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    An Analysis of the Energy Consumption of LPWA-based IoT Devices

    Finnegan, Joseph and Brown, Stephen (2018) An Analysis of the Energy Consumption of LPWA-based IoT Devices. In: 2018 International Symposium on Networks, Computers and Communications (ISNCC). IEEE. ISBN 9781538637791

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    The unique challenges posed by the breadth of Internet of Things applications have resulted in the development of a number of different Low Power Wide Area wireless solutions. These technologies enable scalable long range networks on cheap low power devices, facilitating the development of a ubiquitous Internet of Things. The energy efficiency of these wireless technologies have a significant impact on battery lifetime. In this paper we propose an approach to energy efficiency calculations suited to this new paradigm by focusing on daily throughput. We present a set of deployment cases, develop energy models to represent each of the technologies studied, and use these models to provide a thorough comparison in terms of predicted device lifetime for a range of daily throughputs. This quantitative analysis of network device efficiency vs. daily throughput enables identification of the changeover point between optimal solutions. Our contributions are the integration of different energy models that have not been previously compared into a common framework, and the identification of the energy-efficiency crossover points between these models. This enables the selection of the most efficient wireless solution for specific Internet of Things applications, which is a key factor in optimising device lifetime.

    Item Type: Book Section
    Additional Information: Cite as: J. Finnegan and S. Brown, "An Analysis of the Energy Consumption of LPWA-based IoT Devices," 2018 International Symposium on Networks, Computers and Communications (ISNCC), Rome, 2018, pp. 1-6, doi: 10.1109/ISNCC.2018.8531068.
    Keywords: Energy Consumption; Low Power Wide Area wireless; IoT Devices; Throughput; Downlink; Wireless communication; Uplink; Internet of Things; Wireless sensor networks; Batteries;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 13342
    Identification Number:
    Depositing User: Stephen Brown
    Date Deposited: 01 Oct 2020 15:53
    Publisher: IEEE
    Refereed: Yes
    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

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