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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.

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Heterogeneous agent architecture for WSN.
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f3-sensors-07-01359: Heterogeneous agent architecture for WSN.

Mentions: The proposed heterogeneous architecture framework is shown in Figure 3. From the figure, it is obvious that the architecture is a heterogeneous one, for both multi-agent systems and mobile agents are incorporated. The multi-agent system is a hierarchical one. The top is the interface agent, which receives user query about the environment, inquires the lower level agents accordingly and reports the query results to the users. The immediately lower level is the regional agent. In such architecture, several regional agents may coexist, and each one is in charge of a region within the sensor field. Regional agents receive query requests from the interface agent and control sensor nodes within its region to collaboratively respond to the requests. A region is further split to sub-regions (clusters) that are coordinated by manager agents. Manager agents directly control the behavior of these sensor nodes which are modeled as observing agents (OA). Effectively a manager agent and its OAs play the dominant role in WSN collaborative processing. This is due to the fact that a target or an event to be dealt with must belong to one cluster organized by a manager agent. This is a miniature multi-agent system relative to the whole system.


Agent Collaborative Target Localization and Classification in Wireless Sensor Networks
Heterogeneous agent architecture for WSN.
© Copyright Policy
Related In: Results  -  Collection

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

f3-sensors-07-01359: Heterogeneous agent architecture for WSN.
Mentions: The proposed heterogeneous architecture framework is shown in Figure 3. From the figure, it is obvious that the architecture is a heterogeneous one, for both multi-agent systems and mobile agents are incorporated. The multi-agent system is a hierarchical one. The top is the interface agent, which receives user query about the environment, inquires the lower level agents accordingly and reports the query results to the users. The immediately lower level is the regional agent. In such architecture, several regional agents may coexist, and each one is in charge of a region within the sensor field. Regional agents receive query requests from the interface agent and control sensor nodes within its region to collaboratively respond to the requests. A region is further split to sub-regions (clusters) that are coordinated by manager agents. Manager agents directly control the behavior of these sensor nodes which are modeled as observing agents (OA). Effectively a manager agent and its OAs play the dominant role in WSN collaborative processing. This is due to the fact that a target or an event to be dealt with must belong to one cluster organized by a manager agent. This is a miniature multi-agent system relative to the whole system.

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.