Distributed Computing Paradigm for Target Classification in Sensor Networks
In this paper, we develop an energy and bandwidth efficient approach for target classification in sensor networks. Instead of adopting decision fusion to reduce network traffic as some recent research, we try to realize energy efficient target classification from a computational point of view. Our contribution is we propose a novel tree construction algorithm that autonomously organizes the distributed computation resources to execute the trained BP-network (BPN) in parallel manner. We evaluate the performance of our parallel computing paradigm compared to the traditional client/server-based computing paradigm from perspectives of energy consumption and communication traffic through analytical study. Finally, we take a target classification experiment to show the effectiveness of the proposed computing paradigm.
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