Optimization of mobility sampling in dynamic networks using predictive wavelet analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255011" target="_blank" >RIV/61989100:27240/24:10255011 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/24:10255011

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1574119224000130?dgcid=rss_sd_all" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1574119224000130?dgcid=rss_sd_all</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.pmcj.2024.101887" target="_blank" >10.1016/j.pmcj.2024.101887</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of mobility sampling in dynamic networks using predictive wavelet analysis

Popis výsledku v původním jazyce

In the last decade, the investigation of mobility features has gained enormous significance in many scenarios as a result of the significant diffusion and deployment of mobile devices covered by high-speed technologies (e.g., 5G). Many contributions in the literature have attempted to discover mobility properties, but most studies are based on the time features of the mobility process. No study has yet considered the effects of setting a proper sampling frequency (generally set to 1 s), in order to avoid information loss. Following our previous works, we propose a novel predictive spectral approach for mobility sampling based on the concept of a predictive wavelet. With this method, the choice of sampling frequency is governed by the current spectral components of the mobility process and derived from an analysis of future, predicted components. To assess whether our proposal may yield a helpful method, we conducted several simulation campaigns to test sampling accuracy and obtained results that confirmed our expectations. (C) 2024 The Author(s)

Název v anglickém jazyce

Optimization of mobility sampling in dynamic networks using predictive wavelet analysis

Popis výsledku anglicky

In the last decade, the investigation of mobility features has gained enormous significance in many scenarios as a result of the significant diffusion and deployment of mobile devices covered by high-speed technologies (e.g., 5G). Many contributions in the literature have attempted to discover mobility properties, but most studies are based on the time features of the mobility process. No study has yet considered the effects of setting a proper sampling frequency (generally set to 1 s), in order to avoid information loss. Following our previous works, we propose a novel predictive spectral approach for mobility sampling based on the concept of a predictive wavelet. With this method, the choice of sampling frequency is governed by the current spectral components of the mobility process and derived from an analysis of future, predicted components. To assess whether our proposal may yield a helpful method, we conducted several simulation campaigns to test sampling accuracy and obtained results that confirmed our expectations. (C) 2024 The Author(s)

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Pervasive and Mobile Computing

ISSN

1574-1192

e-ISSN

—

Svazek periodika

98

Číslo periodika v rámci svazku

FEB 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

001182614400001

EID výsledku v databázi Scopus

2-s2.0-85184010298