Handover Prediction Integrated with Service Migration in 5G Systems

Abdah, Hadeel; Barraca, João Paulo; Aguiar, Rui L.

doi:10.1109/icc40277.2020.9149426

Cited by 13 publications

(15 citation statements)

References 22 publications

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“…The primary purpose of this technique is to maintain the connections between resources when they are reallocated or fill out the video buffer with low-definition video frames before the user loses the connection. In addition, a recent study proposed clustering and classifying algorithms to streamline the 5G handover procedure and enhance the network connectivity [ 4 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several proposed handover prediction methods utilize machine learning models (e.g., [ 3 , 4 ]) to obtain better communication on terrestrial sensor networks. Other works applying machine learning models to improve the handover prediction problem in UWSNs include [ 1 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed prediction method is based on a machine learning system, where the pattern of the received signal strength indicator (RSSI) is used to determine a future handover over time. In [ 4 ], the authors proposed two different methods to predict the handovers of a mobile user using the user’s behavior information given by the networks. To validate their method, the authors compared the performance of the proposed handover prediction mechanism to decision tree classification and K-nearest neighbors.…”

Section: Introductionmentioning

confidence: 99%

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A Robust UWSN Handover Prediction System Using Ensemble Learning

Eldesouky

Bekhit

Fathalla

et al. 2021

Sensors

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The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Robust UWSN Handover Prediction System Using Ensemble Learning

Eldesouky

Bekhit

Fathalla

et al. 2021

Sensors

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“…This architecture has shown to be a cornerstone together with ML algorithms and software-defined networks (SDN) for practical implementation of intelligent services within multi-tenant cellular networks. Deep learning models running on the MEC can be used to address different problems such as prediction of number of users in base stations [23], traffic prediction [26], channel state information estimation [27], fault detection for providing low latency and reliable communication [28], cache optimization for mitigating challenges in transporting the big volume of data [29,30], anomaly detection in the network traffic [31] handover prediction for avoiding errors and improving user experience [32], resource allocation [33]. Fig.…”

Section: Deep Learning Architectures For Mobile Networkmentioning

confidence: 99%

Application of deep learning algorithms and architectures in the new generation of mobile networks

Dašić

Vučetić²,

Ilić

et al. 2021

Serb J Electr Eng

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Operators of modern mobile networks are faced with significant challenges in providing the requested level of service to an ever increasing number of user entities. Advanced machine learning techniques based on deep architectures and appropriate learning methods are recognized as promising ways of tackling the said challenges in many aspects of mobile networks, such as mobile data and mobility analysis, network control, network security and signal processing. Having firstly presented the background of deep learning and related technologies, the paper goes on to present the architectures used for deployment of deep learning in mobile networks. The paper continues with an overview of applications and services related to the new generation of mobile networks that employ deep learning methods. Finally, the paper presents practical use case of modulation classification as implementation of deep learning in an application essential for modern spectrum management. We complete this work by pinpointing future directions for research.

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“…In our prior work of [24], we presented a proof-of-concept mobile game streaming evaluation over an actual testbed setup that involved a kubernetes-based Edge service and highlighted the benefits of timely synchronising Edge service containers rather than migrating or using checkpoint and restore (CRIU) 3 . Specifically, we showed a two-order magnitude reduction of service downtime induced by a HO, from 5487 ms to an incredible 25 ms.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Mobile Handover Prediction for Zero Downtime Edge Application Mobility

Uniyal

Bravalheri

Vasilakos

et al. 2021

2021 IEEE Global Communications Conference (GLOBECOM)

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Ultra-reliable low-latency communication services are intrinsically challenging to deliver, with many 5G and future services, including mobile game streaming, adding further complexity by demanding zero service downtime in high-mobility scenarios. Solving these challenges is essential and must be addressed beyond mobile gaming to realise a multitude of current and future services like Virtual Reality or holoportation in mobile scenarios. Multi-access Edge Computing brings services "closer" to user consumption with evident advantages yet at the cost of maintaining a zero downtime guarantee when user handovers (HOs) are prevalent due to the decentralisation of services towards the network edge. In this work, we design and evaluate intelligent HO prediction models between radio 5G Base Stations. The motivation for timely user HO prediction lies in being a vital presupposition for path steering and other MANO control actions in contemporary programmable 5G networks to deliver a zero downtime perception during HO events. Our meticulous simulation and actual testbed evaluation results show that effective HO prediction can be achieved using a combination of Long Short-Term Memory (LSTM) or gradient boost regression with classification models, with the latter filtering out any Reference Signal Received Power (RSRP) prediction input outliers for predicting the serving cell.

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Handover Prediction Integrated with Service Migration in 5G Systems

Cited by 13 publications

References 22 publications

A Robust UWSN Handover Prediction System Using Ensemble Learning

A Robust UWSN Handover Prediction System Using Ensemble Learning

Application of deep learning algorithms and architectures in the new generation of mobile networks

Intelligent Mobile Handover Prediction for Zero Downtime Edge Application Mobility

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