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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.


Vehicle localization by collaboration between s4, s5, s6 and s7.
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f13-sensors-07-01359: Vehicle localization by collaboration between s4, s5, s6 and s7.

Mentions: As pointed out in the formulation of the agent collaborative algorithm, the selection of agents involved in the objective function (20) is actually by intuition. To compare with the intuitive approach an experiment using the 4 agents giving the lowest average energy is conducted. The result is shown in Figure 13. As shown in the figure, s4, s5, s6 and s7 (reporting the lowest average energy) are chosen for collaboration and the search is started from s5 (reporting the highest average energy among the 4 agents). It takes 11 steps to converge to the estimated location (-3.07, 1.91) which is 1.43m away from the actual position. Again no relaxation of termination condition is deed.


Agent Collaborative Target Localization and Classification in Wireless Sensor Networks
Vehicle localization by collaboration between s4, s5, s6 and s7.
© Copyright Policy
Related In: Results  -  Collection

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

f13-sensors-07-01359: Vehicle localization by collaboration between s4, s5, s6 and s7.
Mentions: As pointed out in the formulation of the agent collaborative algorithm, the selection of agents involved in the objective function (20) is actually by intuition. To compare with the intuitive approach an experiment using the 4 agents giving the lowest average energy is conducted. The result is shown in Figure 13. As shown in the figure, s4, s5, s6 and s7 (reporting the lowest average energy) are chosen for collaboration and the search is started from s5 (reporting the highest average energy among the 4 agents). It takes 11 steps to converge to the estimated location (-3.07, 1.91) which is 1.43m away from the actual position. Again no relaxation of termination condition is deed.

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.