Liu, Q. and Ball, E. orcid.org/0000-0002-6283-5949 (2020) A tractable stochastic geometry model of coverage and an approach to energy efficiency estimation in LPWAN networks. International Journal of Sensor Networks, 33 (4). pp. 211-223. ISSN 1748-1279
Abstract
In this paper, we consider two types of low-power wide area network (LPWAN): LoRa wide area network (LoRaWAN) and narrow-band internet of things (NBIoT) network. We first propose a framework to calculate the average number of retransmissions in LoRaWAN networks and NBIoT networks based on stochastic geometry. Combining the average number of retransmissions, we give an approximate method to calculate both networks' energy efficiency and estimate the battery lifetime in LoRaWAN networks and NBIoT networks. The numerical results show that the battery lifetime is mainly influenced by the number of active UEs and the spreading factor in LoRaWAN networks and sleeping mode in NBIoT networks, when the data size transmitted each day is fixed. In NBIoT networks, the UEs can work for much longer with power saving mode (PSM) than with extended idle-mode discontinuous reception cycle (eDRX), even exceeding LoRaWAN networks in some cases.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Inderscience Enterprises Ltd. This is an author-produced version of a paper subsequently published in International Journal of Sensor Networks. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | LPWAN; LoRaWAN; NBIoT; Stochastic Geometry; PSM; eDRX |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Feb 2020 14:35 |
Last Modified: | 15 Aug 2021 00:38 |
Status: | Published |
Publisher: | Inderscience |
Refereed: | Yes |
Identification Number: | 10.1504/ijsnet.2020.109188 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157618 |