Meet User's Service Requirements in Smart Cities Using Recurrent Neural Networks and Optimization Algorithm

Basit Öğe Kaydı
dc.authoridSefati, Seyed Salar/0000-0002-7208-3576
dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authoridBouyer, Asgarali/0000-0002-4808-2856
dc.authorwosidSefati, Seyed Salar/AAU-2556-2021
dc.authorwosidBouyer, Asgarali/JOZ-6483-2023
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.contributor.authorSefati, Seyed Salar
dc.contributor.authorArasteh, Bahman
dc.contributor.authorHalunga, Simona
dc.contributor.authorFratu, Octavian
dc.contributor.authorBouyer, Asgarali
dc.date.accessioned2024-05-19T14:39:29Z
dc.date.available2024-05-19T14:39:29Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractDespite significant advancements in Internet of Things (IoT)-based smart cities, service discovery, and composition continue to pose challenges. Current methodologies face limitations in optimizing Quality of Service (QoS) in diverse network conditions, thus creating a critical research gap. This study presents an original and innovative solution to this issue by introducing a novel three-layered recurrent neural network (RNN) algorithm. Aimed at optimizing QoS in the context of IoT service discovery, our method incorporates user requirements into its evaluation matrix. It also integrates long short-term memory (LSTM) networks and a unique black widow optimization (BWO) algorithm, collectively facilitating the selection and composition of optimal services for specific tasks. This approach allows the RNN algorithm to identify the top-K services based on QoS under varying network conditions. Our methodology's novelty lies in implementing LSTM in the hidden layer and employing backpropagation through time (BPTT) for parameter updates, which enables the RNN to capture temporal patterns and intricate relationships between devices and services. Further, we use the BWO algorithm, which simulates the behavior of black widow spiders, to find the optimal combination of services to meet system requirements. This algorithm factors in both the attractive and repulsive forces between services to isolate the best candidate solutions. In comparison with existing methods, our approach shows superior performance in terms of latency, availability, and reliability. Thus, it provides an efficient and effective solution for service discovery and composition in IoT-based smart cities, bridging a significant gap in current research.en_US
dc.description.sponsorshipEuropean Union [861219]en_US
dc.description.sponsorshipThis work was supported by the Project Mobility and Training foR Beyond 5G Ecosystems (MOTOR5G) which received funding from the European Union's Horizon 2020 Programme underthe Marie Sklodowska Curie Actions (MSCA) Innovative Training Network(ITN) under Grant 861219.en_US
dc.identifier.doi10.1109/JIOT.2023.3303188
dc.identifier.endpage22269en_US
dc.identifier.issn2327-4662
dc.identifier.issue24en_US
dc.identifier.scopus2-s2.0-85167816865en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage22256en_US
dc.identifier.urihttps://doi.org10.1109/JIOT.2023.3303188
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4788
dc.identifier.volume10en_US
dc.identifier.wosWOS:001163472700080en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Internet of Things Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectBlack Widow Optimization (Bwo) Algorithm (Bwo)en_US
dc.subjectInternet Of Things (Iot)en_US
dc.subjectRecurrent Neural Network (Rnn)en_US
dc.subjectService Discoveryen_US
dc.subjectSmart Citiesen_US
dc.titleMeet User's Service Requirements in Smart Cities Using Recurrent Neural Networks and Optimization Algorithmen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu