Multiple-target tracking based on compressed sensing in the Internet of Things. (15th November 2018)

• Record Type: Journal Article
• Authors: Lu, Xu; Liu, Jun; Qi, Wei; Dai, Qingyun
• Is Part Of: Journal of network and computer applications. Volume 122(2018)
• Page Start: 16
• Page End: 23
• Publication Date: 2018-11-15
• Subjects: Internet of Things -- Target tracking -- Compressed sensing -- Sampling subtraction; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2018.08.006
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: An algorithm based on compressed sensing for tracking multiple targets in the Internet of Things (IoT) is proposed in this study. First, we build a sparse representation of the changes in the sampling signal caused by nodes to multiple targets in a monitored area. Second, we observe the sampling of the sensing signals by nodes to mobile targets and reconstruct the sampling subtraction data. Third, we use sampling subtraction, which is extended by a background subtraction technique in video target tracking, to obtain useful tracking data with sampling subtraction data and to locate the mobile targets. Simulation results show that the proposed algorithm recovers the sensing signal with sparse sampling subtraction data, accurately locates multiple targets, significantly reduces network communication traffic, and improves the energy efficiency of the system with the sparse sampling strategy.