A Bi-objective Genetic Algorithm for Wireless Sensor Network Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10252184" target="_blank" >RIV/61989100:27240/22:10252184 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-08812-4_15" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-08812-4_15</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-08812-4_15" target="_blank" >10.1007/978-3-031-08812-4_15</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Bi-objective Genetic Algorithm for Wireless Sensor Network Optimization

Popis výsledku v původním jazyce

When designing a wireless sensor network several performance metrics should be considered, e.g., network lifetime, target coverage, sensor energy consumption. As a rule, these metrics are in conflict with each other, which means that by optimizing some of them we worsen the others. Designing the network is therefore a problem of multi-objective optimization. In this work, we propose a bi-objective genetic algorithm that optimizes network lifetime and target coverage. We consider two variants of the algorithm, in which the fitness function comprises only the network lifetime, or where it includes both, the network lifetime and target coverage. This makes it possible to find a trade-off between these two objectives. In-depth experimental studies are carried out for both variants of the algorithm. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Název v anglickém jazyce

A Bi-objective Genetic Algorithm for Wireless Sensor Network Optimization

Popis výsledku anglicky

When designing a wireless sensor network several performance metrics should be considered, e.g., network lifetime, target coverage, sensor energy consumption. As a rule, these metrics are in conflict with each other, which means that by optimizing some of them we worsen the others. Designing the network is therefore a problem of multi-objective optimization. In this work, we propose a bi-objective genetic algorithm that optimizes network lifetime and target coverage. We consider two variants of the algorithm, in which the fitness function comprises only the network lifetime, or where it includes both, the network lifetime and target coverage. This makes it possible to find a trade-off between these two objectives. In-depth experimental studies are carried out for both variants of the algorithm. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/LTAIN19176" target="_blank" >LTAIN19176: Metaheuristický rámec pro vícecílové kombinatorické optimalizační problémy (META MO-COP)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Networks and Systems. Volume 497

ISBN

978-3-031-08811-7

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

13

Strana od-do

147-159

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Kitakjúšú

Datum konání akce

29. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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