Solving multi-sensor problem with a new approach

Smart environments is a technological concept that, according to Mark Weiser is ldquoa physical world that is richly and invisibly interwoven with sensors, actuators, displays, and computational elements, embedded seamlessly in the everyday objects of our lives, and connected through a continuous network.rdquo But sometimes the data gathered from the sensors is with different importance. It means some sensors are more reliable than others for some reasons. For example some sensors may be in relatively bad environments and some of the gathered data is destroyed or ruined. How to deal with the information gathered from different sensors efficiently is an important multi-sensors problem. The existence of multi-sensors problem will degrade the learning quality of classification models. And almost all of the existing classifier can not deal with this problem. So handling multi-sensors problem is important and necessary for building a high quality classification model and smart environments. In this paper a new classifier capable of dealing with this multi-sensors problem is proposed and it has a very good performance which is proved by experiments. This classifier can combine the information gathered from different sensors efficiently and in can add the new coming data to make a more efficient classifier even all of the original data is lost. Because of all the advantages it has, the new classifier is proposed sincerely to apply into smart environments.