A hybrid Harrison Hawk optimization based on differential evolution for the node localization problem in IoT networks

Basit Öğe Kaydı
dc.authoridBiroğul, Serdar/0000-0003-4966-5970
dc.authoridmihoubi, miloud/0000-0001-7892-0382
dc.authoridBAIDAR, Lotfi/0000-0002-2385-0433
dc.authoridLorenz, Pascal/0000-0003-3346-7216
dc.authorwosidBiroğul, Serdar/HJH-8009-2023
dc.authorwosidmihoubi, miloud/O-8484-2018
dc.contributor.authorBaidar, Lotfi
dc.contributor.authorRahmoun, Abdellatif
dc.contributor.authorMihoubi, Miloud
dc.contributor.authorLorenz, Pascal
dc.contributor.authorBiroğul, Serdar
dc.date.accessioned2023-07-26T11:53:48Z
dc.date.available2023-07-26T11:53:48Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractDespite the close of a tumultuous 2020 and the start of 2021, connected devices will continue to shape the future of numerous industries, and businesses are confident that the Internet of Things (IoT) will play a key role in the future success of their trade. The growing Internet of Things (IoT) is connecting devices to a variety of sensors, applications, and other IoT elements to automate business processes and support human efficiencies in business and the home. WSN along with node localization algorithms can play a critical role in IoT applications. Nevertheless, in IoT applications, the context of real-time location-based services is gaining an overwhelming interest. To do this, several approaches are proposed in the recent literature based mainly on computational intelligence algorithms. This paper proposes a node localization algorithm based on swarm intelligence algorithms, that is, a hybrid Harris Hawks optimization based on differential evolution (HHODE).HHODE algorithm relies on Euclidian Distance as objective function to evaluate best-fit coordinates of sensor nodes in a wireless sensor network. Moreover, several experimentations are performed depending on the network size, communication range of sensors, geographical distribution, and the beacon nodes' density to demonstrate the efficiency of the HHODE algorithm. Compared to Standard DE, HOO, PSO, and Bat Algorithm, HHODE shows higher performance with regard to node localization.en_US
dc.identifier.doi10.1002/dac.5129
dc.identifier.issn1074-5351
dc.identifier.issn1099-1131
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85125202119en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1002/dac.5129
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12610
dc.identifier.volume35en_US
dc.identifier.wosWOS:000760705500001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBiroğul, Serdar
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofInternational Journal of Communication Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectDifferential Evolution; Harris Hawk Optimization Algorithm; Localization Error; Localization Time; Node Localization; Optimization; Wireless Sensor Networken_US
dc.subjectWireless; Algorithmen_US
dc.titleA hybrid Harrison Hawk optimization based on differential evolution for the node localization problem in IoT networksen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
12610.pdf
Boyut:
2.45 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu