Semi-tensor CS for distributed channel estimation in WSNs

Bao, Donghai; Yang, Fang; Wang, Yinping; Jiang, Qianru; Bai, Huang; Li, Sheng

doi:10.1109/spawc.2017.8227672

Cited by 2 publications

(3 citation statements)

References 24 publications

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“…To form the buffer Ũ jB (t), first we note that there are six common elements between consecutive temporal signals. To extract this commonality, we rewrite (26) in the following form: (see (27)) . Then extract its last six elements to get to Ũ jB (t).…”

Section: First-loop Buffer Term [ũ Jb (T)]mentioning

confidence: 99%

“…We have proposed an algorithm to increase the accuracy of reconstruction and decrease the energy consumption. Storage and computational complexity limitations are addressed in [27]. Authors in [27] have proposed a Distributed Channel Estimation scheme based on CS and semi‐tensor product (CS‐DCE‐STP) to relieve every sensor's storage space and reduce its computational complexity.…”

Section: Survey For Energy Consumption Reduction In Wsnsmentioning

confidence: 99%

“…Storage and computational complexity limitations are addressed in [27]. Authors in [27] have proposed a Distributed Channel Estimation scheme based on CS and semi‐tensor product (CS‐DCE‐STP) to relieve every sensor's storage space and reduce its computational complexity. The contribution of [27] is reducing the space occupied by sensing matrix and computing consumption of every node by employing STP‐CS for DCE in WSNs.…”

Section: Survey For Energy Consumption Reduction In Wsnsmentioning

confidence: 99%

See 2 more Smart Citations

Energy‐efficient data reconstruction algorithm for spatially‐ and temporally correlated data in wireless sensor networks

Adnani

Tinati

Azarnia

et al. 2018

IET signal process.

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This study introduces a novel algorithm for reconstruction of wireless sensor networks data which inherently have spatial and temporal correlations. The authors' algorithm is based on compressed sensing (CS) and benefits from sliding window processing. This new algorithm rearranges the data in form of a cube and uses this representation to extract more information about the data. There are two optimisation loops which are solved simultaneously and periodically reconstruct one part of the whole signal from measurements that arrive at the sink. In particular, the first reconstruction loop, which uses a modified version of basis pursuit reconstruction algorithm, is meant for reconstruction of a temporal data which is extracted from the data cube, and the second loop which uses a modified version of reweighted l 1-norm algorithm is for reconstruction of data windows. The authors used a special kind of binary sparse random measurement matrices for sampling which is equipped with a condition to get samples as variously as possible and this, in turn, balances the duty among sensors and provides more information from the field. Simulation results verify that the proposed algorithm achieves better reconstruction accuracy and less energy consumption in comparison with state-of-the-art CS reconstruction methods.

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Section: First-loop Buffer Term [ũ Jb (T)]mentioning

confidence: 99%

Section: Survey For Energy Consumption Reduction In Wsnsmentioning

confidence: 99%

Section: Survey For Energy Consumption Reduction In Wsnsmentioning

confidence: 99%

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Energy‐efficient data reconstruction algorithm for spatially‐ and temporally correlated data in wireless sensor networks

Adnani

Tinati

Azarnia

et al. 2018

IET signal process.

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Signal Reconstruction Based on Time‐Varying Sliding Window in WSNs Using Compressed Sensing

Zeynali,

Ali Tinati

2024

Int J Communication

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This paper presents a new algorithm that utilizes compressed sensing (CS) for reconstruction of wireless sensor networks (WSNs) data with spatial and temporal correlation. The proposed method utilizes a time‐varying sliding window mechanism that dynamically adjusts both the window size and the number of measurements. This flexibility allows the algorithm to exploit spatio‐temporal correlations effectively, ensuring that data within the window remains sparse and thus more compressible. By dynamically varying the number of measurements, the algorithm equitably distributes the sampling rate across different time slots, adapting to changes in signal characteristics and minimizing transmission costs. Simulation results demonstrate that our proposed algorithm outperforms other CS reconstruction methods by achieving higher reconstruction precision while requiring fewer transmissions. This is achieved through a decentralized data‐window framework that maximizes the use of prior signal information, leading to improved signal recovery performance in diverse WSN scenarios.

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Semi-tensor CS for distributed channel estimation in WSNs

Cited by 2 publications

References 24 publications

Energy‐efficient data reconstruction algorithm for spatially‐ and temporally correlated data in wireless sensor networks

Energy‐efficient data reconstruction algorithm for spatially‐ and temporally correlated data in wireless sensor networks

Signal Reconstruction Based on Time‐Varying Sliding Window in WSNs Using Compressed Sensing

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