Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks

Wireless Sensor Networks (WSNs) used in modern applications like environmental monitoring, smart cities, and healthcare systems depend on accurate sensor node localization. However, attaining accurate localization is challenging due to dynamic environmental conditions. Varying network densities and...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 27015 - 19
Hauptverfasser:	Prabha, K. Lakshmi, Mengash, Hanan Abdullah, Alqahtani, Hamed, Allafi, Randa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 24.07.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 639/166/987 Accuracy Adaptability Algorithms Bayesian analysis Decision making Environmental conditions Environmental monitoring Golden jack optimization Heuristic Humanities and Social Sciences Localization Localization error Machine learning Marl Mathematical models Multi agent reinforcement learning algorithm multidisciplinary Neural networks Optimization techniques Performance evaluation Prediction. error minimization Problem solving Regression analysis Reinforcement Science Science (multidisciplinary) Sensors Support vector machines Wireless sensor network Wireless sensor network Multi agent reinforcement learning algorithm Localization error Prediction. error minimization Golden jack optimization
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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