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.
Simple linear regression is usually used for WSN data reduction. The mechanism is concerned about energy consumption, but neglects the prediction accuracy. The prediction error from it is often ignored and inconsistencies are forwarded to the user application. This paper proposes to use a method based on multiple linear regression to improve prediction accuracy. The improvement is achieved by multivariate correlation of readings gathered by sensor nodes in field. Tests show that our solution outperforms some current solutions adopted in the literature.
Smart Grid is a recent area where the key feature is shift the present power system approach. But, the challenges of upgrade this present power system are several, such as: how to add reliable links between customers' home and data centers to enable smart meter sending power consumption data? and how to avoid big data and bottleneck on backbone to transmission of millions of these customers' devices? On the other hand, smart meter can be treated as a sensor network device. Thus, we can use the same data reduction mechanisms that have been studied in wireless sensor network to decrease its traffic. This paper proposes a data reduction approach based on prediction by simple linear regression to avoid flow of readings between smart meter and smart grid system. Our approach models the data gathered by smart meter and turns them into coefficients, which are sent to smart grid system instead of raw data. The prediction mechanism is performed by destination device using these coefficients for data recovering. Although the approximation performed by linear regression increases the prediction error in some instances, we have implemented an adaptive mechanism (Adaptive Simple Linear Regression -ASLR) that checks if the error or lack of relationship between the modeled samples is harmful to our data reduction approach. Thereby, two ways have been deployed to tune the samples window (amount of readings) for improve own approach. One mechanism adjusts samples window based on prediction error and another one adjusts samples window based on Pearson's coefficient.
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