Toward LoRa-Based LEO Satellite IoT: A Stochastic Geometry Perspective

Recently, Long-Range (LoRa) based low Earth orbit (LEO) satellite Internet of Things (IoT) has garnered growing interest from both academia and industry, since it can guarantee pervasive connectivity in an energy-efficient and cost-effective manner. In this paper, we provide a novel spherical stochastic geometry (SG) based analytical framework for characterizing the uplink access probability of LoRa-based LEO satellite IoT system. Specifically, multiple classes of LoRa end-devices (EDs) are taken into consideration, where each class of LoRa EDs is modeled by an independent Poisson point process (PPP). Both the channel characteristics of the satellite-to-Earth communications and the unique features of the LoRa network are considered to derive closed-form analytical expressions for the uplink access probability of such a new paradigm. Moreover, the non-trivial impact of the spreading factor, the ED’s density, the orbit altitude, and the satellite effective beamwidth on the system performance is thoroughly investigated. Extensive numerical simulations are conducted, which not only validate the accuracy of our theoretical analysis but also provide useful insights into the practical design and implementation of LoRa-based LEO satellite IoT system.

Authors

Institutions

Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-05-20
DOI
https://doi.org/10.1109/jiot.2025.3571928
Citations
3
Primary Topic
IoT Networks and Protocols
Type
article
Field-Weighted Citation Impact
2.19

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Toward LoRa-Based LEO Satellite IoT: A Stochastic Geometry Perspective

Dongxuan He, Michail Matthaiou, Quantao Yu, Jinhong Yuan et al.
3 citations
IEEE Internet of Things Journal
IoT Networks and Protocols
2.19
article

Toward LoRa-Based LEO Satellite IoT: A Stochastic Geometry Perspective

Dongxuan He, Michail Matthaiou, Quantao Yu, Jinhong Yuan, Hua Wang, Deepak Mishra
article en
3 citations

Abstract

Recently, Long-Range (LoRa) based low Earth orbit (LEO) satellite Internet of Things (IoT) has garnered growing interest from both academia and industry, since it can guarantee pervasive connectivity in an energy-efficient and cost-effective manner. In this paper, we provide a novel spherical stochastic geometry (SG) based analytical framework for characterizing the uplink access probability of LoRa-based LEO satellite IoT system. Specifically, multiple classes of LoRa end-devices (EDs) are taken into consideration, where each class of LoRa EDs is modeled by an independent Poisson point process (PPP). Both the channel characteristics of the satellite-to-Earth communications and the unique features of the LoRa network are considered to derive closed-form analytical expressions for the uplink access probability of such a new paradigm. Moreover, the non-trivial impact of the spreading factor, the ED’s density, the orbit altitude, and the satellite effective beamwidth on the system performance is thoroughly investigated. Extensive numerical simulations are conducted, which not only validate the accuracy of our theoretical analysis but also provide useful insights into the practical design and implementation of LoRa-based LEO satellite IoT system.

IEEE Internet of Things JournalVol. 12(15)
Beijing Institute of Technology (CN), Queen's University Belfast (GB), UNSW Sydney (AU)
European Commission, Queen's University Belfast, Queen's University, China Scholarship Council, Engineering and Physical Sciences Research Council, National Key Research and Development Program of China
Openalex Percentile: Top 12%
IoT Networks and Protocols
2.19
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.