Work

An Energy-Efficient Collaborative Target Tracking Framework in Distributed Wireless Sensor Networks

Year: 2014

Type: article

Abstract: Energy consumption and tracking accuracy are two significant issues for collaborative tracking in distributed wireless sensor networks (DWSNs). To obtain a benefit from those issues, most of the recent work tends to reduce the spatial redundancy, while ignoring utilizing the attribute of time redundancy. In this paper, a novel energy-efficient framework of collaborative signal and information fusion is proposed for acoustic target tracking. The proposed fusion algorithm is based on neural network aggregation model and Gaussian particle filtering (GPF) estimation. And the neural network based aggregation (NNBA) can reduce spatial and time redundancy. Furthermore, a fresh cluster head (CH) selection method demanding less task handover is also presented to decrease energy consumption. The analyzed framework coupled with simulations demonstrates its excellent performance in tracking accuracy and energy consumption.

Source: International Journal of Distributed Sensor Networks

DOI: 10.1155/2014/396109

Authors: Lin Shang, Kang Zhao, Zhengguo Cai, Dan Gao, Maolin Hu

Institutions: Chinese Academy of Sciences, Shanghai Institute of Microsystem and Information Technology

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,680

Funder: Chinese Academy of Sciences