Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction

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Performance evaluation of real-time multivariate data reduction models for adaptive-threshold in wireless sensor networks

This paper presents a new metric to assess the performance of different multivariate data reduction models in wireless sensor networks (WSNs). The proposed metric is called Updating Frequency Metric (UFM) which is defined as the frequency of updating the model reference parameters during data collection. A method for estimating the error threshold value during the training phase is also suggested. The proposed threshold of error is used to update the model reference parameters when it is necessary. Numerical analysis and simulation results show that the proposed metric validates its effectiveness in the performance of multivariate data reduction models in terms of the sensor node energy consumption. Furthermore, the proposed adaptive threshold enhances the model's performance more than the non-adaptive threshold in decreasing the frequency of updating the model reference parameters which positively prolongs the lifetime of the node. The adaptive threshold improves the frequency of updating the parameters by 80% and 52% in comparison to the non-adaptive threshold for multivariate data reduction models of MLR-B and PCA-B respectively

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

A prediÃÃo de dados nÃo enviados ao sorvedouro à uma tÃcnica usada para economizar energia em RSSF atravÃs da reduÃÃo da quantidade de dados trafegados. PorÃm, os dispositivos devem rodar mecanismos simples devido as suas limitaÃÃes de recursos, os quais podem gerar erros indesejÃveis e isto pode nÃo ser muito preciso. Este trabalho propÃe um mÃtodo baseado na correlaÃÃo espacial e temporal multivariada para melhorar a precisÃo da prediÃÃo na reduÃÃo de dados de Redes de Sensores Sem Fio (RSSF). SimulaÃÃes foram feitas envolvendo funÃÃes de regressÃo linear simples e regressÃo linear mÃltipla para verificar o desempenho do mÃtodo proposto. Os resultados mostram um maior grau de correlaÃÃo entre as variÃveis coletadas em campo, quando comparadas com a variÃvel tempo, a qual à uma variÃvel independente usada para prediÃÃo. A precisÃo da prediÃÃo à menor quando a regressÃo linear simples à usada, enquanto a regressÃo linear mÃltipla à mais precisa. AlÃm disto, a soluÃÃo proposta supera algumas soluÃÃes atuais em cerca de 50% na prediÃÃo da variÃvel umidade e em cerca de 21% na prediÃÃo da variÃvel luminosidade.Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, sensor devices must run simple mechanisms due to its constrained resources, which may cause unwanted errors and this may not be very accurate. This work proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to variable time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, the proposed solution outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction