MURAL - Maynooth University Research Archive Library



    Identification and insight into a long transitory phase in random-access protocols


    Cano, Christina and Malone, David (2017) Identification and insight into a long transitory phase in random-access protocols. In: 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE. ISBN 978-3-9018-8290-6

    [img]
    Preview
    Download (588kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    In this work we show that random-access protocols, which are used in a range of networks (e.g. WiFi, power line communications and Internet of Things), may experience a high-throughput, extremely long (of the order of hours) transitory phase. This behaviour is not highlighted by common analysis techniques and experimental evaluations, which can lead to incorrect prediction of network performance. We identify factors that led to this transitory behaviour being overlooked in previous work. Via numerical analysis and experimental evaluation, we establish under which conditions this transitory phase occurs. Additionally, we give insight into the duration of this transitory period and its statistical properties.

    Item Type: Book Section
    Additional Information: This paper was presented at 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt); 15-19 May 2017 in Paris, France.
    Keywords: Throughput; Analytical models; Protocols; Mathematical model; Optimization; Mobile computing; Mobile communication; access protocols; computer network performance evaluation; numerical analysis; statistical analysis; wireless LAN; long transitory phase; random access protocol; high-throughput; network performance prediction; numerical analysis; experimental evaluation; statistical properties; Wi-Fi network;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 12005
    Identification Number: https://doi.org/10.23919/WIOPT.2017.7959898
    Depositing User: Dr. David Malone
    Date Deposited: 06 Dec 2019 12:04
    Publisher: IEEE
    Refereed: Yes
    URI:
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads