Development of Handover Decision Algorithms in Hybrid Li-Fi and Wi-Fi Networks

Sanusi, Jaafaru; Idris, Sadiq; Adeshina, Steve A.; Aibinu, Abiodun Musa; Ibrahim, Umar Adam

doi:10.1109/iciss49785.2020.9316116

Cited by 9 publications

(8 citation statements)

References 37 publications

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“…The problems such as load balancing, mobility management, light path blockage, and interference management in HLWNets are mainly connected with the problem of handover management. Some recent studies have proposed the handover mechanisms considering either of the problems in HLWNets in [31][32][33][34][35][36][37][38][39]. In [31], the authors have presented a method for addressing mobility and LoS challenges faced by users in HLWNets.…”

Section: A State-of-art Methodsmentioning

confidence: 99%

“…Furthermore, the suggested approach provides adjustable WiFi coverage based on the user's mobility. The Fuzzy Logic (FL) and ANN (artificial neural network) handover decision algorithms had proposed in [35] for HLWNets. The FL-based handover decision-making algorithm was designed that takes inputs SINR, average SINR, mobility speed, and received signal strength (RSS) parameters.…”

Section: A State-of-art Methodsmentioning

confidence: 99%

“…-The handover management methods in [31][32][33][34][35][36][37][38][39] focused on handover decisionmaking by considering either parameter like UE mobility, shadowing effects, and load management only. Such systems have failed to detect the sort of handover required during decision-making, resulting in increased computing complexity and frequent handovers.…”

Section: B Motivation and Contributionsmentioning

confidence: 99%

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MOHAN: Multi-Objective Handover for hybrid (LiFi-WiFi) Application-based Networks

Satarkar

Chowdhary

2022

Preprint

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Recently the hybrid networks using the Light Fidelity (LiFi) and Wireless Fidelity (WiFi) have received significant attention due to their advantages of higher data rate. Such networks form the Hybrid LiFi-WiFi Networks (HLWNets). As the design and implementation of HLWNets are still in their early stages, it faces several problems. The vertical/horizontal handovers process becomes a challenging task in HLWNets due to the frequent mobility of the users, short coverage range of Access Point (AP), overlapping coverage regions of different networks (WiFi and LiWi). Designing efficient handover policies for HLWNets needs to address the multiple challenges like reducing handover frequency, mitigating the shadow/light path blockage, reducing communication delay/jitter, interference management, and data loss reduction. According to current research, there is a lack of a multi-objective handover mechanism for HLWNets. To end this, we propose a novel Multi-Objective Handover for hybrid Application-based Networks, i.e., HLWNets (MOHAN). The MOHAN protocol mainly addresses the two challenges shadow effect and interference management while performing the handovers. The MOHAN protocol performs handover initialization, target AP selection, and handover execution. During the periodic handover initialization phase, the proposed technique conquers both horizontal and vertical handovers, resulting in effective interference control in the network. The handover initialization phase decides the type of handover using the user mobility and received signal power to mitigate the shadow effects and interferences. In the target AP selection phase of vertical/horizontal, QoS-aware algorithms are designed to reduce the data loss, handover frequency, and interferences. The experimental results show the efficiency of the proposed MOHAN protocol using different network conditions compared to existing methods.

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Section: A State-of-art Methodsmentioning

confidence: 99%

Section: A State-of-art Methodsmentioning

confidence: 99%

Section: B Motivation and Contributionsmentioning

confidence: 99%

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MOHAN: Multi-Objective Handover for hybrid (LiFi-WiFi) Application-based Networks

Satarkar

Chowdhary

2022

Preprint

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“…The handover decision is made based on channel quality, resource availability, and user mobility, e.g., Wi-Fi-only APs are preferred for mobile users while Wi-Fi-Li-Fi APs are selected for static users based on received signal strength and user satisfaction levels. Finally, Sanusi et al [264], combine fuzzy logic with NN to support Wi-Fi-Li-Fi handovers.…”

Section: Cooperative Coexistencementioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Szott

Kosek-Szott

Gawłowicz

et al. 2022

IEEE Commun. Surv. Tutorials

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Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.

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“…The handover decision is made based on channel quality, resource availability and user mobility, e.g., WiFi-only APs are preferred for mobile users while WiFi-LiFi APs are selected for static users based on received signal strength and user satisfaction levels. Finally, in [224], fuzzy logic is combined with NN to support WiFi-LiFi handovers.…”

Section: Cooperative Coexistencementioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

View full text Add to dashboard Cite

Wireless local area networks (WLANs) empowered by IEEE 802.11 (WiFi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as affordable and highly interoperable devices. The WiFi community is currently deploying WiFi 6 and developing WiFi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classic optimization approaches fail in such conditions, machine learning (ML) is well known for being able to handle complexity. Much research has been published on using ML to improve WiFi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describing the various areas where WiFi can be enhanced using ML. To this end, we analyze over 200 papers in the field providing readers with an overview of the main trends. Based on this review, we identify both open challenges in each WiFi performance area as well as general future research directions.

show abstract

Development of Handover Decision Algorithms in Hybrid Li-Fi and Wi-Fi Networks

Cited by 9 publications

References 37 publications

MOHAN: Multi-Objective Handover for hybrid (LiFi-WiFi) Application-based Networks

MOHAN: Multi-Objective Handover for hybrid (LiFi-WiFi) Application-based Networks

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

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