skip to main content
10.1145/3479240.3488514acmconferencesArticle/Chapter ViewAbstractPublication PagesmswimConference Proceedingsconference-collections
research-article

Towards a Throughput and Energy Efficient Association Strategy for Wi-Fi/LiFi Heterogeneous Networks

Published: 22 November 2021 Publication History

Abstract

In very dense or ultra-dense scenarios wherein Wi-Fi alone may not be enough to accommodate the needs of all stations, LiFi (Light Fidelity) access points can help alleviate the strain on the Wi-Fi by offloading some Wi-Fi traffic to LiFi. We study the issue of associating stations in a Wi-Fi/LiFi heterogeneous network composed of a Wi-Fi access point and multiples LiFi access points. We propose a conceptually simple and easy to implement solution to search and find an efficient mapping for the associations between stations and access points using analytical performance models for the individual throughput of each station and for the overall network energy consumption. Using two realistic deployments of heterogeneous networks for offices, we have evaluated the effectiveness of our solution at discovering better trade-offs than baseline strategies. Our numerical results show that significant gains can be obtained in terms of the throughput of the stations as well as overall energy consumption.

References

[1]
Hamada Alshaer and Harald Haas. 2020. Software-Defined Networking-Enabled Heterogeneous Wireless Networks and Applications Convergence. IEEE Access, Vol. 8 (2020), 66672--66692. https://doi.org/10.1109/ACCESS.2020.2986132
[2]
Mohammed Amer, Anthony Busson, and Isabelle Guérin Lassous. 2016. Association Optimization in Wi-Fi Networks: Use of an Access-Based Fairness. In Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (Malta, Malta) (MSWiM '16). Association for Computing Machinery, New York, NY, USA, 119--126. https://doi.org/10.1145/2988287.2989153
[3]
Muhammad Asad, Saad Qaisar, and Abdul Basit. 2020. Client-Centric Access Device Selection for Heterogeneous QoS Requirements in Beyond 5G IoT Networks. IEEE Access, Vol. 8 (2020), 219820--219836. https://doi.org/10.1109/ACCESS.2020.3042522
[4]
Xuan Li, Rong Zhang, and Lajos Hanzo. 2015. Cooperative Load Balancing in Hybrid Visible Light Communications and WiFi. IEEE Transactions on Communications, Vol. 63, 4 (2015), 1319--1329. https://doi.org/10.1109/TCOMM.2015.2409172
[5]
pureLiFi. [n.d.]. Data sheet. https://purelifi.com/wp-content/uploads/2017/12/LiFi-XC-Data-sheet-Snapshot.pdf
[6]
Yunlu Wang, Xiping Wu, and Harald Haas. 2015. Distributed load balancing for Internet of Things by using Li-Fi and RF hybrid network. In 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). 1289--1294. https://doi.org/10.1109/PIMRC.2015.7343497
[7]
Xiping Wu, Majid Safari, and Harald Haas. 2017a. Access Point Selection for Hybrid Li-Fi and Wi-Fi Networks. IEEE Transactions on Communications, Vol. 65, 12 (2017), 5375--5385. https://doi.org/10.1109/TCOMM.2017.2740211
[8]
Xiping Wu, Majid Safari, and Harald Haas. 2017b. Joint Optimisation of Load Balancing and Handover for Hybrid LiFi and WiFi Networks. In 2017 IEEE Wireless Communications and Networking Conference (WCNC) . 1--5. https://doi.org/10.1109/WCNC.2017.7925839

Cited By

View all
  • (2023)Classification of Services through Feature Selection and Machine Learning in 5G NetworksAutomatic Control and Computer Sciences10.3103/S014641162306007X57:6(589-599)Online publication date: 1-Dec-2023

Index Terms

  1. Towards a Throughput and Energy Efficient Association Strategy for Wi-Fi/LiFi Heterogeneous Networks

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    PE-WASUN '21: Proceedings of the 18th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks
    November 2021
    133 pages
    ISBN:9781450390781
    DOI:10.1145/3479240
    © 2021 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 22 November 2021

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. association
    2. energy consumption
    3. heterogeneous
    4. lifi
    5. local search
    6. score
    7. strategy
    8. wi-fi

    Qualifiers

    • Research-article

    Conference

    MSWiM '21
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 70 of 240 submissions, 29%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)20
    • Downloads (Last 6 weeks)3
    Reflects downloads up to 17 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)Classification of Services through Feature Selection and Machine Learning in 5G NetworksAutomatic Control and Computer Sciences10.3103/S014641162306007X57:6(589-599)Online publication date: 1-Dec-2023

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media