Abstract: Sensors are always used to monitor external environment,whenever faults occur in sensors,there will be prediction errors.In order to improve the ability of fault tolerance when faults occur,this paper proposed a fault tolerant regression model for sensing data.Firstly,we analyzed linear models including least squares and ridge regression,and also analyzed statistics of regression model.Secondly,we analyzed the related statistics when sensors failed,and analyzed the lower and upper bounds of covariate matrix based on these statistics.Finally,we defined fault index based on covariate matrix,and transformed the optimum of model into minimize fault index and mean square error simultaneously.The experiments show that the proposed fault tolerant model has lower error than traditional least squares and ridge regression,and thus has better robustness when sensors fail.

Key words: Sensors,Sensing data,Regression model,Fault tolerant mechanism