IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v13y2021i5p115-d546361.html
   My bibliography  Save this article

Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area

Author

Listed:
  • Mike Oluwatayo Ojo

    (Department of Veterinary Sciences, University of Turin, 10095 Grugliasco (TO), Italy
    Department of Information Engineering, University of Pisa, Via Caruso 16, 56122 Pisa, Italy)

  • Davide Adami

    (CNIT Research Unit, Department of Information Engineering, University of Pisa, 56100 Pisa, Italy)

  • Stefano Giordano

    (Department of Information Engineering, University of Pisa, Via Caruso 16, 56122 Pisa, Italy)

Abstract

Smart agriculture and wildlife monitoring are one of the recent trends of Internet of Things (IoT) applications, which are evolving in providing sustainable solutions from producers. This article details the design, development and assessment of a wildlife monitoring application for IoT animal repelling devices that is able to cover large areas, thanks to the low power wide area networks (LPWAN), which bridge the gap between cellular technologies and short range wireless technologies. LoRa, the global de-facto LPWAN, continues to attract attention given its open specification and ready availability of off-the-shelf hardware, with claims of several kilometers of range in harsh challenging environments. At first, this article presents a survey of the LPWAN for smart agriculture applications. We proceed to evaluate the performance of LoRa transmission technology operating in the 433 MHz and 868 MHz bands, aimed at wildlife monitoring in a forest vegetation area. To characterize the communication link, we mainly use the signal-to-noise ratio (SNR), received signal strength indicator (RSSI) and packet delivery ratio (PDR). Findings from this study show that achievable performance can greatly vary between the 433 MHz and 868 MHz bands, and prompt caution is required when taking numbers at face value, as this can have implications for IoT applications. In addition, our results show that the link reaches up to 860 m in the highly dense forest vegetation environment, while in the not so dense forest vegetation environment, it reaches up to 2050 m.

Suggested Citation

  • Mike Oluwatayo Ojo & Davide Adami & Stefano Giordano, 2021. "Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area," Future Internet, MDPI, vol. 13(5), pages 1-22, April.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:5:p:115-:d:546361
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/13/5/115/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/13/5/115/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:13:y:2021:i:5:p:115-:d:546361. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.