Efficient WSN Node Placement by Coupling KNN Machine Learning for Signal Estimations and I-HBIA Metaheuristic Algorithm for Node Position Optimization - Sciences pour l'Environnement Accéder directement au contenu
Article Dans Une Revue Sensors Année : 2022

Efficient WSN Node Placement by Coupling KNN Machine Learning for Signal Estimations and I-HBIA Metaheuristic Algorithm for Node Position Optimization

Bastien Poggi
Chabi Babatounde
Evelyne Vittori

Résumé

Wireless sensor network (WSN) deployment is an intensive field of research. In this paper, we propose a novel approach based on machine learning (ML) and metaheuristics (MH) for supporting decision-makers during the deployment process. We suggest optimizing node positions by introducing a new hybridized version of the “Hitchcock bird-inspired algorithm” (HBIA) metaheuristic algorithm that we named “Intensified-Hitchcock bird-inspired algorithm” (I-HBIA). During the optimization process, our fitness function focuses on received signal maximization between nodes and antennas. Signal estimations are provided by the machine learning “K Nearest Neighbors” (KNN) algorithm working with real measured data. To highlight our contribution, we compare the performances of the canonical HBIA algorithm and our I-HBIA algorithm on classical optimization benchmarks. We then evaluate the accuracy of signal predictions by the KNN algorithm on different maps. Finally, we couple KNN and I-HBIA to provide efficient deployment propositions according to actual measured signal on areas of interest.
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hal-04163441 , version 1 (15-07-2024)

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Bastien Poggi, Chabi Babatounde, Evelyne Vittori, Thierry Antoine-Santoni. Efficient WSN Node Placement by Coupling KNN Machine Learning for Signal Estimations and I-HBIA Metaheuristic Algorithm for Node Position Optimization. Sensors, 2022, Wireless Sensor Networks, 22 (24), pp.9927. ⟨10.3390/s22249927⟩. ⟨hal-04163441⟩
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