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Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

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.


HV and SV algorithm for distributed SVM learning with mobile agents.
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f7-sensors-07-01359: HV and SV algorithm for distributed SVM learning with mobile agents.

Mentions: Mobile agents should be used for distributed SVM learning in WSN. Otherwise large volumes of raw data have to be transmitted to the manager agents from each relevant observing agent. A mobile agent based distributed SVM learning with the HV and SV algorithm is presented in Figure 7. Note that in the proposed learning scheme, a feature extraction agent is incorporated into the SVM learning agent to extract feature vectors from raw data at local observing agents.


Agent Collaborative Target Localization and Classification in Wireless Sensor Networks
HV and SV algorithm for distributed SVM learning with mobile agents.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3814857&req=5

f7-sensors-07-01359: HV and SV algorithm for distributed SVM learning with mobile agents.
Mentions: Mobile agents should be used for distributed SVM learning in WSN. Otherwise large volumes of raw data have to be transmitted to the manager agents from each relevant observing agent. A mobile agent based distributed SVM learning with the HV and SV algorithm is presented in Figure 7. Note that in the proposed learning scheme, a feature extraction agent is incorporated into the SVM learning agent to extract feature vectors from raw data at local observing agents.

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.