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Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

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ABSTRACT

Wireless sensor networks (WSNs) are autonomous networks that have been frequently deployed to collaboratively perform target localization and classification tasks. Their autonomous and collaborative features resemble the characteristics of agents. Such similarities inspire the development of heterogeneous agent architecture for WSN in this paper. The proposed agent architecture views WSN as multi-agent systems and mobile agents are employed to reduce in-network communication. According to the architecture, an energy based acoustic localization algorithm is proposed. In localization, estimate of target location is obtained by steepest descent search. The search algorithm adapts to measurement environments by dynamically adjusting its termination condition. With the agent architecture, target classification is accomplished by distributed support vector machine (SVM). Mobile agents are employed for feature extraction and distributed SVM learning to reduce communication load. Desirable learning performance is guaranteed by combining support vectors and convex hull vectors. Fusion algorithms are designed to merge SVM classification decisions made from various modalities. Real world experiments with MICAz sensor nodes are conducted for vehicle localization and classification. Experimental results show the proposed agent architecture remarkably facilitates WSN designs and algorithm implementation. The localization and classification algorithms also prove to be accurate and energy efficient.

No MeSH data available.


Deployment of MICAz motes on the schoolyard for vehicle localization and classification.
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f10-sensors-07-01359: Deployment of MICAz motes on the schoolyard for vehicle localization and classification.

Mentions: In the experiment, the 8 MICAz motes are randomly deployed on the schoolyard as illustrated in Figure 10. As shown in the figure, the 8 motes denoted by s1, s2 through s8 are deployed within an area approximately in the size of 26m×36m. Their corresponding x and y coordinates in the Cartesian coordinate system are marked in the parentheses. The star in the figure denotes an imaginary target.


Agent Collaborative Target Localization and Classification in Wireless Sensor Networks
Deployment of MICAz motes on the schoolyard for vehicle localization and classification.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3814857&req=5

f10-sensors-07-01359: Deployment of MICAz motes on the schoolyard for vehicle localization and classification.
Mentions: In the experiment, the 8 MICAz motes are randomly deployed on the schoolyard as illustrated in Figure 10. As shown in the figure, the 8 motes denoted by s1, s2 through s8 are deployed within an area approximately in the size of 26m×36m. Their corresponding x and y coordinates in the Cartesian coordinate system are marked in the parentheses. The star in the figure denotes an imaginary target.

View Article: PubMed Central

ABSTRACT

Wireless sensor networks (WSNs) are autonomous networks that have been frequently deployed to collaboratively perform target localization and classification tasks. Their autonomous and collaborative features resemble the characteristics of agents. Such similarities inspire the development of heterogeneous agent architecture for WSN in this paper. The proposed agent architecture views WSN as multi-agent systems and mobile agents are employed to reduce in-network communication. According to the architecture, an energy based acoustic localization algorithm is proposed. In localization, estimate of target location is obtained by steepest descent search. The search algorithm adapts to measurement environments by dynamically adjusting its termination condition. With the agent architecture, target classification is accomplished by distributed support vector machine (SVM). Mobile agents are employed for feature extraction and distributed SVM learning to reduce communication load. Desirable learning performance is guaranteed by combining support vectors and convex hull vectors. Fusion algorithms are designed to merge SVM classification decisions made from various modalities. Real world experiments with MICAz sensor nodes are conducted for vehicle localization and classification. Experimental results show the proposed agent architecture remarkably facilitates WSN designs and algorithm implementation. The localization and classification algorithms also prove to be accurate and energy efficient.

No MeSH data available.