Indoor WiFi Fingerprint Localization Based on Dual- Population-PSO of Stacked Autoencoder and Multi- Label Classification Article Swipe
Ying Cheng
,
Kun Zhang
,
Bing Zheng
,
Keliu Long
,
Yu Zhou
,
Ming Jin
,
Jing He
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6512205/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6512205/v1
WiFi fingerprinting has become a widely adopted solution for indoor localization due to its low deployment cost and wide availability. However, its positioning accuracy is often unsatisfactory, especially in complex environments involving multiple floors and buildings, where signal interference and structural complexity significantly degrade localization performance. To address these challenges, this paper proposes a deep neural network (DNN) framework that integrates a Stacked Autoencoder (SAE) for feature extraction and a multi-label classification strategy for accurate localization using received signal strength (RSS) data.To optimize the model parameters effectively in high-dimensional spaces, we introduce a Dual-Swarm Particle Swarm Optimization (DSPSO) algorithm. Unlike conventional PSO, which is prone to premature convergence, DSPSO partitions the particle population into two distinct sub-swarms with adaptive update mechanisms to enhance global exploration and avoid local optima.Experimental evaluations on seven 100-dimensional benchmark functions demonstrate that DSPSO outperforms traditional PSO and recent algorithms like the Sparrow Search Algorithm (SSA), achieving the global optimum in four cases. When applied to the WiFi localization task, the DSPSO-optimized SAE-DNN model achieves an average positioning error of 9.42 meters—representing 13.74% improvement over the Support Vector Regression (SVR) model and 24.03% improvement over the non-optimized version. Furthermore, the model achieves 100% accuracy in building identification and 92.97% accuracy in floor prediction, proving its effectiveness in multi-building, multi-floor indoor localization scenarios.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-6512205/v1
- https://www.researchsquare.com/article/rs-6512205/latest.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410323674
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410323674Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-6512205/v1Digital Object Identifier
- Title
-
Indoor WiFi Fingerprint Localization Based on Dual- Population-PSO of Stacked Autoencoder and Multi- Label ClassificationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-13Full publication date if available
- Authors
-
Ying Cheng, Kun Zhang, Bing Zheng, Keliu Long, Yu Zhou, Ming Jin, Jing HeList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-6512205/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-6512205/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-6512205/latest.pdfDirect OA link when available
- Concepts
-
Autoencoder, Fingerprint (computing), Dual (grammatical number), Artificial intelligence, Computer science, Pattern recognition (psychology), Population, Medicine, Deep learning, Environmental health, Literature, ArtTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.which | 103 |
| abstract_inverted_index.(SSA), | 150 |
| abstract_inverted_index.13.74% | 177 |
| abstract_inverted_index.24.03% | 187 |
| abstract_inverted_index.92.97% | 203 |
| abstract_inverted_index.Search | 148 |
| abstract_inverted_index.Unlike | 100 |
| abstract_inverted_index.Vector | 182 |
| abstract_inverted_index.become | 4 |
| abstract_inverted_index.cases. | 157 |
| abstract_inverted_index.floors | 34 |
| abstract_inverted_index.global | 124, 153 |
| abstract_inverted_index.indoor | 10, 214 |
| abstract_inverted_index.neural | 56 |
| abstract_inverted_index.recent | 143 |
| abstract_inverted_index.signal | 38, 79 |
| abstract_inverted_index.update | 120 |
| abstract_inverted_index.widely | 6 |
| abstract_inverted_index.(DSPSO) | 98 |
| abstract_inverted_index.SAE-DNN | 167 |
| abstract_inverted_index.Sparrow | 147 |
| abstract_inverted_index.Stacked | 63 |
| abstract_inverted_index.Support | 181 |
| abstract_inverted_index.address | 48 |
| abstract_inverted_index.adopted | 7 |
| abstract_inverted_index.applied | 159 |
| abstract_inverted_index.average | 171 |
| abstract_inverted_index.complex | 30 |
| abstract_inverted_index.data.To | 82 |
| abstract_inverted_index.degrade | 44 |
| abstract_inverted_index.enhance | 123 |
| abstract_inverted_index.feature | 67 |
| abstract_inverted_index.network | 57 |
| abstract_inverted_index.optimum | 154 |
| abstract_inverted_index.proving | 208 |
| abstract_inverted_index.spaces, | 90 |
| abstract_inverted_index.However, | 21 |
| abstract_inverted_index.Particle | 95 |
| abstract_inverted_index.accuracy | 24, 198, 204 |
| abstract_inverted_index.accurate | 75 |
| abstract_inverted_index.achieves | 169, 196 |
| abstract_inverted_index.adaptive | 119 |
| abstract_inverted_index.building | 200 |
| abstract_inverted_index.distinct | 116 |
| abstract_inverted_index.multiple | 33 |
| abstract_inverted_index.optimize | 83 |
| abstract_inverted_index.particle | 112 |
| abstract_inverted_index.proposes | 53 |
| abstract_inverted_index.received | 78 |
| abstract_inverted_index.solution | 8 |
| abstract_inverted_index.strategy | 73 |
| abstract_inverted_index.strength | 80 |
| abstract_inverted_index.version. | 192 |
| abstract_inverted_index.Algorithm | 149 |
| abstract_inverted_index.achieving | 151 |
| abstract_inverted_index.benchmark | 134 |
| abstract_inverted_index.framework | 59 |
| abstract_inverted_index.functions | 135 |
| abstract_inverted_index.introduce | 92 |
| abstract_inverted_index.involving | 32 |
| abstract_inverted_index.premature | 107 |
| abstract_inverted_index.Dual-Swarm | 94 |
| abstract_inverted_index.Regression | 183 |
| abstract_inverted_index.algorithm. | 99 |
| abstract_inverted_index.algorithms | 144 |
| abstract_inverted_index.buildings, | 36 |
| abstract_inverted_index.complexity | 42 |
| abstract_inverted_index.deployment | 16 |
| abstract_inverted_index.especially | 28 |
| abstract_inverted_index.extraction | 68 |
| abstract_inverted_index.integrates | 61 |
| abstract_inverted_index.mechanisms | 121 |
| abstract_inverted_index.parameters | 86 |
| abstract_inverted_index.partitions | 110 |
| abstract_inverted_index.population | 113 |
| abstract_inverted_index.scenarios. | 216 |
| abstract_inverted_index.structural | 41 |
| abstract_inverted_index.sub-swarms | 117 |
| abstract_inverted_index.Autoencoder | 64 |
| abstract_inverted_index.challenges, | 50 |
| abstract_inverted_index.demonstrate | 136 |
| abstract_inverted_index.effectively | 87 |
| abstract_inverted_index.evaluations | 130 |
| abstract_inverted_index.exploration | 125 |
| abstract_inverted_index.improvement | 178, 188 |
| abstract_inverted_index.multi-floor | 213 |
| abstract_inverted_index.multi-label | 71 |
| abstract_inverted_index.outperforms | 139 |
| abstract_inverted_index.positioning | 23, 172 |
| abstract_inverted_index.prediction, | 207 |
| abstract_inverted_index.traditional | 140 |
| abstract_inverted_index.Furthermore, | 193 |
| abstract_inverted_index.Optimization | 97 |
| abstract_inverted_index.conventional | 101 |
| abstract_inverted_index.convergence, | 108 |
| abstract_inverted_index.environments | 31 |
| abstract_inverted_index.interference | 39 |
| abstract_inverted_index.localization | 11, 45, 76, 163, 215 |
| abstract_inverted_index.performance. | 46 |
| abstract_inverted_index.availability. | 20 |
| abstract_inverted_index.effectiveness | 210 |
| abstract_inverted_index.non-optimized | 191 |
| abstract_inverted_index.significantly | 43 |
| abstract_inverted_index.classification | 72 |
| abstract_inverted_index.fingerprinting | 2 |
| abstract_inverted_index.identification | 201 |
| abstract_inverted_index.100-dimensional | 133 |
| abstract_inverted_index.DSPSO-optimized | 166 |
| abstract_inverted_index.multi-building, | 212 |
| abstract_inverted_index.unsatisfactory, | 27 |
| abstract_inverted_index.high-dimensional | 89 |
| abstract_inverted_index.optima.Experimental | 129 |
| abstract_inverted_index.meters—representing | 176 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.19768186 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |