Energy-efficient data gathering algorithm relying on compressive sensing in lossy WSNs. (December 2019)

• Record Type: Journal Article
• Authors: Zhang, Ce; Li, Ou; Yang, Yanping; Liu, Guangyi; Tong, Xin
• Is Part Of: Measurement. Volume 147(2019)
• Publication Date: 2019-12
• Subjects: Wireless sensor networks -- Compressed sensing -- Data gathering -- Unreliable link; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.106875
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The network is divided into several clusters. Both the inter-cluster and intra-cluster correlation is utilized. The measurement matrix is formulated based on the gathered data. The proposed scheme maintains reconstruction accuracy under unreliable wireless links. Network life is prolonged effectively. Abstract: Packet loss is one of the most critical factors affecting the accuracy of compressed sensing (CS)-based data gathering algorithms. In this paper, a data gathering algorithm is proposed to decrease energy consumption and resist packet loss. Each cluster head formulates a sparsest random measurement matrix (SRMM) via the received data to avoid the measurement of the lost node and decrease the number of measurements. To employ spatial correlation between clusters, the sink constructs a block diagonal matrix (BDM) as a measurement matrix via SRMMs and reconstructs the entire network data. Additionally, the optimal number of clusters is discussed under this framework to reach the minimum power consumption. The SR-BDM is evaluated on the emulated data and the real sensor data from GreenOrbs, respectively. The simulation results indicate the proposed algorithm reaches high precision, both with reliable links and with a 60% packet loss rate link, without causing increased energy consumption.