Title :
Distributed spatial-temporal compressive data gathering for large-scale WSNs
Author :
Xuangou Wu ; Yan Xiong ; Mingxi Li ; Wenchao Huang
Author_Institution :
Sch. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China
Abstract :
Wireless sensor networks (WSNs) are widely used for monitoring physical phenomena of interest, but one of the major challenges for designing sensor networks is to minimize transmission cost with obtaining fidelity information in the sink. Distributed compressive sensing (CS) is a promising in-network data compression technique to reduce data transmission cost and accurately recover sensory data in the sink. In this paper, we propose a distributed spatial-temporal compressive data gathering scheme for large-scale WSNs to improve the recovery quality of sensory data and prolong the sensor network´s lifetime as well. In our scheme, we first present a sensory data partition model to improve the compressibility of gathering data. Sparse and dense random projections are used for compressing and gathering different components of our sensory data partition model to obtain the same projection process. We also exploit cluster-based routing strategy to gather CS measurement and reduce energy consumption. Finally, experiment results show that our scheme not only improves the sensory data recovery quality compared with dense random projections data gathering scheme, but also significantly prolongs the sensor network´s lifetime compared with tree-type CS based data gathering schemes.
Keywords :
compressed sensing; data compression; telecommunication network reliability; telecommunication network routing; wireless sensor networks; CS measurement; cluster-based routing strategy; data transmission cost minimization; dense random projection; distributed CS; distributed compressive sensing; distributed spatial-temporal compressive data gathering scheme; energy consumption reduction; fidelity information; gathering data compressibility; innetwork data compression technique; large-scale WSN; sensor network lifetime; sensory data partition model; sensory data recovery quality; sparse random projection; tree-type CS-based data gathering scheme; wireless sensor networks; Atmospheric measurements; Data models; Distributed databases; Particle measurements; Routing; Sparse matrices; Wireless sensor networks; Wireless sensor networks; compressive sensing; data gathering; random projections; spatial-temporal correlation; transmission cost;
Conference_Titel :
Computing, Communications and IT Applications Conference (ComComAp), 2013
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4673-6043-2
DOI :
10.1109/ComComAp.2013.6533618