Energy efficient offloading mechanism using particle swarm optimization in 5G enabled edge nodes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50019369" target="_blank" >RIV/62690094:18470/23:50019369 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10586-022-03609-z" target="_blank" >https://link.springer.com/article/10.1007/s10586-022-03609-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10586-022-03609-z" target="_blank" >10.1007/s10586-022-03609-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Energy efficient offloading mechanism using particle swarm optimization in 5G enabled edge nodes

Popis výsledku v původním jazyce

Today&apos;s world naturally depends on wireless devices for the daily necessities like communication, smart car driving, smart medical check up, smart housing security, etc. These applications create huge amount of data to be processed across the edge and cloud devices. Mobile or wireless devices can efficiently handle the input data with practical limitations on computing capacity. These limitations are otherwise difficult to handle and could be overcame by using mobile edge computing technology. When computing tasks depend upon edge devices to store and process data, it tends to offload in available edge nodes. Advanced smart applications use 5G networks to process the data in edge nodes with central units or distributed cloud units. Our research problem is focused on 5G data offloading by saving the energy over time. It mainly works on selecting appropriate edge nodes with minimum cost and energy for 5G data offloading process. Balancing the load at every edge node became a crucial task in advanced 5G networks. High-class networks have more density which tends to increase the energy consumption appropriately. In our proposed work, energy efficient offloading is done with mobile edge computing (MEC), macro base stations, small base stations to compute the data with less energy. The process of selecting minimum energy devices in edge network is done using particle swarm optimization (PSO) algorithm. This proposed offloading scheme helps to process data in 5G networks very effectively. The workload energy of the 5G network at IoT and MEC is preserved by using the multi-level offloading mechanism. Further complexity of the system is optimized with energy optimization algorithm called PSO to reduce the execution time and energy. Results have shown that for the set of 500 tasks, mobile edge server consumes 11 J, while the core cloud consumes 15 J of energy per task execution. Mobile edge computing consumes less energy than cloud and mobile devices.

Název v anglickém jazyce

Energy efficient offloading mechanism using particle swarm optimization in 5G enabled edge nodes

Popis výsledku anglicky

Today&apos;s world naturally depends on wireless devices for the daily necessities like communication, smart car driving, smart medical check up, smart housing security, etc. These applications create huge amount of data to be processed across the edge and cloud devices. Mobile or wireless devices can efficiently handle the input data with practical limitations on computing capacity. These limitations are otherwise difficult to handle and could be overcame by using mobile edge computing technology. When computing tasks depend upon edge devices to store and process data, it tends to offload in available edge nodes. Advanced smart applications use 5G networks to process the data in edge nodes with central units or distributed cloud units. Our research problem is focused on 5G data offloading by saving the energy over time. It mainly works on selecting appropriate edge nodes with minimum cost and energy for 5G data offloading process. Balancing the load at every edge node became a crucial task in advanced 5G networks. High-class networks have more density which tends to increase the energy consumption appropriately. In our proposed work, energy efficient offloading is done with mobile edge computing (MEC), macro base stations, small base stations to compute the data with less energy. The process of selecting minimum energy devices in edge network is done using particle swarm optimization (PSO) algorithm. This proposed offloading scheme helps to process data in 5G networks very effectively. The workload energy of the 5G network at IoT and MEC is preserved by using the multi-level offloading mechanism. Further complexity of the system is optimized with energy optimization algorithm called PSO to reduce the execution time and energy. Results have shown that for the set of 500 tasks, mobile edge server consumes 11 J, while the core cloud consumes 15 J of energy per task execution. Mobile edge computing consumes less energy than cloud and mobile devices.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS

ISSN

1386-7857

e-ISSN

1573-7543

Svazek periodika

26

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

587-598

Kód UT WoS článku

000800435300001

EID výsledku v databázi Scopus

2-s2.0-85130705841