Modeling and Performance Analysis of IoT-Over-LEO Satellite Systems Under Realistic Operational Constraints: A Stochastic Geometry Approach Article Swipe
Wen-Yu Dong
,
Shaoshi Yang
,
Ping Zhang
,
Sheng Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jiot.2025.3570843
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jiot.2025.3570843
The growing demand for reliable and extensive connectivity has made low Earth orbit (LEO) satellites aided Internet of Things (IoT) systems a critical area of research. However, current theoretical studies on IoT-over-LEO satellite systems often rely on unrealistic assumptions, such as infinite terrestrial areas and omnidirectional satellite coverage, leaving significant gaps in theoretical analysis for more realistic operational constraints. These constraints involve finite terrestrial area, limited satellite coverage, Earth curvature effect, integral uplink and downlink analysis, and link-dependent interference. To address these gaps, this paper proposes a novel stochastic geometry based model to rigorously analyze the performance of IoT-over-LEO satellite systems. By adopting a binomial point process (BPP) instead of the conventional Poisson point process (PPP), our model accurately characterizes the geographical distribution of a fixed number of IoT devices in a finite terrestrial region. This modeling framework enables the derivation of distance distribution functions for both the links from the terrestrial IoT devices to the satellites (T-S) and from the satellites to the Earth station (S-ES), while also accounting for limited satellite coverage and Earth curvature effects. To realistically represent channel
conditions, the Nakagami fading model is employed for the TS links to characterize diverse small-scale fading environments, while the shadowed-Rician fading model is used for the S-ES links to capture the combined effects of shadowing and dominant
line-of-sight paths. Furthermore, the analysis incorporates uplink and downlink interference, ensuring a comprehensive evaluation of system performance. The accuracy and effectiveness of our theoretical framework are validated through extensive Monte Carlo simulations. These results provide insights into key
performance metrics, such as coverage probability and average ergodic rate, for both individual links and the overall system. Our study also offers an important analytical tool for optimizing the design and performance of IoT-over-LEO satellite systems with the operational constraints that are more realistic.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jiot.2025.3570843
- OA Status
- green
- Cited By
- 1
- References
- 39
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- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410428239Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jiot.2025.3570843Digital Object Identifier
- Title
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Modeling and Performance Analysis of IoT-Over-LEO Satellite Systems Under Realistic Operational Constraints: A Stochastic Geometry ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-16Full publication date if available
- Authors
-
Wen-Yu Dong, Shaoshi Yang, Ping Zhang, Sheng ChenList of authors in order
- Landing page
-
https://doi.org/10.1109/jiot.2025.3570843Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2505.12336Direct OA link when available
- Concepts
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Computer science, Stochastic geometry, Satellite, Distributed computing, Remote sensing, Aerospace engineering, Mathematics, Geology, Statistics, EngineeringTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 17, 24, 97, 109, 123, 127, 141, 214, 231, 238, 286 |
| abstract_inverted_index.on | 30, 36 |
| abstract_inverted_index.to | 92, 154, 162, 192, 209 |
| abstract_inverted_index.IoT | 128, 152 |
| abstract_inverted_index.Our | 272 |
| abstract_inverted_index.The | 0, 234 |
| abstract_inverted_index.and | 5, 44, 73, 76, 158, 174, 216, 224, 236, 260, 268, 284 |
| abstract_inverted_index.are | 242, 295 |
| abstract_inverted_index.for | 3, 54, 145, 170, 188, 205, 264, 280 |
| abstract_inverted_index.has | 8 |
| abstract_inverted_index.low | 10 |
| abstract_inverted_index.our | 116, 239 |
| abstract_inverted_index.the | 95, 110, 120, 139, 147, 150, 155, 160, 163, 182, 189, 199, 206, 211, 220, 269, 282, 291 |
| abstract_inverted_index.S-ES | 207 |
| abstract_inverted_index.This | 135 |
| abstract_inverted_index.also | 168, 274 |
| abstract_inverted_index.area | 23 |
| abstract_inverted_index.both | 146, 265 |
| abstract_inverted_index.from | 149, 159 |
| abstract_inverted_index.gaps | 50 |
| abstract_inverted_index.into | 253 |
| abstract_inverted_index.made | 9 |
| abstract_inverted_index.more | 55, 296 |
| abstract_inverted_index.rely | 35 |
| abstract_inverted_index.such | 39, 256 |
| abstract_inverted_index.that | 294 |
| abstract_inverted_index.this | 83 |
| abstract_inverted_index.tool | 279 |
| abstract_inverted_index.used | 204 |
| abstract_inverted_index.with | 290 |
| abstract_inverted_index.(BPP) | 107 |
| abstract_inverted_index.(IoT) | 19 |
| abstract_inverted_index.(LEO) | 13 |
| abstract_inverted_index.(T-S) | 157 |
| abstract_inverted_index.Carlo | 247 |
| abstract_inverted_index.Earth | 11, 68, 164, 175 |
| abstract_inverted_index.Monte | 246 |
| abstract_inverted_index.These | 59, 249 |
| abstract_inverted_index.aided | 15 |
| abstract_inverted_index.area, | 64 |
| abstract_inverted_index.areas | 43 |
| abstract_inverted_index.based | 90 |
| abstract_inverted_index.fixed | 125 |
| abstract_inverted_index.gaps, | 82 |
| abstract_inverted_index.links | 148, 191, 208, 267 |
| abstract_inverted_index.model | 91, 117, 185, 202 |
| abstract_inverted_index.novel | 87 |
| abstract_inverted_index.often | 34 |
| abstract_inverted_index.orbit | 12 |
| abstract_inverted_index.paper | 84 |
| abstract_inverted_index.point | 105, 113 |
| abstract_inverted_index.rate, | 263 |
| abstract_inverted_index.study | 273 |
| abstract_inverted_index.these | 81 |
| abstract_inverted_index.while | 167, 198 |
| abstract_inverted_index.(PPP), | 115 |
| abstract_inverted_index.Things | 18 |
| abstract_inverted_index.demand | 2 |
| abstract_inverted_index.design | 283 |
| abstract_inverted_index.fading | 184, 196, 201 |
| abstract_inverted_index.finite | 62, 132 |
| abstract_inverted_index.number | 126 |
| abstract_inverted_index.offers | 275 |
| abstract_inverted_index.paths. | 218 |
| abstract_inverted_index.system | 232 |
| abstract_inverted_index.uplink | 72, 223 |
| abstract_inverted_index.(S-ES), | 166 |
| abstract_inverted_index.Poisson | 112 |
| abstract_inverted_index.address | 80 |
| abstract_inverted_index.analyze | 94 |
| abstract_inverted_index.average | 261 |
| abstract_inverted_index.capture | 210 |
| abstract_inverted_index.current | 27 |
| abstract_inverted_index.devices | 129, 153 |
| abstract_inverted_index.diverse | 194 |
| abstract_inverted_index.effect, | 70 |
| abstract_inverted_index.effects | 213 |
| abstract_inverted_index.enables | 138 |
| abstract_inverted_index.ergodic | 262 |
| abstract_inverted_index.growing | 1 |
| abstract_inverted_index.instead | 108 |
| abstract_inverted_index.involve | 61 |
| abstract_inverted_index.leaving | 48 |
| abstract_inverted_index.limited | 65, 171 |
| abstract_inverted_index.overall | 270 |
| abstract_inverted_index.process | 106, 114 |
| abstract_inverted_index.provide | 251 |
| abstract_inverted_index.region. | 134 |
| abstract_inverted_index.results | 250 |
| abstract_inverted_index.station | 165 |
| abstract_inverted_index.studies | 29 |
| abstract_inverted_index.system. | 271 |
| abstract_inverted_index.systems | 20, 33, 289 |
| abstract_inverted_index.through | 244 |
| abstract_inverted_index.However, | 26 |
| abstract_inverted_index.Internet | 16 |
| abstract_inverted_index.Nakagami | 183 |
| abstract_inverted_index.accuracy | 235 |
| abstract_inverted_index.adopting | 102 |
| abstract_inverted_index.analysis | 53, 221 |
| abstract_inverted_index.binomial | 104 |
| abstract_inverted_index.combined | 212 |
| abstract_inverted_index.coverage | 173, 258 |
| abstract_inverted_index.critical | 22 |
| abstract_inverted_index.distance | 142 |
| abstract_inverted_index.downlink | 74, 225 |
| abstract_inverted_index.effects. | 177 |
| abstract_inverted_index.employed | 187 |
| abstract_inverted_index.ensuring | 227 |
| abstract_inverted_index.geometry | 89 |
| abstract_inverted_index.infinite | 41 |
| abstract_inverted_index.insights | 252 |
| abstract_inverted_index.integral | 71 |
| abstract_inverted_index.metrics, | 255 |
| abstract_inverted_index.modeling | 136 |
| abstract_inverted_index.proposes | 85 |
| abstract_inverted_index.reliable | 4 |
| abstract_inverted_index.systems. | 100 |
| abstract_inverted_index.analysis, | 75 |
| abstract_inverted_index.coverage, | 47, 67 |
| abstract_inverted_index.curvature | 69, 176 |
| abstract_inverted_index.extensive | 6, 245 |
| abstract_inverted_index.framework | 137, 241 |
| abstract_inverted_index.functions | 144 |
| abstract_inverted_index.important | 277 |
| abstract_inverted_index.realistic | 56 |
| abstract_inverted_index.represent | 180 |
| abstract_inverted_index.research. | 25 |
| abstract_inverted_index.satellite | 32, 46, 66, 99, 172, 288 |
| abstract_inverted_index.shadowing | 215 |
| abstract_inverted_index.validated | 243 |
| abstract_inverted_index.accounting | 169 |
| abstract_inverted_index.accurately | 118 |
| abstract_inverted_index.analytical | 278 |
| abstract_inverted_index.derivation | 140 |
| abstract_inverted_index.evaluation | 230 |
| abstract_inverted_index.individual | 266 |
| abstract_inverted_index.optimizing | 281 |
| abstract_inverted_index.realistic. | 297 |
| abstract_inverted_index.rigorously | 93 |
| abstract_inverted_index.satellites | 14, 156, 161 |
| abstract_inverted_index.stochastic | 88 |
| abstract_inverted_index.constraints | 60, 293 |
| abstract_inverted_index.operational | 57, 292 |
| abstract_inverted_index.performance | 96, 285 |
| abstract_inverted_index.probability | 259 |
| abstract_inverted_index.significant | 49 |
| abstract_inverted_index.small-scale | 195 |
| abstract_inverted_index.terrestrial | 42, 63, 133, 151 |
| abstract_inverted_index.theoretical | 28, 52, 240 |
| abstract_inverted_index.unrealistic | 37 |
| abstract_inverted_index.Furthermore, | 219 |
| abstract_inverted_index.IoT-over-LEO | 31, 98, 287 |
| abstract_inverted_index.assumptions, | 38 |
| abstract_inverted_index.characterize | 193 |
| abstract_inverted_index.connectivity | 7 |
| abstract_inverted_index.constraints. | 58 |
| abstract_inverted_index.conventional | 111 |
| abstract_inverted_index.distribution | 122, 143 |
| abstract_inverted_index.geographical | 121 |
| abstract_inverted_index.incorporates | 222 |
| abstract_inverted_index.performance. | 233 |
| abstract_inverted_index.simulations. | 248 |
| abstract_inverted_index.characterizes | 119 |
| abstract_inverted_index.comprehensive | 229 |
| abstract_inverted_index.effectiveness | 237 |
| abstract_inverted_index.environments, | 197 |
| abstract_inverted_index.interference, | 226 |
| abstract_inverted_index.interference. | 78 |
| abstract_inverted_index.realistically | 179 |
| abstract_inverted_index.link-dependent | 77 |
| abstract_inverted_index.omnidirectional | 45 |
| abstract_inverted_index.shadowed-Rician | 200 |
| abstract_inverted_index.key<br/>performance | 254 |
| abstract_inverted_index.channel<br/>conditions, | 181 |
| abstract_inverted_index.dominant<br/>line-of-sight | 217 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.92951381 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |