Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks

Hussain, B., Du, Q., Imran, A. and Imran, M. A. (2020) Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks. IEEE Transactions on Industrial Informatics, 16(8), pp. 4986-4996. (doi: 10.1109/TII.2019.2953201)

Abstract

Escalating cell outages and congestion-treated as anomalies-cost a substantial revenue loss to the cellular operators and severely affect subscriber quality of experience. Stateof-the-art literature applies feed-forward deep neural network at core network (CN) for the detection of above problems in a single cell; however, the solution is impractical as it will overload the CN that monitors thousands of cells at a time. Inspired from mobile edge computing and breakthroughs of deep convolutional neural networks (CNNs) in computer vision research, we split the network into several 100-cell regions each monitored by an edge server; and propose a framework that pre-processes raw call detail records having user activities to create an image-like volume, fed to a CNN model. The framework outputs a multilabeled vector identifying anomalous cell(s). Our results suggest that our solution can detect anomalies with up to 96% accuracy, and is scalable and expandable for industrial Internet of things environment.

• Item Type: Articles
• Additional Information: The research reported in this paper was supported in part by the National Natural Science Foundation of China under the Grant No. 61671371 and the Fundamental Research Funds for the Central Universities.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad
• Authors: Hussain, B., Du, Q., Imran, A., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Transactions on Industrial Informatics
• Publisher: IEEE
• ISSN: 1551-3203
• ISSN (Online): 1941-0050
• Published Online: 12 November 2019
• Copyright Holders: Copyright © 2019 IEEE
• First Published: First published in IEEE Transactions on Industrial Informatics 16(8): 4986-4996
• Publisher Policy: Reproduced in accordance with the publisher copyright policy