Title: Data aggregation and recovery in wireless sensor networks using compressed sensing

Authors: Guangming Cao; Peter Jung; Sławomir Stańczak; Fengqi Yu

Addresses: Department of Integrated Electronics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen 518055, China ' TU Berlin Einsteinufer 25, 10587 Berlin, Germany ' TU Berlin Einsteinufer 25, 10587 Berlin, Germany ' Department of Integrated Electronics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Abstract: QoS support for data aggregation in large-scale multi-hop wireless sensor networks (WSNs) inevitably faces two crucial issues: packet loss and energy dissipation. Fortunately, most sensing data is spatially and temporally correlated and compressible. Therefore, compressed sensing (CS) is a promising reconstruction scheme having the potential of packet error correction with low-energy consumption. In this paper we present such a CS-oriented data aggregation technique for the multi-hop topology. Our scheme is balanced in energy consumption among the nodes and recovers lost packets at fusion centre without additional transmitting costs. Simulations show that our approach works well even for 50% data loss rate when environmental data is sparse in a certain domain. Comparing with the existing methods, our method achieves higher recovery accuracy and less energy consumption on TinyOS. Furthermore, the system is demonstrated in the experiment of monitoring grid computer facilities set up at Shenzhen Institutes of Advanced Technology.

Keywords: large-scale WSNs; wireless sensor networks; compressed sensing; packet loss; energy balance; data aggregation; data recovery; energy consumption; simulation; grid computing; quality of service; QoS.

DOI: 10.1504/IJSNET.2016.080370

International Journal of Sensor Networks, 2016 Vol.22 No.4, pp.209 - 219

Available online: 18 Nov 2016 *

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