Genetic Algorithm with Heuristic Mutation for Wireless Sensor Network Optimization

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Genetic Algorithm with Heuristic Mutation for Wireless Sensor Network Optimization
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/GF22-34873K" target="_blank" >GF22-34873K: Constrained Multiobjective Optimization Based on Problem Landscape Analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Lecture Notes on Data Engineering and Communications Technologies. Volume 182
ISBN
978-3-031-40970-7
ISSN
2367-4512
e-ISSN
2367-4520
Number of pages
13
Pages from-to
177-189
Publisher name
Springer
Place of publication
Cham
Event location
Čiang Mai
Event date
Sep 6, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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