Big Data Analytics for Prediction of Mobile Users Movement using Neural Networks

No Thumbnail Available



Journal Title

Journal ISSN

Volume Title


Addis Ababa University


The rise of data warehouses and the rise of multi-media, social media and the Internet of Things (IoT) generate an increasing Volume of structured, semi-structured and unstructured data. Towards the investigation of these large Volumes of data, Big Data and data analytics have become emerging research fields attracting the attention of academia, industry and governments. Researchers, entrepreneurs, decision makers and problem solvers view ‘Big Data’ as an important tool used to revolutionize various industries and sectors, such as business, health-care, retail, research, education and public administration. In this context, a general view on analysis of Big Data, especially in telecommunications industry is proposed. In order to allocate scarce resources efficiently the location of mobile users should be predicted. So, this work focuses on analysis of data for mobile users movement prediction in telecommunications network. The objective of this work is to process and analyze obtained samples of OpenCelliD data by means of Neural Network and provide as accurate mobile users movement prediction as possible. More specifically, Modified Levenberg-Marquardt (LM) algorithm is presented as an effective algorithm for movement prediction. Obtained result from prediction is optimized by iteration method designed for finding the best possible combination of Neural Network parameters. Efficiency of mobile users movement prediction is verified by simulation in Matrix Laboratory (MATLAB). Simulation results show sufficient accuracy for wide use of prediction for mobile networks optimization or services exploiting prediction of mobile users movement. Measured results fully reflect real solution for telecommunications industry and can help to plan activities connected with mobile users movement in a given area.



Big Data, Data Analytics, Location-Based Services (LBS), Telecommunications, Prediction, Neural Network, Non-Linear Autoregressive with External Input Neural Network (NARXNN), Dataset, Training, Optimization