title: An adaptive data collection algorithm based on a Bayesian compressed sensing framework.
abstract: For Wireless Sensor Networks, energy efficiency is always a key consideration in system design. Compressed sensing is a new theory which has promising prospects in WSNs. However, how to construct a sparse projection matrix is a problem. In this paper, based on a Bayesian compressed sensing framework, a new adaptive algorithm which can integrate routing and data collection is proposed. By introducing new target node selection metrics, embedding the routing structure and maximizing the differential entropy for each collection round, an adaptive projection vector is constructed. Simulations show that compared to reference algorithms, the proposed algorithm can decrease computation complexity and improve energy efficiency.
authors.affiliation: College of Information Engineering, North China University of Technology, Beijing 100144, China. lzliu@ncut.edu.cn.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Liu
authors.affiliation: College of Information Engineering, North China University of Technology, Beijing 100144, China. zmm@ncut.edu.cn.
authors.forename: Mengmeng
authors.initials: M
authors.lastname: Zhang
authors.affiliation: College of Information Engineering, North China University of Technology, Beijing 100144, China. landrew@126.com.
authors.forename: Jian
authors.initials: J
authors.lastname: Cui