Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks

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aut.relation.issue7
aut.relation.journalSensors (Basel)
aut.relation.startpage2021
aut.relation.volume24
dc.contributor.authorKhan, Mohammad Usman Ali
dc.contributor.authorBabar, Mohammad Inayatullah
dc.contributor.authorRehman, Saeed Ur
dc.contributor.authorKomosny, Dan
dc.contributor.authorChong, Peter Han Joo
dc.date.accessioned2024-04-23T03:31:09Z
dc.date.available2024-04-23T03:31:09Z
dc.date.issued2024-03-22
dc.description.abstractA Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.
dc.identifier.citationSensors (Basel), ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 24(7), 2021-. doi: 10.3390/s24072021
dc.identifier.doi10.3390/s24072021
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10292/17453
dc.languageeng
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/1424-8220/24/7/2021
dc.rights© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDNN
dc.subjecthandover
dc.subjectHLWNet
dc.subjectlight fidelity
dc.subjectWiFi
dc.subjectDNN
dc.subjectHLWNet
dc.subjectWiFi
dc.subjecthandover
dc.subjectlight fidelity
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subject0301 Analytical Chemistry
dc.subject0502 Environmental Science and Management
dc.subject0602 Ecology
dc.subject0805 Distributed Computing
dc.subject0906 Electrical and Electronic Engineering
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.titleOptimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks
dc.typeJournal Article
pubs.elements-id543522
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