Browsing by Author "Rajesh, G."

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Correlation analysis and statistical characterization of heterogeneous sensor data in environmental sensor networks
(2019) Rajesh, G.; Chaturvedi, A.
In wireless sensor networks, missing data is an inevitable phenomenon due to the inherent limitations of the sensor nodes, such as battery power constraints of nodes, missing communication links, bandwidth limitation, etc. Missing data adversely affects the quality of data received by the sink node. Since the data acquired by the sensor nodes in a multimodal environmental sensor network are spatially and temporally correlated, these correlations play a pivotal role in missing data recovery and data prediction. This paper proposes an analytical framework to characterize the correlation between two different pairs of modalities in an environmental sensor network using a set of classical and robust measures of correlation coefficient estimates. Monte Carlo simulation is performed to approximately model sensed environmental data characteristics. Three classical estimates (Pearson's correlation coefficient, Spearman's rank correlation coefficient, and Kendall's-tau rank correlation coefficient), and four robust estimates of correlation coefficients are used to establish the correlation between different pairs of sensed modalities in the data characteristics. The efficacy of these estimates is obtained using the two performance metrics, mean-squared error (MSE) and relative estimation efficiency (RE). Stationarity analysis among the acquired environmental variables shed light upon the best estimates of the correlation coefficient, which could be used for prediction of temperature modality in a known region of slope/stationarity in the data characteristics. The robustness of the correlation coefficient estimates in the presence of outliers present in the data due to noise, errors, low residual battery power of sensor nodes, etc. is also investigated. 2019 Elsevier B.V.
Correlation analysis and statistical characterization of heterogeneous sensor data in environmental sensor networks
(Elsevier B.V., 2019) Rajesh, G.; Chaturvedi, A.
In wireless sensor networks, missing data is an inevitable phenomenon due to the inherent limitations of the sensor nodes, such as battery power constraints of nodes, missing communication links, bandwidth limitation, etc. Missing data adversely affects the quality of data received by the sink node. Since the data acquired by the sensor nodes in a multimodal environmental sensor network are spatially and temporally correlated, these correlations play a pivotal role in missing data recovery and data prediction. This paper proposes an analytical framework to characterize the correlation between two different pairs of modalities in an environmental sensor network using a set of classical and robust measures of correlation coefficient estimates. Monte Carlo simulation is performed to approximately model sensed environmental data characteristics. Three classical estimates (Pearson's correlation coefficient, Spearman's rank correlation coefficient, and Kendall's-tau rank correlation coefficient), and four robust estimates of correlation coefficients are used to establish the correlation between different pairs of sensed modalities in the data characteristics. The efficacy of these estimates is obtained using the two performance metrics, mean-squared error (MSE) and relative estimation efficiency (RE). Stationarity analysis among the acquired environmental variables shed light upon the best estimates of the correlation coefficient, which could be used for prediction of temperature modality in a known region of slope/stationarity in the data characteristics. The robustness of the correlation coefficient estimates in the presence of outliers present in the data due to noise, errors, low residual battery power of sensor nodes, etc. is also investigated. © 2019 Elsevier B.V.
Correlation Analysis of Multimodal Sensor Data in Environmental Sensor Networks
(IEEE Computer Society help@computer.org, 2019) Rajesh, G.; Chaturvedi, A.
In this paper, the performance analysis of the classical measures of correlation coefficients, i.e., Pearson's correlation coefficient r-{P}, Spearman's rank correlation coefficient, r-{S} and Kendall-\tau rank correlation coefficient, r-{K}, and four robust correlation coefficients, is carried out in a specific scenario of multimodal environmental sensor network. The correlation coefficients were compared based on their estimated values with true values calculated using the slope of the regression line of the data points. The analysis is performed for two typical multivariate environmental sensor network scenarios, viz, positive correlation and negative correlation. The simulation results shed light on the required sample size of multimodal data and the class of the correlation coefficient required to give the best performance while establishing the correlation between the multimodal variables in environmental sensor data. Â© 2019 IEEE.
Data Reconstruction in Heterogeneous Environmental Wireless Sensor Networks Using Robust Tensor Principal Component Analysis
(Institute of Electrical and Electronics Engineers Inc., 2021) Rajesh, G.; Chaturvedi, A.
The major challenges during the data acquisition process in an environmental wireless sensor network (EWSN) architecture are the presence of outliers and missing data. Outliers and missing data are ubiquitous in EWSN due to sensor failures, external noise, power dwindling, communication failures etc. Robust tensor principal component analysis (RTPCA) decomposes a noisy data tensor into a low-rank tensor and a sparse tensor, which can be exploited for the data recovery in EWSNs, where the low-rank component represents the intrinsic data tensor and the sparse component represents the gross outlier tensor. In this paper, a novel probabilistic outlier modelling scheme using multivariate Chebyshev's inequality hypothesis is proposed, which maps the sample population and the associated magnitudes of outliers with the spatio-temporal correlations in the acquired data. The inherent spatio-temporal and multi-attribute correlations in the EWSN data are established using singular value methods. A tensor nuclear norm (TNN) which extracts more temporal and multi-attribute correlations in the sensory data through block circulant matricization is used as the minimization function in RTPCA. In the forest surveillance scenario, usage of RTPCA results in a reconstruction accuracy of approximately 90% for a dataset with a high missing ratio of 0.95 and the outliers having largest possible magnitudes considered. For oceanographic data, in which the variance is lower, the simulations show similar performance for different outlier contaminations, although RPTCA performs better than other matrix and tensor data completion methods in terms of reconstruction accuracy. © 2015 IEEE.