An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network

Liu, Jing; Huang, Kaiyu; Zhang, Guoxian

doi:10.3390/s17040907

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Thus, CS is used for fMRI image sampling and reconstruction (Chiew et al 2018;, ultrasound images (Kruizinga 2017;Kim et al 2020a), remote sensing images (Zhao et al 2020a;Wang 2017), and other image-related domains. WSN data sub-sampling and recovery represents another significant area for CS (Xiao et al 2019;Liu et al 2017;Qie et al 2020) as does speech compression and reconstruction (Shawky 2017;Al-Azawi and Gaze 2017).…”

Section: From Samples To Inferencesmentioning

confidence: 99%

Deep learning for compressive sensing: a ubiquitous systems perspective

Machidon

Pejović

2022

Artif Intell Rev

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Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor sampling rate, potentially bringing context-awareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning from the compressed samples. While the CS–DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has any light been shed on practical issues towards bringing the CS–DL to real world implementations in the ubiquitous computing domain. In this paper we identify main possible ways in which CS and DL can interplay, extract key ideas for making CS–DL efficient, outline major trends in the CS–DL research space, and derive guidelines for the future evolution of CS–DL within the ubiquitous computing domain.

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Section: From Samples To Inferencesmentioning

confidence: 99%

Deep learning for compressive sensing: a ubiquitous systems perspective

Machidon

Pejović

2022

Artif Intell Rev

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show abstract

“…Thus, CS is used for fMRI image sampling and reconstruction [24,78], ultrasound images [66,68,86], remote sensing images [140,163], and other image-related domains. WSN data sub-sampling and recovery represents another significant area for CS [80,115,144] as does speech compression and reconstruction [5,122].…”

Section: 13mentioning

confidence: 99%

Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective

Machidon,

Pejovic

2021

Preprint

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Compressive sensing (CS) is a mathematically elegant tool for reducing the sampling rate, potentially bringing contextawareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning form the compressed samples. While the CS-DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has the light been shed on practical issues towards bringing the CS-DL to real world implementations in the ubicomp domain. In this paper we identify main possible ways in which CS and DL can interplay, extract key ideas for making CS-DL efficient, identify major trends in CS-DL research space, and derive guidelines for future evolution of CS-DL within the ubicomp domain. CCS Concepts: • Computer systems organization → Sensor networks; • Human-centered computing → Ubiquitous computing; • Computing methodologies → Neural networks.

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“…I NTERNET of Things (IoT) [1]- [4] is used in military affairs, environmental monitoring, agriculture production and many other fields [5], [6]. It contains a large number of distributed nodes (sensor nodes and sink nodes) with sensing, and computing and communication capabilities [7], [8]. In IoT, sensor nodes transmit the perceived temperature, humidity, and other environmental information to sink nodes through wireless communication [9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Shortcut Addition Algorithm With Particle Swarm for Multisink Internet of Things

Qiu

Zhou

et al. 2020

IEEE Trans. Ind. Inf.

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The Internet of Things (IoT) integrates a large number of distributed nodes to collect or transmit data. When the network scale increases, individuals use multiple sink nodes to construct the network. This increases the complexity of the network and leads to significant challenges in terms of the existing methods with respect to the aspect of data forwarding and collection. In order to address the issue, this paper proposes a Shortcut Addition strategy based on the Particle Swarm algorithm for multi-sink network (SAPS). It constructs network topology with multiple sinks based on a small-world network.In SAPS, we create a fitness function by combining the average path length and load of the sink node to evaluate the quality of a particle. Subsequently, crossover and mutation are used to update particles to determine the optimal solution. The simulation results indicate that SAPS is superior to the GMSW and LM-GAS in terms of the average path length, load balance, and number of added shortcuts.

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An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network

Cited by 8 publications

References 27 publications

Deep learning for compressive sensing: a ubiquitous systems perspective

Deep learning for compressive sensing: a ubiquitous systems perspective

Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective

A Novel Shortcut Addition Algorithm With Particle Swarm for Multisink Internet of Things

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