Energy-efficient topology optimization of wireless sensor networks using a modified genetic algorithm
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Pyrih Yaroslav, Przystupa Krzysztof, Pyrih Yuliia, Sikora Jarosław, Beshley Mykola |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 10 |
| Wolumen/Tom: | 15 |
| Numer artykułu: | 2016 |
| Strony: | 1 - 30 |
| Impact Factor: | 2,6 |
| Web of Science® Times Cited: | 0 |
| Bazy: | Web of Science |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 9 maja 2026 |
| Abstrakty: | angielski |
| This paper addresses the challenge of WSN topology optimisation through the develop- ment and implementation of a modified genetic algorithm (MGA). Unlike classical ap- proaches, the proposed method is based on the assessment of sensor node distribution density, employing an adaptive penalty system and considering the minimum inter-node distance to determine optimal configurations during the evolutionary selection process. A software module has been developed in Python (version 3.12.1) for the simulation of WSN functionality, accounting for dynamic topology changes and limited network re- sources. A comparative analysis of the proposed approach’s effectiveness was conducted against greedy, random, and uniform algorithms, varying sensor ranges (20, 30 and 40 m) and minimum inter-node distance constraints. Simulation results for scenarios involving 25 and 100 sensor nodes demonstrate that the proposed MGA consistently outperforms traditional approaches, including uniform (mesh), greedy, and random search algorithms. Unlike these methods, which either result in significant overlap (up to 13.23%) or fail to deploy all nodes, the MGA achieves 100% node placement with near-zero overlap. Fur- thermore, the proposed method exhibits stable convergence and high reliability, main- taining consistent performance across multiple runs with diverse initial conditions. The proposed Integrated Energy Efficiency Metric (IEEM) establishes a relationship between the spatial distribution of sensor nodes and the overall energy consumption of a WSN. By linking topology formation with energy costs, this metric enables a comprehensive assess- ment of deployment efficiency. Simulation results across various deployment scenarios demonstrate that the proposed MGA consistently achieves the lowest IEEM values com- pared to Mesh, Greedy, and Random placement strategies. The observed improvements range from 4.76% to 31.38%, confirming a substantial reduction in total energy losses. The proposed approach is particularly well-suited for dense deployments and resource-con- strained environments, where effective coverage and minimal energy consumption are critical. |
