Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location Article Swipe
Vivek Kaundal
,
Paawan Sharma
,
Manish Prateek
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1515/eletel-2017-0054
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1515/eletel-2017-0054
The paper aims at localization of the anchor node (fixed node) by pursuit nodes (movable node) in outdoor location. Two methods are studied for node localization. The first method is based on LNSM (Log Normal Shadowing Model) technique to localize the anchor node and the second method is based on Hybrid TLBO (Teacher Learning Based Optimization Algorithm)- Unilateral technique. In the first approach the ZigBee protocol has been used to localize the node, which uses RSSI (Received Signal Strength Indicator) values in dBm. LNSM technique is implemented in the self-designed hardware node and localization is studied for Outdoor location. The statistical analysis using RMSE (root mean square error) for outdoor location is done and distance error found to be 35 mtrs. The same outdoor location has been used and statistical analysis is done for localization of nodes using Hybrid TLBO-Unilateral technique. The Hybrid- TLBO Unilateral technique significantly localizes anchor node with distance error of 0.7 mtrs. The RSSI values obtained are normally distributed and standard deviation in RSSI value is observed as 1.01 for outdoor location. The node becomes 100% discoverable after using hybrid TLBO- Unilateral technique.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1515/eletel-2017-0054
- http://journals.pan.pl/Content/106050/PDF/54.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2766987958
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2766987958Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1515/eletel-2017-0054Digital Object Identifier
- Title
-
Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor LocationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-11-27Full publication date if available
- Authors
-
Vivek Kaundal, Paawan Sharma, Manish PrateekList of authors in order
- Landing page
-
https://doi.org/10.1515/eletel-2017-0054Publisher landing page
- PDF URL
-
https://journals.pan.pl/Content/106050/PDF/54.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://journals.pan.pl/Content/106050/PDF/54.pdfDirect OA link when available
- Concepts
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Node (physics), Wireless sensor network, Computer science, Wireless, Mean squared error, Statistical analysis, Real-time computing, Mathematics, Computer network, Engineering, Statistics, Telecommunications, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 2, 2021: 1, 2019: 1, 2018: 1, 2016: 1Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.uses | 75 |
| abstract_inverted_index.with | 151 |
| abstract_inverted_index.(root | 105 |
| abstract_inverted_index.Based | 55 |
| abstract_inverted_index.TLBO- | 185 |
| abstract_inverted_index.after | 182 |
| abstract_inverted_index.based | 31, 49 |
| abstract_inverted_index.error | 116, 153 |
| abstract_inverted_index.first | 28, 62 |
| abstract_inverted_index.found | 117 |
| abstract_inverted_index.mtrs. | 121, 156 |
| abstract_inverted_index.node) | 11, 16 |
| abstract_inverted_index.node, | 73 |
| abstract_inverted_index.nodes | 14, 137 |
| abstract_inverted_index.paper | 2 |
| abstract_inverted_index.using | 103, 138, 183 |
| abstract_inverted_index.value | 169 |
| abstract_inverted_index.which | 74 |
| abstract_inverted_index.(fixed | 10 |
| abstract_inverted_index.Hybrid | 51, 139 |
| abstract_inverted_index.Model) | 37 |
| abstract_inverted_index.Normal | 35 |
| abstract_inverted_index.Signal | 78 |
| abstract_inverted_index.ZigBee | 65 |
| abstract_inverted_index.anchor | 8, 42, 149 |
| abstract_inverted_index.error) | 108 |
| abstract_inverted_index.hybrid | 184 |
| abstract_inverted_index.method | 29, 47 |
| abstract_inverted_index.second | 46 |
| abstract_inverted_index.square | 107 |
| abstract_inverted_index.values | 81, 159 |
| abstract_inverted_index.Hybrid- | 143 |
| abstract_inverted_index.Outdoor | 98 |
| abstract_inverted_index.becomes | 179 |
| abstract_inverted_index.methods | 21 |
| abstract_inverted_index.outdoor | 18, 110, 124, 175 |
| abstract_inverted_index.pursuit | 13 |
| abstract_inverted_index.studied | 23, 96 |
| abstract_inverted_index.(Teacher | 53 |
| abstract_inverted_index.(movable | 15 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Learning | 54 |
| abstract_inverted_index.Strength | 79 |
| abstract_inverted_index.analysis | 102, 131 |
| abstract_inverted_index.approach | 63 |
| abstract_inverted_index.distance | 115, 152 |
| abstract_inverted_index.hardware | 91 |
| abstract_inverted_index.localize | 40, 71 |
| abstract_inverted_index.location | 111, 125 |
| abstract_inverted_index.normally | 162 |
| abstract_inverted_index.observed | 171 |
| abstract_inverted_index.obtained | 160 |
| abstract_inverted_index.protocol | 66 |
| abstract_inverted_index.standard | 165 |
| abstract_inverted_index.(Received | 77 |
| abstract_inverted_index.Shadowing | 36 |
| abstract_inverted_index.deviation | 166 |
| abstract_inverted_index.localizes | 148 |
| abstract_inverted_index.location. | 19, 99, 176 |
| abstract_inverted_index.technique | 38, 85, 146 |
| abstract_inverted_index.Indicator) | 80 |
| abstract_inverted_index.Unilateral | 58, 145, 186 |
| abstract_inverted_index.technique. | 59, 141, 187 |
| abstract_inverted_index.Algorithm)- | 57 |
| abstract_inverted_index.distributed | 163 |
| abstract_inverted_index.implemented | 87 |
| abstract_inverted_index.statistical | 101, 130 |
| abstract_inverted_index.Optimization | 56 |
| abstract_inverted_index.discoverable | 181 |
| abstract_inverted_index.localization | 5, 94, 135 |
| abstract_inverted_index.localization. | 26 |
| abstract_inverted_index.self-designed | 90 |
| abstract_inverted_index.significantly | 147 |
| abstract_inverted_index.TLBO-Unilateral | 140 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.60778835 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |