Pattern Prediction and Passive Bandwidth Management for Hand-over Optimization in QoS Cellular Networks with Vehicular Mobility
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86097957" target="_blank" >RIV/61989100:27240/16:86097957 - isvavai.cz</a>
Výsledek na webu
<a href="http://ieeexplore.ieee.org/document/7378986/" target="_blank" >http://ieeexplore.ieee.org/document/7378986/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TMC.2016.2516996" target="_blank" >10.1109/TMC.2016.2516996</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Pattern Prediction and Passive Bandwidth Management for Hand-over Optimization in QoS Cellular Networks with Vehicular Mobility
Popis výsledku v původním jazyce
In wireless networking, the main desire of end-users is to take advantage of satisfactory services, in terms of QoS, especially when they pay for a required need. Many efforts have been made to investigate how the continuity of services can be guaranteed in QoS networks, where users can move from one cell to another one. The introduction of a prediction scheme with passive reservations is the only way to face this issue; however, the deployment of in-advance bandwidth leads the system to waste resources. This work consists of two main integrated contributions: a new pattern prediction scheme based on a distributed set of Markov chains, in order to handle passive reservations, and a statistical bandwidth management algorithm for the reduction of bandwidth wastage. The result of the integration is the Distributed Prediction with Bandwidth Management Algorithm (DPBMA) that is independent from the considered technology and the vehicular environment. Several simulation campaigns were conducted in order to evaluate the effectiveness of the proposed idea. It was also compared with other prediction schemes, in terms of system utilization, accuracy, call dropping, and call blocking probabilities.
Název v anglickém jazyce
Pattern Prediction and Passive Bandwidth Management for Hand-over Optimization in QoS Cellular Networks with Vehicular Mobility
Popis výsledku anglicky
In wireless networking, the main desire of end-users is to take advantage of satisfactory services, in terms of QoS, especially when they pay for a required need. Many efforts have been made to investigate how the continuity of services can be guaranteed in QoS networks, where users can move from one cell to another one. The introduction of a prediction scheme with passive reservations is the only way to face this issue; however, the deployment of in-advance bandwidth leads the system to waste resources. This work consists of two main integrated contributions: a new pattern prediction scheme based on a distributed set of Markov chains, in order to handle passive reservations, and a statistical bandwidth management algorithm for the reduction of bandwidth wastage. The result of the integration is the Distributed Prediction with Bandwidth Management Algorithm (DPBMA) that is independent from the considered technology and the vehicular environment. Several simulation campaigns were conducted in order to evaluate the effectiveness of the proposed idea. It was also compared with other prediction schemes, in terms of system utilization, accuracy, call dropping, and call blocking probabilities.
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
IN - Informatika
OECD FORD obor
—
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2016
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
IEEE transactions on mobile computing
ISSN
1536-1233
e-ISSN
—
Svazek periodika
15
Číslo periodika v rámci svazku
11
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
26
Strana od-do
2809-2024
Kód UT WoS článku
000385717200012
EID výsledku v databázi Scopus
2-s2.0-84992153950