Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

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.

Acoustic Source Localization in Wireless Sensor Network

“…E is the expectation operator, and · denotes the vector 2-norm x = x 2 1 + x 2 2 . In [6,10,26] as well as in Appendix A.1, it is shown that the measured acoustic energyÊ i (actually estimated energy at the ith sensor node) can be modeled bŷ…”

“…The existing acoustic source localization techniques are typically based on three types of physical quantities: (1) time difference of arrival TDOA [3,4,18] or its variation, time of arrival (TOA) [13,17,27], (2) angle of arrival AOA (usually named as direction of arrival) [1,8,20,24,27], and (3) received signal energy [6,10,21,26]. Received signal energy is usually termed as received signal strength (RSS).…”

Pervasive surveillance-agent system based on wireless sensor networks: design and deployment

“…In the literature [6][7][8], several proposals to model the WSN as heterogeneous agent systems are presented.…”

Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks

“…In these applications, it is necessary for the nodes to know their own physical locations. In addition, location information provides essential network operations such as routing protocols, clustering, data centric storage and geographic key distribution [1][2][3].…”