A Novel Weighted Fusion Based Efficient Clustering for Improved Wi-Fi Fingerprint Indoor Positioning Article Swipe
Pampa Sadhukhan
,
Keshav Dahal
,
Pradip Kumar Das
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/twc.2022.3225796
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/twc.2022.3225796
The received signal strength (RSS) based Wi-Fi fingerprint technique is not only a cost-effective means for indoor positioning but also provides reliable positioning accuracy in the indoor settings. Thus, such positioning technique has drawn many researchers0 attention to address its several limitations like degraded positioning accuracy due to continuous changes in surrounding environment, high positioning overhead, storage overhead etc. To address these issues, we propose a novel weighted fusion based
efficient clustering strategy (WF-ECS) for fingerprint positioning system in this paper. Our proposed technique WF-ECS computes a weighted average of the group of reference points (RPs) having similar RSS patterns and thus, creates a more perfect match between fused positional co-ordinates and RSS patterns considered for merging to a single entry. Extensive experimentation have been carried out to evaluate and compare the performances of our proposed system WF-ECS with the contemporary fingerprint positioning systems including our prior work using the simulation test bed, the dataset collected from our departmental building and also the benchmark dataset. The experimental results depict that our newly proposed technique WF-ECS can outperform the contemporary techniques in terms of positioning accuracy and positioning overhead while reducing the storage overhead in real indoor settings.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/twc.2022.3225796
- OA Status
- green
- Cited By
- 18
- References
- 57
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4312660080Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/twc.2022.3225796Digital Object Identifier
- Title
-
A Novel Weighted Fusion Based Efficient Clustering for Improved Wi-Fi Fingerprint Indoor PositioningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-12-07Full publication date if available
- Authors
-
Pampa Sadhukhan, Keshav Dahal, Pradip Kumar DasList of authors in order
- Landing page
-
https://doi.org/10.1109/twc.2022.3225796Publisher 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://research-portal.uws.ac.uk/en/publications/daf9f817-763e-48d7-bb25-a113e171c549Direct OA link when available
- Concepts
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Computer science, Cluster analysis, Sensor fusion, Overhead (engineering), Fingerprint (computing), Artificial intelligence, Algorithm, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 8, 2023: 6Per-year citation counts (last 5 years)
- References (count)
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57Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.also | 19, 160 |
| abstract_inverted_index.bed, | 151 |
| abstract_inverted_index.been | 123 |
| abstract_inverted_index.etc. | 58 |
| abstract_inverted_index.from | 155 |
| abstract_inverted_index.have | 122 |
| abstract_inverted_index.high | 53 |
| abstract_inverted_index.like | 42 |
| abstract_inverted_index.many | 34 |
| abstract_inverted_index.more | 103 |
| abstract_inverted_index.only | 11 |
| abstract_inverted_index.real | 193 |
| abstract_inverted_index.such | 29 |
| abstract_inverted_index.test | 150 |
| abstract_inverted_index.that | 168 |
| abstract_inverted_index.this | 78 |
| abstract_inverted_index.with | 137 |
| abstract_inverted_index.work | 146 |
| abstract_inverted_index.(RPs) | 94 |
| abstract_inverted_index.(RSS) | 4 |
| abstract_inverted_index.Thus, | 28 |
| abstract_inverted_index.Wi-Fi | 6 |
| abstract_inverted_index.based | 5 |
| abstract_inverted_index.drawn | 33 |
| abstract_inverted_index.fused | 107 |
| abstract_inverted_index.group | 90 |
| abstract_inverted_index.match | 105 |
| abstract_inverted_index.means | 14 |
| abstract_inverted_index.newly | 170 |
| abstract_inverted_index.novel | 66 |
| abstract_inverted_index.prior | 145 |
| abstract_inverted_index.terms | 180 |
| abstract_inverted_index.these | 61 |
| abstract_inverted_index.thus, | 100 |
| abstract_inverted_index.using | 147 |
| abstract_inverted_index.while | 187 |
| abstract_inverted_index.WF-ECS | 83, 136, 173 |
| abstract_inverted_index.depict | 167 |
| abstract_inverted_index.entry. | 119 |
| abstract_inverted_index.fusion | 68 |
| abstract_inverted_index.having | 95 |
| abstract_inverted_index.indoor | 16, 26, 194 |
| abstract_inverted_index.paper. | 79 |
| abstract_inverted_index.points | 93 |
| abstract_inverted_index.signal | 2 |
| abstract_inverted_index.single | 118 |
| abstract_inverted_index.system | 76, 135 |
| abstract_inverted_index.address | 38, 60 |
| abstract_inverted_index.average | 87 |
| abstract_inverted_index.between | 106 |
| abstract_inverted_index.carried | 124 |
| abstract_inverted_index.changes | 49 |
| abstract_inverted_index.compare | 129 |
| abstract_inverted_index.creates | 101 |
| abstract_inverted_index.dataset | 153 |
| abstract_inverted_index.issues, | 62 |
| abstract_inverted_index.merging | 115 |
| abstract_inverted_index.perfect | 104 |
| abstract_inverted_index.propose | 64 |
| abstract_inverted_index.results | 166 |
| abstract_inverted_index.several | 40 |
| abstract_inverted_index.similar | 96 |
| abstract_inverted_index.storage | 56, 190 |
| abstract_inverted_index.systems | 142 |
| abstract_inverted_index.(WF-ECS) | 72 |
| abstract_inverted_index.accuracy | 23, 45, 183 |
| abstract_inverted_index.building | 158 |
| abstract_inverted_index.computes | 84 |
| abstract_inverted_index.dataset. | 163 |
| abstract_inverted_index.degraded | 43 |
| abstract_inverted_index.evaluate | 127 |
| abstract_inverted_index.overhead | 57, 186, 191 |
| abstract_inverted_index.patterns | 98, 112 |
| abstract_inverted_index.proposed | 81, 134, 171 |
| abstract_inverted_index.provides | 20 |
| abstract_inverted_index.received | 1 |
| abstract_inverted_index.reducing | 188 |
| abstract_inverted_index.reliable | 21 |
| abstract_inverted_index.strategy | 71 |
| abstract_inverted_index.strength | 3 |
| abstract_inverted_index.weighted | 67, 86 |
| abstract_inverted_index.Extensive | 120 |
| abstract_inverted_index.attention | 36 |
| abstract_inverted_index.benchmark | 162 |
| abstract_inverted_index.collected | 154 |
| abstract_inverted_index.including | 143 |
| abstract_inverted_index.overhead, | 55 |
| abstract_inverted_index.reference | 92 |
| abstract_inverted_index.settings. | 27, 195 |
| abstract_inverted_index.technique | 8, 31, 82, 172 |
| abstract_inverted_index.clustering | 70 |
| abstract_inverted_index.considered | 113 |
| abstract_inverted_index.continuous | 48 |
| abstract_inverted_index.outperform | 175 |
| abstract_inverted_index.positional | 108 |
| abstract_inverted_index.simulation | 149 |
| abstract_inverted_index.techniques | 178 |
| abstract_inverted_index.fingerprint | 7, 74, 140 |
| abstract_inverted_index.limitations | 41 |
| abstract_inverted_index.positioning | 17, 22, 30, 44, 54, 75, 141, 182, 185 |
| abstract_inverted_index.surrounding | 51 |
| abstract_inverted_index.co-ordinates | 109 |
| abstract_inverted_index.contemporary | 139, 177 |
| abstract_inverted_index.departmental | 157 |
| abstract_inverted_index.environment, | 52 |
| abstract_inverted_index.experimental | 165 |
| abstract_inverted_index.performances | 131 |
| abstract_inverted_index.researchers0 | 35 |
| abstract_inverted_index.cost-effective | 13 |
| abstract_inverted_index.experimentation | 121 |
| abstract_inverted_index.based<br/>efficient | 69 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.85091659 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |