Leveraging Intelligence from Network CDR Data for Interference Aware Energy Consumption Minimization

Zoha, Ahmed; Saeed, Arsalan; Farooq, Hasan; Rizwan, Ali; Imran, Ali; Imran, Muhammad Ali

doi:10.1109/tmc.2017.2773609

Cited by 17 publications

(19 citation statements)

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“…The data from mobile networks, for instance call details record (CDR) datasets, are also massive, incomplete and have misleading patterns. Analysis of such type of data is also challenging [61]. One may explore advanced machine learning techniques such as active learning, online learning, deep learning, distributed and parallel learning, transfer learning and representation learning to make sense of the complex data.…”

Section: Some Advanced Learning Methodsmentioning

confidence: 99%

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Big Data Perspective and Challenges in Next Generation Networks

2018

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With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks.

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Section: Some Advanced Learning Methodsmentioning

confidence: 99%

“…Cache Management [52,59] Minimize end-to-end delay, Maximum spectrum utilization Resource Optimization [51,61] Interference minimization, Network power optimization…”

Section: Name References Solutionmentioning

confidence: 99%

Big Data Perspective and Challenges in Next Generation Networks

2018

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“…Due to the increasing measures of data beyond smart cities and communication networks and the necessity for intelligent data analytics, the use of ML algorithms has become a reasonable response to the challenging cases across many divisions such as; entertainment, social and financial services, entertainment, transportation, and health care. Using the discussed ML algorithms and features in order to organise movement patterns that reveal relationships and predict system dynamics or human behaviour, system operators can make automated intelligent decisions without any human intervention [47][48][49][50][51]. Passenger motion and their activities through ML bring advantages to the transportation sector where passenger movement is recorded through RFID technology.…”

Section: Machine Learning Based Mobility Prediction Algorithmsmentioning

confidence: 99%

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

Asad

Ahmad

Hussain

et al. 2020

Sensors

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Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (<0.01), entropy (>7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity (<0.3) and energy (<0.01) prove the efficacy of the proposed encryption scheme.

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“…In particular, the authors in [33] identify that the user and network related information carried in the network data, were seldom employed to improve a network's performance. Similarly, the importance of useful insights obtained from data analysis is elaborated for different scenarios in studies undertaken in [34,35].…”

Section: Insights Into Cdrs Driven Traffic Optimization Approachmentioning

confidence: 99%

Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization

et al. 2019

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The information contained within Call Details records (CDRs) of mobile networks can be used to study the operational efficacy of cellular networks and behavioural pattern of mobile subscribers. In this study, we extract actionable insights from the CDR data and show that there exists a strong spatiotemporal predictability in real network traffic patterns. This knowledge can be leveraged by the mobile operators for effective network planning such as resource management and optimization. Motivated by this, we perform the spatiotemporal analysis of CDR data publicly available from Telecom Italia. Thus, on the basis of spatiotemporal insights, we propose a framework for mobile traffic classification. Experimental results show that the proposed model based on machine learning technique is able to accurately model and classify the network traffic patterns. Furthermore, we demonstrate the application of such insights for resource optimisation.

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Leveraging Intelligence from Network CDR Data for Interference Aware Energy Consumption Minimization

Cited by 17 publications

References 50 publications

Big Data Perspective and Challenges in Next Generation Networks

Big Data Perspective and Challenges in Next Generation Networks

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization

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