited by 11 publications

(10 citation statements)

References 58 publications

(80 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Seufert et al [14] make analysis on a public WiFi dataset and then bring forward a simple WiFi hotspot model in smart city. Brdar et al [15] analyze the mobile phone data and then make discussions on knowledge retrieval from the dataset with various contexts of application scenarios.…”

Section: B Urban Hotspot Analysis Schemesmentioning

confidence: 99%

Edge Oriented Urban Hotspot Prediction for Human-Centric Internet of Things

Zeng

Xiang

2021

IEEE Access

View full text Add to dashboard Cite

Mobile edge computing works well with the rapid growth of human-centric IoT applications by providing timely and in-situ data caching and processing. Edge oriented urban hotspot prediction is to predict the high traffic regions in case to provide basis for further deployment and replenishment of edge servers. Most of the existing hotspot analysis work is not well suited for human-centric IoT application scenarios especially for large-scale urban city areas. Instead, in this paper we propose edge-oriented hotspot prediction scheme based on information Interactive Graph Attention Networks (I-GAN) for urban city within the human-centric IoT and MEC application scenarios. I-GAN makes reasoning on the user service needs by learning the interactive relations of multifaceted human-centric factors: including online human behavior and offline crowd flow through graph attention networks. On the other hand, I-GAN makes representation of urban-scale edge service capability by graph embeddings through inductive learning with distributed multi-layer graph convolutions. Based on it, I-GAN makes matching between them and then predicts the edge-oriented hotspots with time variance. The performance evaluations are based on realistic dataset of a southern city of China provided by China Unicom. I-GAN is compared with the other related hotspot analysis schemes. The results show that I-GAN is with much better prediction accuracy with multiple time dimensions.

show abstract

Section: B Urban Hotspot Analysis Schemesmentioning

confidence: 99%

Edge Oriented Urban Hotspot Prediction for Human-Centric Internet of Things

Zeng

Xiang

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Their objective was to find the hotspots in that network and measure the interactions among them. [29]. They presented several measures, mainly focused on graph theory and machine learning, and discussed various steps involved in knowledge recovery from raw telecom data.…”

Section: Related Workmentioning

confidence: 99%

Hotspots Analysis Using Cyber-Physical-Social System for a Smart City

Amin

Choi

2020

IEEE Access

View full text Add to dashboard Cite

Internet of things plays a vital role in providing various services to users. Significant volumes of data are generated from the communication between a large numbers of heterogeneous devices over the Internet. Big data technology is generally used to handle the large volume of data. Complex networks are graphs (networks) having non-trivial topological features, such as random graphs and lattices. Big data of complex networks concerns big data methods that can be used to analyze massive structural data sets, including considerably large networks and sets of graphs. This study is based on the critical phenomenon arising in complex networks that enable us to analytically predict the hotspots in smart cities. Hotspots are places with significantly high communication traffic relative to others. In this study, we propose a cyber-physical-social system for the analysis of high communication traffic hotspots using telecom data. The proposed model constructs a graph, and perform social network analysis on it. The process of hotspot extraction is performed, followed by social network analysis, which is conducted by quantifying the importance of each hotspot based on network metrics. These metrics aid in determining the importance of each hotspot in a telecom data network. Our objective is to prioritize different areas and detect hotspots quickly. Our results indicate that the proposed model has an efficiency comparable with that of state of the art methods. This research study will be helpful for urban planning and development, as well as in upgrading telecommunication infrastructure.

show abstract

“…Analytical pipelines over such data sets could be computationally expensive, while at the same time delivery of the results needs to be efficient since many applications require almost real-time response. Brdar et al [35] provided a broad overview of the entire workflow starting from raw data access, followed by demands for analytical performance and data fusion, to the final application. They pointed out the critical challenges in mobile phone data analysis that need to be addressed, in order to disclose the hidden potential of the data.…”

Section: Related Workmentioning

confidence: 99%

“…Spatial aggregation is performed in order to hide the exact location of Radio Base Station where telecom traffic is distributed onto cells in regular grid covering predefined spatial areas. Another data anonymization approach is to add noise to original data up to the level not affecting the statistics significantly while preserving the users' privacy [35].…”

Section: Data Description and Preparationmentioning

confidence: 99%

Uncovering the Relationship between Human Connectivity Dynamics and Land Use

Novovic

Brdar

Mesaroš

et al. 2020

IJGI

Self Cite

View full text Add to dashboard Cite

CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform an analysis of CDR data for the city of Milan that originate from Telecom Italia Big Data Challenge. A set of graphs is generated from aggregated CDR data, where each node represents a centroid of an RBS (Radio Base Station) polygon, and each edge represents aggregated telecom traffic between two RBSs. To explore the community structure, we apply a modularity-based algorithm. Community structure between days is highly dynamic, with variations in number, size and spatial distribution. One general rule observed is that communities formed over the urban core of the city are small in size and prone to dynamic change in spatial distribution, while communities formed in the suburban areas are larger in size and more consistent with respect to their spatial distribution. To evaluate the dynamics of change in community structure between days, we introduced different graph based and spatial community properties which contain latent footprint of human dynamics. We created land use profiles for each RBS polygon based on the Copernicus Land Monitoring Service Urban Atlas data set to quantify the correlation and predictivennes of human dynamics properties based on land use. The results reveal a strong correlation between some properties and land use which motivated us to further explore this topic. The proposed methodology has been implemented in the programming language Scala inside the Apache Spark engine to support the most computationally intensive tasks and in Python using the rich portfolio of data analytics and machine learning libraries for the less demanding tasks.

show abstract

Big Data Processing, Analysis and Applications in Mobile Cellular Networks

Cited by 11 publications

References 58 publications

Edge Oriented Urban Hotspot Prediction for Human-Centric Internet of Things

Edge Oriented Urban Hotspot Prediction for Human-Centric Internet of Things

Hotspots Analysis Using Cyber-Physical-Social System for a Smart City

Uncovering the Relationship between Human Connectivity Dynamics and Land Use

Contact Info

Product

Resources

About