Comparative Analysis Of Sigmoidal Feedforward Artificial Neural Networks And Radial Basis Function Networks Approach For Localization In Wireless Sensor Networks Article Swipe
Ashish Payal
,
Sien Chi
,
B. V. R. Reddy
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.1125122
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.1125122
With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m2 grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.1125122
- OA Status
- green
- Cited By
- 3
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2427241640
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2427241640Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.1125122Digital Object Identifier
- Title
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Comparative Analysis Of Sigmoidal Feedforward Artificial Neural Networks And Radial Basis Function Networks Approach For Localization In Wireless Sensor NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2016Year of publication
- Publication date
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2016-04-15Full publication date if available
- Authors
-
Ashish Payal, Sien Chi, B. V. R. ReddyList of authors in order
- Landing page
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https://doi.org/10.5281/zenodo.1125122Publisher 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://doi.org/10.5281/zenodo.1125122Direct OA link when available
- Concepts
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Sigmoid function, Radial basis function, Artificial neural network, Computer science, Feedforward neural network, Wireless sensor network, Feed forward, Function (biology), Artificial intelligence, Control engineering, Computer network, Engineering, Biology, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1, 2019: 1, 2018: 1Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.coordinate | 58 |
| abstract_inverted_index.developing | 91 |
| abstract_inverted_index.efficiency | 29 |
| abstract_inverted_index.estimation | 40 |
| abstract_inverted_index.evaluation | 122 |
| abstract_inverted_index.increasing | 2 |
| abstract_inverted_index.simulation | 132 |
| abstract_inverted_index.Feedforward | 79 |
| abstract_inverted_index.application | 5 |
| abstract_inverted_index.demonstrate | 135 |
| abstract_inverted_index.effectively | 134 |
| abstract_inverted_index.localization | 34, 92, 144 |
| abstract_inverted_index.comprehensive | 121 |
| abstract_inverted_index.m<sup>2</sup> | 114 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.65943053 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |