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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.


Acoustic signatures measured by microphones at s1, s2 and s3.
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f11-sensors-07-01359: Acoustic signatures measured by microphones at s1, s2 and s3.

Mentions: In the localization experiment, the vehicle is a toy tank emitting loud sound. It is kept stationary during the localization. Nevertheless it must be clarified that the proposed method is equally applicable to localization of moving vehicles. In one experiment, the vehicle is deployed at a position whose Cartesian coordinates are (-4, 3). This is exactly where the target is in Figure 10. The acoustic signatures measured by microphone sensors at s1, s2 and s3 (shown as examples) are shown in Figure 11. The signatures have all been processed to eliminate the influence of sensor gain discrepancy.


Agent Collaborative Target Localization and Classification in Wireless Sensor Networks
Acoustic signatures measured by microphones at s1, s2 and s3.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3814857&req=5

f11-sensors-07-01359: Acoustic signatures measured by microphones at s1, s2 and s3.
Mentions: In the localization experiment, the vehicle is a toy tank emitting loud sound. It is kept stationary during the localization. Nevertheless it must be clarified that the proposed method is equally applicable to localization of moving vehicles. In one experiment, the vehicle is deployed at a position whose Cartesian coordinates are (-4, 3). This is exactly where the target is in Figure 10. The acoustic signatures measured by microphone sensors at s1, s2 and s3 (shown as examples) are shown in Figure 11. The signatures have all been processed to eliminate the influence of sensor gain discrepancy.

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.