Feature extraction and classification are two intertwined components in pattern recognition. Our hypothesis is that for each type of target, there exists an optimal set of features in conjunction with a specific classifier, which can yield the best performance in terms of classification accuracy using least amount of computation, measured by the number of features used. In this paper, our study is in the context of an application in wireless sensor networks (WSNs). Due to the extremely limited resources on each sensor platform, the decision making is prune to fault, making sensor fusion a necessity. We present a concept of dynamic target classification in WSNs. The main idea is to dynamically select the optimal combination of features and classifiers based on the ldquoprobabilityrdquo that the target to be classified might belong to a certain category. We use two data sets to validate our hypothesis and derive the optimal combination sets by minimizing a cost function. We apply the proposed algorithm to a scenario of collaborative target classification among a group of sensors in WSNs. Experimental results show that our approach can significantly reduce the computational time while at the same time, achieve better classification accuracy, compared with traditional classification approaches, making it a viable solution in practice.
['Ying Sun', 'Hairong Qi']
Dynamic target classification in wireless sensor networks