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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.


Agent collaborative localization of randomly positioned vehicles in the sensor field.
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f14-sensors-07-01359: Agent collaborative localization of randomly positioned vehicles in the sensor field.

Mentions: Note that the proposed algorithm needs 10 steps and the estimated position is 1.05m from the true location. By comparison, localization using the 4 agents with the highest average energy requires less search steps and provides better localization accuracy. Therefore the proposed search method is time efficient and accurate. Further evaluation of the proposed search method (i.e. searching with the agents reporting highest average energy) is carried out by localizing vehicles randomly positioned within the sensor field as shown in Figure 14.


Agent Collaborative Target Localization and Classification in Wireless Sensor Networks
Agent collaborative localization of randomly positioned vehicles in the sensor field.
© Copyright Policy
Related In: Results  -  Collection

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

f14-sensors-07-01359: Agent collaborative localization of randomly positioned vehicles in the sensor field.
Mentions: Note that the proposed algorithm needs 10 steps and the estimated position is 1.05m from the true location. By comparison, localization using the 4 agents with the highest average energy requires less search steps and provides better localization accuracy. Therefore the proposed search method is time efficient and accurate. Further evaluation of the proposed search method (i.e. searching with the agents reporting highest average energy) is carried out by localizing vehicles randomly positioned within the sensor field as shown in Figure 14.

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.