Evaluation of memory-efficient 1-bit compressed sensing in wireless sensor networks

Sakdejayont, Theerat; Lee, Doohwan; Peng, Yuanyuan; Yamashita, Yu; Morikawa, Hiroyuki

doi:10.1109/r10-htc.2013.6669064

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Recently, integration of CS into the design of wireless sensor networks (WSNs) has received considerable attention [8][9]. The 1-bit CS is particularly attractive in this scenario, due to its capability of reducing the communication and computational costs of local sensors [10][11]. In practical WSNs, communication link errors caused by channel fading and noise are unavoidable.…”

Section: Introductionmentioning

confidence: 99%

Amplitude-Aided 1-Bit Compressive Sensing Over Noisy Wireless Sensor Networks

Chen

2015

IEEE Wireless Commun. Lett.

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Abstract-One-bit compressive sensing (CS) is known to be particularly suited for resource-constrained wireless sensor networks (WSNs). In this paper, we consider 1-bit CS over noisy WSNs subject to channel-induced bit flipping errors, and propose an amplitude-aided signal reconstruction scheme, by which (i) the representation points of local binary quantizers are designed to minimize the loss of data fidelity caused by local sensing noise, quantization, and bit sign flipping, and (ii) the fusion center adopts the conventional 1 − minimization method for sparse signal recovery using the decoded and de-mapped binary data. The representation points of binary quantizers are designed by minimizing the mean square error (MSE) of the net data mismatch, taking into account the distributions of the nonzero signal entries, local sensing noise, quantization error, and bit flipping; a simple closed-form solution is then obtained. Numerical simulations show that our method improves the estimation accuracy when SNR is low or the number of sensors is small, as compared to state-of-the-art 1-bit CS algorithms relying solely on the sign message for signal recovery.

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