Continuous Monitoring of the Ambient Factors via <i>ε</i>-Smooth Support Vector Regression

Abstract

Thanks to the advances of the Internet of Things (IoTs), more and more wireless sensor networks applications have been realized. One of the fundamental but crucial applications is the continuous monitoring of environmental factors including temperature, humidity, illumination, etc. We develop a nonlinear regression model which takes spatial and temporal information into account to construct a globally three-dimensional heat map for a closed space based on very sparse sensor deployment. However, fitting the whole-space heat map with a very limited number of sensor observations gives a very poor estimation when we use a nonlinear model. We call it the coverage hole problem. We utilize the uniform experimental design which is well known in industrial statistics to allocate the synthetic sensors. We estimate those synthetic sensor readings on the basis of linear model locally. We then apply ε -SSVR, a nonlinear support vector regression model to fit the globally three-dimensional heat map by combining real sensor and synthetic sensor readings. The numerical results demonstrate our proposed model can enhance the accuracy significantly.

Keywords

• smooth support vector regression
• coverage hole problem
• inverse distance weighting
• uniform design