A novel urban mobility classification approach based on convolutional neural networks and mobility-to-image encoding

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252791" target="_blank" >RIV/61989100:27240/23:10252791 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/23:10252791

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1319157823001076" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1319157823001076</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jksuci.2023.101561" target="_blank" >10.1016/j.jksuci.2023.101561</a>

Result language

angličtina

Original language name

A novel urban mobility classification approach based on convolutional neural networks and mobility-to-image encoding

Original language description

Over the last few decades, the classification and prediction of mobility trajectories in dynamic networks have become major research topics. Switching of mobility areas (hand-over) in modern cellular networks is frequent due to restricted coverage area and node speeds (urban, highway, etc.). Accurate management of hand-over events is highly desirable to improve the system&apos;s quality of service. We have exploited the high accuracy of machine learning to classify user mobility from mobility traces which we encoded into images. The method delivers high performance in mobility classification/prediction (exceeding 95%) and avoids the need to study and implement a dedicated neural network structure. The technique requires the conversion of mobility traces into image structures and the subsequent application of a convolutional neural network. We propose a novel approach to classifying mobility that involves data-to-image encoding and machine learning for image classification. Numerous simulations were performed to demonstrate the benefits of the proposed technique and to illustrate the variance in the accuracy of the functions of many encoding/classification parameters. The work represents a first preliminary step towards a new mobility prediction approach. We demonstrate that it is possible to achieve a very high level of prediction accuracy with low computational complexity, exploiting the strength of neural networks in image recognition. (C) 2023 The Author(s)

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20203 - Telecommunications

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of King Saud University - Computer and Information Sciences

ISSN

1319-1578

e-ISSN

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Volume of the periodical

35

Issue of the periodical within the volume

6

Country of publishing house

SA - THE KINGDOM OF SAUDI ARABIA

Number of pages

13

Pages from-to

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UT code for WoS article

001042923200001

EID of the result in the Scopus database

2-s2.0-85158045553