Big Data in Telecommunications

Big Data ◽  
2016 ◽  
pp. 778-792
Author(s):  
Yen Pei Tay ◽  
Vasaki Ponnusamy ◽  
Lam Hong Lee
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Traffic Congestion ◽  
Big Data Analytics ◽  
Mobile Network ◽  
Consumer Electronics ◽  
Data Traffic ◽  
Network Discovery ◽  
Crowd Intelligence ◽  
4G Lte

The meteoric rise of smart devices in dominating worldwide consumer electronics market complemented with data-hungry mobile applications and widely accessible heterogeneous networks e.g. 3G, 4G LTE and Wi-Fi, have elevated Mobile Internet from a ‘nice-to-have' to a mandatory feature on every mobile computing device. This has spurred serious data traffic congestion on mobile networks as a consequence. The nature of mobile network traffic today is more like little Data Tsunami, unpredictable in terms of time and location while pounding the access networks with waves of data streams. This chapter explains how Big Data analytics can be applied to understand the Device-Network-Application (DNA) dimensions in annotating mobile connectivity routine and how Simplify, a seamless network discovery solution developed at Nextwave Technology, can be extended to leverage crowd intelligence in predicting and collaboratively shaping mobile data traffic towards achieving real-time network congestion control. The chapter also presents the Big Data architecture hosted on Google Cloud Platform powering the backbone behind Simplify in realizing its intelligent traffic steering solution.

Big Data in Telecommunications

2015 ◽  
pp. 67-81
Author(s):  
Yen Pei Tay ◽  
Vasaki Ponnusamy ◽  
Lam Hong Lee
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Traffic Congestion ◽  
Big Data Analytics ◽  
Mobile Network ◽  
Consumer Electronics ◽  
Data Traffic ◽  
Network Discovery ◽  
Crowd Intelligence ◽  
4G Lte

The meteoric rise of smart devices in dominating worldwide consumer electronics market complemented with data-hungry mobile applications and widely accessible heterogeneous networks e.g. 3G, 4G LTE and Wi-Fi, have elevated Mobile Internet from a ‘nice-to-have' to a mandatory feature on every mobile computing device. This has spurred serious data traffic congestion on mobile networks as a consequence. The nature of mobile network traffic today is more like little Data Tsunami, unpredictable in terms of time and location while pounding the access networks with waves of data streams. This chapter explains how Big Data analytics can be applied to understand the Device-Network-Application (DNA) dimensions in annotating mobile connectivity routine and how Simplify, a seamless network discovery solution developed at Nextwave Technology, can be extended to leverage crowd intelligence in predicting and collaboratively shaping mobile data traffic towards achieving real-time network congestion control. The chapter also presents the Big Data architecture hosted on Google Cloud Platform powering the backbone behind Simplify in realizing its intelligent traffic steering solution.

scholarly journals Towards a software-based mobility management for 5G: An experimental approach for flattened network architectures

2020 ◽  
Vol 17 (1) ◽  
pp. 51-70
Author(s):  
Jesús Calle-Cancho ◽  
José-Manuel Mendoza-Rubio ◽  
José-Luis González-Sánchez ◽  
David Cortés-Polo ◽  
Javier Carmona-Murillo
Keyword(s):  
Mobile Networks ◽  
Mobility Management ◽  
Network Performance ◽  
Mobile Network ◽  
Network Control ◽  
Network Services ◽  
Smart Devices ◽  
Network Architectures ◽  
Data Traffic ◽  
Management Capability

The number of mobile subscribers, as well as the data traffic generated by them, is increasing exponentially with the growth of wireless smart devices and the number of network services that they can support. This significant growth is pushing mobile network operators towards new solutions to improve their network performance and efficiency. Thus, the appearance of Software Defined Networking (SDN) can overcome the limitations of current deployments through decoupling the network control plane from the data plane, allowing higher flexibility and programmability to the network. In this context, the process of handling user mobility becomes an essential part of future mobile networks. Taking advantage of the benefits that SDN brings, in this article we present a novel mobility management solution. This proposal avoids the use of IP-IP tunnels and it adds the dynamic flow management capability provided by SDN. In order to analyse performance, an analytical model is developed to compare it with NB-DMM (Network-based DMM), one of the main DMM (Distributed Mobility Management) solutions. Additionally, performance is also evaluated with an experimental testbed. The results allow handover latency in real scenarios and numerical investigations to be measured, and also show that SR-DMM achieves better efficiency in terms of signaling and routing cost than NB-DMM solution.

Smart and Connected Cities Through Technologies

2019 ◽  
pp. 1-24 ◽  
Author(s):  
Vrushali Gajanan Kadam ◽  
Sharvari Chandrashekhar Tamane ◽  
Vijender Kumar Solanki
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Smart City ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Crowd Sensing ◽  
The World ◽  
Important Challenge ◽  
Mobile Crowd

The world is growing and energy conservation is a very important challenge for the engineering domain. The emergence of smart cities is one possible solution for the same, as it claims that energy and resources are saved in the smart city infrastructure. This chapter is divided into five sections. Section 1 gives the past, present, and future of the living style. It gives the representation from rural, urban, to smart city. Section 2 gives the explanations of four pillars of big data, and through grid, a big data analysis is presented in the chapter. Section 3 started with the case study on smart grid. It comprises traffic congestion and their prospective solution through big data analytics. Section 4 starts from the mobile crowd sensing. It discusses a good elaboration on crowd sensing whereas Section 5 discusses the smart city approach. Important issues like lighting, parking, and traffic were taken into consideration.

A Closer Look at Direct Carrier Billing

2020 ◽  
Vol 10 (4) ◽  
pp. 18-40
Author(s):  
Lorena Herrera López
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mobile Network ◽  
Marketing Channels ◽  
Digital Marketing ◽  
Mobile Service ◽  
Web Analytics ◽  
Network Operator ◽  
Over The Top

The impulse to digitalization by telecom operators requires the commercialization of over-the-top services (OTT) based on the fine understanding and prediction of customer behaviour through pattern recognition involving big data, resulting in an essential part of web analytics and digital marketing. The objective of this research is to analyse factors influencing the purchase and use of a mobile game commercialized by a mobile network operator (MNO), through different digital marketing channels and using direct carrier billing (DCB) as payment channel. The novelty contribution of this study is twofold. Firstly, it assesses determinants related to the purchase and use of a mobile service through the analysis of variables identified in the scientific literature's review. In addition, it also incorporates a set of variables based on data retrieved from big data analytics. Secondly, this research analyses the willingness of consumers to pay through DCB.

scholarly journals Handover with Buffering for Distributed Mobility Management in Software Defined Mobile Networks

2018 ◽  
Vol 6 (1) ◽  
pp. 26-40
Author(s):  
Battulga Davaasambuu
Keyword(s):  
Mobile Networks ◽  
Mobility Management ◽  
Single Point ◽  
Mobile Network ◽  
Base Stations ◽  
Data Traffic ◽  
Distributed Mobility Management ◽  
Mobile Cellular ◽  
Network Technologies

The rapidly-growing number of mobile subscribers has led to the creation of a large number of signalling messages. This makes it difficult to efficiently handle the mobility of subscribers in mobile cellular networks. The long-term evolution (LTE) architecture provides software-defined networking (SDN) to meet the requirements of 5G networks and to forward massive mobile data traffic. The SDN solution proposes separation of the control and data planes of a network. Centralized mobility management (CMM) is widely used in current mobile network technologies, such as 4G networks. One of the problems related to CMM is a single point of failure. To solve the problems of CMM and in order to provide for efficient mobility management, IETF has developed a solution called distributed mobility management (DMM), in which mobility is handled via the nearest mobility anchor. In this paper, we propose a DMM solution with handover operations for SDN-enabled mobile networks. The advantage of the proposed solution is that intra and inter handover procedures are defined with the data buffering and forwarding processes between base stations and mobility anchors. We adopt a simulation model to evaluate and compare the proposed solution with the existing solution in terms of handover latency, packet loss and handover failures.

scholarly journals The dynamics of traffic congestion: a specific look into malaysian scenario and the plausible solutions to eradicate it using machine learning

2019 ◽  
Vol 15 (2) ◽  
pp. 1086
Author(s):  
M. Ali ◽  
T. K. Sheng ◽  
K. M. Yusof ◽  
M. R. Suhaili ◽  
N. E. Ghazali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Traffic Congestion ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Active Role ◽  
Machine Learning Algorithms ◽  
Asia Pacific ◽  
The Government

Transportation has been considered as the backbone of the economy for the past many years. Unfortunately, since few years due to the uncontrolled urbanization and inadequate planning, countries are facing problem of congestion. The congestion is hindering the economic growth and also causing environmental issues. This has caused serious concerns among the major economies of the world, especially in Asia-Pacific region. Many countries are playing an active role in eradicating this problem and some have been quite successful so far. Malaysia, being a major ASEAN economy is also tackling with this huge problem. The authorities are committed to solve the issue. In this regard, solving the issue leveraging the use of big data analytics has become crucial. The authorities can form a complete robust framework based on big data analytics and decision making process to solve the issue effectively. The work focuses and observes the traffic data samples and analyzes the accuracy of machine learning algorithms, which helps in decision making. Yet, here is a lot to be done if the government needs to solve the problem effectively. Supposedly, a comprehensive big data transport framework leveraging machine learning, is one way to solve the issue.

scholarly journals Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

2021 ◽  
Author(s):  
Abdelfatteh Haidine ◽  
Fatima Zahra Salmam ◽  
Abdelhak Aqqal ◽  
Aziz Dahbi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Cost Effective ◽  
Mobile Internet ◽  
Network Function ◽  
New Radio ◽  
4G Lte

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed.

The Network Infrastructures for Big Data Analytics

2015 ◽  
pp. 971-999
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Ground Level ◽  
Consumer Electronics ◽  
Data Generation ◽  
Personal Lives ◽  
The Status ◽  
Network Infrastructures ◽  
The Stability

The most delectable factor here is that the stability and maturity of networking and communication technologies enable the seamless and spontaneous interconnectivity of diverse and distributed consumer electronics, electrical, mechanical, and manufacturing devices at ground level and a bevy of services (Web, enterprise, cloud, embedded, analytical, etc.) at cyber level. Any tangible artefact and article gets connected with another to get the right and relevant empowerment, which in turn facilitates more data generation and transmission. Regulated interactions amongst digitalized entities have put a stimulating foundation for hitherto unforeseen and creative new capabilities and competencies. In short, data has grandly acquired the status of an asset not only in business organizations but also in personal lives, and hence, the data gathering, storage, and leverage tasks are fast-growing. With the data explosion happening feverishly, the discipline of big data computing and analytics has become a much-discoursed and deliberated domain of study and research. In this chapter, the authors discuss the emerging and evolving network infrastructures and architectures for big data analytics.

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