MURAL - Maynooth University Research Archive Library



    Modeling the Energy Consumption of LoRaWAN in ns-3 Based on Real World Measurements


    Finnegan, Joseph and Brown, Stephen and Farrell, Ronan (2018) Modeling the Energy Consumption of LoRaWAN in ns-3 Based on Real World Measurements. In: 2018 Global Information Infrastructure and Networking Symposium (GIIS). IEEE, pp. 174-4. ISBN 9781538672723

    [img]
    Preview
    Download (268kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    LPWAN technologies are defined by their focus on extended coverage while maintaining energy efficiency, at the expense of data throughput. In this research we enable the analysis of LoRa, a key LPWAN technology, in terms of energy efficiency. We perform real-world measurements of a standard LoRa chip and use the results to develop an energy consumption module in ns-3. Our contributions are an analysis of the energy consumption of different states in a LoRa transmission by the SX1272, the LoRa transceiver that is used in most common LoRaWAN devices, beyond what is provided in the datasheet, and an energy consumption module for use in three of the LoRaWAN ns-3 modules described in research, enabling more accurate energy consumption analysis of LoRa-based systems.

    Item Type: Book Section
    Additional Information: Cite as: J. Finnegan, S. Brown and R. Farrell, "Modeling the Energy Consumption of LoRaWAN in ns-3 Based on Real World Measurements," 2018 Global Information Infrastructure and Networking Symposium (GIIS), Thessaloniki, Greece, 2018, pp. 1-4, doi: 10.1109/GIIS.2018.8635786.
    Keywords: Analytical models; Energy consumption; Adaptation models; Pins; Data models; Current measurement; Energy measurement;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 13333
    Identification Number: https://doi.org/10.1109/GIIS.2018.8635786
    Depositing User: Ronan Farrell
    Date Deposited: 30 Sep 2020 15:02
    Publisher: IEEE
    Refereed: Yes
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year