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Open AccessArticle

Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

State Key Laboratory of Precision Measurement Technology and Instrument, Tsinghua University, Beijing 100084, P. R. China
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Sensors 2007, 7(8), 1359-1386; https://doi.org/10.3390/s7081359
Received: 26 June 2007 / Accepted: 27 July 2007 / Published: 30 July 2007
Wireless sensor networks (WSNs) are autonomous networks that have beenfrequently deployed to collaboratively perform target localization and classification tasks.Their autonomous and collaborative features resemble the characteristics of agents. Suchsimilarities inspire the development of heterogeneous agent architecture for WSN in thispaper. The proposed agent architecture views WSN as multi-agent systems and mobileagents are employed to reduce in-network communication. According to the architecture,an energy based acoustic localization algorithm is proposed. In localization, estimate oftarget location is obtained by steepest descent search. The search algorithm adapts tomeasurement environments by dynamically adjusting its termination condition. With theagent architecture, target classification is accomplished by distributed support vectormachine (SVM). Mobile agents are employed for feature extraction and distributed SVMlearning to reduce communication load. Desirable learning performance is guaranteed bycombining support vectors and convex hull vectors. Fusion algorithms are designed tomerge SVM classification decisions made from various modalities. Real world experimentswith MICAz sensor nodes are conducted for vehicle localization and classification.Experimental results show the proposed agent architecture remarkably facilitates WSNdesigns and algorithm implementation. The localization and classification algorithms alsoprove to be accurate and energy efficient. View Full-Text
Keywords: wireless sensor networks; multi-agent system; mobile agent; target localization and classification; support vector machine. wireless sensor networks; multi-agent system; mobile agent; target localization and classification; support vector machine.
MDPI and ACS Style

Wang, X.; Bi, D.-W.; Ding, L.; Wang, S. Agent Collaborative Target Localization and Classification in Wireless Sensor Networks. Sensors 2007, 7, 1359-1386.

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