Proactive Anomaly Detection Model for eHealth-Enabled Data in Next Generation Cellular Networks

Hammami, Seif Eddine; Moungla, Hassine; Afifi, Hossam

doi:10.1109/icc.2018.8422516

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Hence, the coordinator chooses the nearest charging station to the cell to be served. Finally, (7) ensures that the designated drone has sufficient energy to return to the charging station while (8) determines that the collected data is lower than the total capacity of the drone.…”

Section: B Problem Formulationmentioning

confidence: 99%

“…CELLPAD detected anomalies using machine-learning regression analysis and was tested on two types of anomalies: sudden drops and correlation changes. [8] proposes an online anomaly detection tool based on Support Vector Regression machine learning method, while work in [9] compared the Support Vector Machines prediction algorithm with two other algorithms named Multi-Layer Perceptron and Multi-Layer Perceptron with Weight Decay. [10] presents a spatiotemporal mathematical model for IoT devices and modeled the uplink channel stochastically.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Range-based Anomaly Detection in Drone-assisted Cellular Networks

Boucetta¹,

Nour²,

Hammami³

et al. 2019

2019 15th International Wireless Communications &Amp; Mobile Computing Conference (IWCMC)

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Stimulated by the emerging Internet of Things (IoT) applications and their massive generated data, the cellular providers are introducing various IoT functionalities into their networks architecture. They should integrate intelligent and autonomous mechanisms that are able to detect sudden and anomalous behavior issues. In this paper, we present an adaptive anomaly detection approach in cellular networks consisting of two parts: the detection of overloaded base-stations using machine learning algorithm (LSTM-Long Short-Term Memory) and the deployment of drones as mobile base-stations that support and back up the overloaded cells. The proposed approach is validated using real dataset extracted from the CDR of Milan combined with semi-synthetic eHealth data. Initially, The LSTM algorithm analyzes the impact of eHealth applications on cellular networks and identifies cells with peak demands. Then, drones are deployed to collect the requested data from these cells. The obtained results show that the use of drones improves the quality of service and provides a better network performance.

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Section: B Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Range-based Anomaly Detection in Drone-assisted Cellular Networks

Boucetta¹,

Nour²,

Hammami³

et al. 2019

2019 15th International Wireless Communications &Amp; Mobile Computing Conference (IWCMC)

Self Cite

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“…In (Wu, Lee, Li, Pan, & Zhang, 2018), anomalies defined as sudden drops and correlation changes of KPIs in a LTE network are identified via regression based anomaly detection. The application of machine learning for smart nextgeneration wireless networks (5G) is presented in (Jiang et al 2017;Hammami, Moungla, & Afifi, 2018;Polese et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Anomaly Detection as an Emerging Trend in Telecommunications

Đorđević¹,

Milošević²,

Poledica³

2020

Manage: J. Sustainable Bus. Manage, Solutions Emerging Econ.

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Research Question: This paper investigates into how machine learning can be applied for the purpose of detecting anomalies in the data describing transport component within the cellular network. Motivation: In the field of telecommunications, terabytes of data are generated each hour. This makes the manual analysis almost impossible to perform. There are thousands of components whose behaviour needs to be monitored, since anomalous behaviour could indicate a possible failure that can lead to network degradation, huge maintenance costs, and finally – a bad user experience. Our goal is to try to catch anomalous behaviour automatically, and thus help domain experts when performing drill down analysis of the degraded base stations and their key performance indicators (KPIs). Idea: The main idea of this paper is to empirically evaluate the application of machine learning for the problem of anomaly detection, in the field of telecommunications, specifically to long term evolution (LTE) networks. Data: Data used in the analysis contains information about base transceiver stations (BTS) behaviour through the time. The data are gathered from a cellular network provider located in Serbia. The data are collected on an hourly basis, for a period of two weeks, resulting in almost 700 thousand rows. The behaviour is assessed by 96 transport KPIs coming from BTS, describing the package losses, delays, transmission success rates, etc. Tools: Two main algorithms, ensemble-based Isolation Forest and autoencoder neural network, are elaborated and applied in order to identify patterns of anomalous behaviour. Findings: The results show that machine learning can be successfully applied in the field of LTE networks for the problem of anomaly detection. Machine learning can significantly reduce the time needed for the domain experts to identify anomalies within the network. In addition to time efficiency, one of the algorithms tested is able to identify anomalous KPIs separately, which is crucial when performing root cause analysis, by using drill-down approach, in order to identify which component is degraded. Contribution: This paper enriches existing research related to anomaly detection in LTE networks and provides an innovative approach to automated root-cause analysis of network degradation.

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“…World Health Organization (WHO) recently reports [1] a global health workforce shortage of 12.9 million during the coming decade. This expected shortage accompanied by various other factors have inspired a slow but steady paradigm shift from conventional healthcare to the smart healthcare [2], [3]. The smart healthcare enables patients with round the clock monitoring and feedback and is also expected to automate critical operations inside ICU [4].…”

Section: Introductionmentioning

confidence: 99%

A Federated Filtering Framework for Internet of Medical Things

Sanyal

Wang

Nour

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IoMT) are resource constrained in power and computational capabilities. The IoMT devices are continuously pushing their readings to the remote cloud servers for real-time data analytics, that causes faster drainage of the device battery. Moreover, other demerits of continuous centralizing of data include exposed privacy and high latency. This paper presents a novel Federated Filtering Framework for IoMT devices which is based on the prediction of data at the central fog server using shared models provided by the local IoMT devices. The fog server performs model averaging to predict the aggregated data matrix and also computes filter parameters for local IoMT devices. Two significant theoretical contributions of this paper are the global tolerable perturbation error (T olF ) and the local filtering parameter (δ); where the former controls the decision-making accuracy due to eigenvalue perturbation and the later balances the tradeoff between the communication overhead and perturbation error of the aggregated data matrix (predicted matrix) at the fog server. Experimental evaluation based on real healthcare data demonstrates that the proposed scheme saves upto 95% of the communication cost while maintaining reasonable data privacy and low latency.

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Proactive Anomaly Detection Model for eHealth-Enabled Data in Next Generation Cellular Networks

Cited by 8 publications

References 11 publications

Adaptive Range-based Anomaly Detection in Drone-assisted Cellular Networks

Adaptive Range-based Anomaly Detection in Drone-assisted Cellular Networks

Machine Learning Based Anomaly Detection as an Emerging Trend in Telecommunications

A Federated Filtering Framework for Internet of Medical Things

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