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Autonomous framework for sensor network quality annotation: maximum probability clustering approach
Citation
Dutta, R and Das, A and Smith, D and Aryal, J and Morshed, A and Terhorst, A, Autonomous framework for sensor network quality annotation: maximum probability clustering approach, Procedia Computer Science Volume 29: ICCS 2014, 10-12 June 2014, Cairns, Australia, pp. 2201-2207. ISSN 1877-0509 (2014) [Refereed Conference Paper]
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Copyright Statement
Copyright The Authors. Licenced under Creative Commons Attribution 3.0 (CC BY 3.0) http://creativecommons.org/licenses/by-nc-nd/3.0/
DOI: doi:10.1016/j.procs.2014.05.205
Abstract
In this paper an autonomous feature clustering framework has been proposed for performance and
reliability evaluation of an environmental sensor network. Environmental time series were statistically
preprocessed to extract multiple semantic features. A novel hybrid clustering framework was designed
based on Principal Component Analysis (PCA), Guided Self-Organizing Map (G-SOM), and Fuzzy-CMeans
(FCM) to cluster the historical multi-feature space into probabilistic state classes. Finally a
dynamic performance annotation mechanism was developed based on Maximum (Bayesian)
Probability Rule (MPR) to quantify the performance of an individual sensor node and network. Based
on the results from this framework, a "data quality knowledge map" was visualized to demonstrate the
effectiveness of this framework.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | maximum (Bayesian) probability rule (MPR), PCA, FCM, SOM, sensor network |
| Research Division: | Engineering |
| Research Group: | Geomatic Engineering |
| Research Field: | Photogrammetry and Remote Sensing |
| Objective Division: | Environment |
| Objective Group: | Environmental and Natural Resource Evaluation |
| Objective Field: | Environmental and Natural Resource Evaluation not elsewhere classified |
| UTAS Author: | Das, A (Dr Aruneema Das) |
| UTAS Author: | Aryal, J (Dr Jagannath Aryal) |
| ID Code: | 92518 |
| Year Published: | 2014 |
| Deposited By: | Geography and Environmental Studies |
| Deposited On: | 2014-06-23 |
| Last Modified: | 2017-10-24 |
| Downloads: | 311 View Download Statistics |
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