Collaborative multi-modality target classification in distributed sensor networks

Abstract

A new computing paradigm which utilizes mobile agents to carry out collaborative target classification in distributed sensor networks is presented in this paper. Instead of each sensor sending local classification results to a processing center where the fusion process is taken place, a mobile agent is dispatched from the processing center and the fusion process is executed at each sensor node. The advantage of using mobile agent is that it achieves progressive accuracy and is task-adaptive. To improve the accuracy of classification, we implement Behavior Knowledge Space method for multi-modality fusion. We also modified the classical k-nearest-neighbor method to be adaptive to collaborative classification in a distributed network of sensor nodes. Experimental results based on a field demo are presented at the end of the paper.