Genetic Algorithm with Heuristic Mutation for Wireless Sensor Network Optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254721" target="_blank" >RIV/61989100:27240/23:10254721 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-40971-4_17" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-40971-4_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-40971-4_17" target="_blank" >10.1007/978-3-031-40971-4_17</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Genetic Algorithm with Heuristic Mutation for Wireless Sensor Network Optimization

Popis výsledku v původním jazyce

Bio-inspired metaheuristics can be useful for the optimization of complex systems. Wireless sensor networks (WSNs) are massively distributed cyber-physical systems whose efficient operation requires appropriate design and control strategies. In certain contexts, like with randomly deployed WSNs, the physical network configuration can be affected only minimally, and optimal control strategies are crucial for optimizing network performance metrics like lifetime, coverage, and energy consumption. These metrics often conflict with each other, making network optimization a complex multi-objective problem. In this study, we introduce an improved version of a bi-objective genetic algorithm for the optimization of sensor network lifetime and target coverage. The new algorithm uses the generic evolutionary optimization framework together with a problem-specific heuristic mutation operator. We investigate the ability of the algorithm to find sensor schedules that extend network lifetime, and improve average target coverage while satisfying the minimum coverage requirement and show that the improved algorithm delivers better schedules than the original GA.

Název v anglickém jazyce

Genetic Algorithm with Heuristic Mutation for Wireless Sensor Network Optimization

Popis výsledku anglicky

Bio-inspired metaheuristics can be useful for the optimization of complex systems. Wireless sensor networks (WSNs) are massively distributed cyber-physical systems whose efficient operation requires appropriate design and control strategies. In certain contexts, like with randomly deployed WSNs, the physical network configuration can be affected only minimally, and optimal control strategies are crucial for optimizing network performance metrics like lifetime, coverage, and energy consumption. These metrics often conflict with each other, making network optimization a complex multi-objective problem. In this study, we introduce an improved version of a bi-objective genetic algorithm for the optimization of sensor network lifetime and target coverage. The new algorithm uses the generic evolutionary optimization framework together with a problem-specific heuristic mutation operator. We investigate the ability of the algorithm to find sensor schedules that extend network lifetime, and improve average target coverage while satisfying the minimum coverage requirement and show that the improved algorithm delivers better schedules than the original GA.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/GF22-34873K" target="_blank" >GF22-34873K: Vícekriteriální optimalizace s omezeními pomocí analýzy potenciálních ploch</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes on Data Engineering and Communications Technologies. Volume 182

ISBN

978-3-031-40970-7

ISSN

2367-4512

e-ISSN

2367-4520

Počet stran výsledku

13

Strana od-do

177-189

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Čiang Mai

Datum konání akce

6. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—