Integrating CLDs and machine learning through hybridization for human-centric wireless networks

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dc.authoridSalah, Bashir/0000-0003-2709-760X
dc.authoridYadav, Ajay/0000-0002-3032-3105
dc.authorwosidSalah, Bashir/ABC-5845-2020
dc.authorwosidYadav, Ajay/HDL-7860-2022
dc.contributor.authorKumari, Binita
dc.contributor.authorYadav, Ajay Kumar
dc.contributor.authorCengiz, Korhan
dc.contributor.authorSalah, Bashir
dc.date.accessioned2024-05-19T14:50:25Z
dc.date.available2024-05-19T14:50:25Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractWireless sensor networks, more commonly abbreviated as WSNs, have been regarded as helpful tool for managing human-centric applications. Nevertheless, the design of wireless systems that are accurate, efficient, and robust remains difficult due to the variables and dynamics of the wireless environment as well as the requirements of the users. Cross-layer designs along with the machine-learning techniques need to be integrated into a novel hybridization framework for human-centric wireless networks in order to simplify the process and make it more manageable. The purpose of the proposed framework is to enhance wireless sensor networks (WSNs) in terms of their energy efficiency, robustness, real-time performance, and scalability. In particular, machine learning are employed for the purpose of extracting features from sensor data, and the framework combines cross-layer optimization and RL in order to facilitate effective and adaptable communication and networking. In comparison to previous work in this field, the accuracy, energy consumption, robustness, real-time performance, and scalability of the proposed framework are all significantly improved. The hybridization framework that has been proposed provides a promising approach to addressing the challenges, and it can be of use to a variety of applications. A diagram of the proposed hybridization framework. The wireless sensor network (WSNs) collects sensor data and uses a convolutional neural network to extract features. The features are then used by a machine learning algorithm to generate control signals for the system. The control signals are then used to adjust the behavior of the wireless network, which in turn sends configuration commands back to the sensor network.imageen_US
dc.identifier.doi10.1002/ett.4864
dc.identifier.issn2161-3915
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85173441876en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.1002/ett.4864
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5701
dc.identifier.volume35en_US
dc.identifier.wosWOS:001075573900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofTransactions on Emerging Telecommunications Technologiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSensor Networksen_US
dc.subjectCommunicationen_US
dc.subjectSupporten_US
dc.titleIntegrating CLDs and machine learning through hybridization for human-centric wireless networksen_US
dc.typeArticleen_US

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