Abstract
Energy-consumption of the wireless sensor networks(WSNs) is proportional to the sampling rate. The need for more energy-efficient methods for WSNs data gathering is greater demand since most of the energy is consumed in sampling and transmission. Recently, compressive sensing (CS) and matrix completion (MC) have been earning increasing interests in the area of WSNs. Both of CS and MC exploit the sparsity presents in sensing environment to reduce sampling rate needed for data acquisition and processing. In this paper, a new scheme based on MC is proposed to reduce such sampling rate. The new scheme is evaluated in a real-data domain and its performance is compared with CS techniques in unified framework. The results show the superiority of MC over the CS techniques in the accuracy of data recovery with different sampling rate.
The original version of this chapter was revised: Two of the authors names were excluded in the chapter. The erratum to this chapter is available at DOI 10.1007/978-3-319-26690-9_47
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-26690-9_47
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Maged, M.A., Akah, H.M. (2016). Compressive Data Recovery in Wireless Sensor Networks—A Matrix Completion Approach. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_32
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