Compressed Data Collection Method for Wireless Sensor Networks Based on Optimized Dictionary Updating Learning

Chen, Junying; Zhou, Fuqiang; Guo, Zhiling; Wan, Jian

doi:10.1109/access.2020.3037123

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…The parameters in CDG can be modified with environment change. Reference [48] used the dictionary learning method to obtain a sparse basis in CDG. Most of previous CDG methods always adopt fixed sparse bases which sometimes are not suitable for diverse scenarios in WSNs.…”

Section: Cdg Combined With Other Technologiesmentioning

confidence: 99%

“…As a result, the learned sparse basis has low selfcoherence structure, which can effectively suppress the impact of environmental noise. However, the training data in [48] is uncompressed historical data that needs a large number of communication resources and lacks of dynamic adaptability in time. So Reference [49] presented a training method to learn the sparse basis from CS measurements.…”

Section: Cdg Combined With Other Technologiesmentioning

confidence: 99%

“…So Reference [49] presented a training method to learn the sparse basis from CS measurements. In this way, the sparse basis can be updated in time and does not require extra data transmissions compared to [48]. Reference [50] got an adaptive dynamic random seed by a seed estimation algorithm.…”

Section: Cdg Combined With Other Technologiesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Compressive Data Gathering in WSNs for IoTs

Wang

Chen

2022

Wireless Communications and Mobile Computing

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Internet of Things (IoTs) are increasingly widespread in the field of health care, smart city and smart home application, industrial and agricultural monitoring, automation, etc. With its growing scale of networks, there are a large amount of data in IoTs needing to be sensed, transmitted, and processed. Resource-limited Wireless Sensor Networks (WSNs) as a perceptual layer of IoTs are hard to handle massive uncompressed sensing data. Compressive data gathering (CDG), which applies compressive sensing theory to data gathering, is a perfectly matching method for data compressing and gathering in WSNs. This promising method has attracted lots of researchers’ attention. In this paper, we attempt to survey substantial references about CDG in WSNs. According to their technology schemes, we classify the published references into three categories, i.e., routing protocol of CDG, clustering scheme of CDG, and CDG combined with other technologies. The merits and defects of each method are highlighted. Our work aims to provide an insight into CDG and promote improvements of this technology.

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Section: Cdg Combined With Other Technologiesmentioning

confidence: 99%

Section: Cdg Combined With Other Technologiesmentioning

confidence: 99%

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A Survey of Compressive Data Gathering in WSNs for IoTs

Wang

Chen

2022

Wireless Communications and Mobile Computing

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“…Various learning algorithms can make CDG schemes more intelligent and adaptive. References [28,29] used the dictionary learning method to obtain a sparse basis which has better sparse representation ability in CDG. References [30,31] incorporated deep learning into CDG and designed a deep compressed sensing network to build a measurement matrix and reconstruct data from CS measurements.…”

Section: Related Workmentioning

confidence: 99%

A Reinforcement Learning-Based Dynamic Clustering Algorithm for Compressive Data Gathering in Wireless Sensor Networks

Wang

Chen

2022

Mobile Information Systems

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Compressive data gathering (CDG) is an effective technique to handle large amounts of data transmissions in resource-constrained wireless sensor networks (WSNs). However, CDG with static clustering cannot adapt to time-varying environments in WSNs. In this paper, a reinforcement learning-based dynamic clustering algorithm (RLDCA) for CDG in WSNs is proposed. It is a dynamic and adaptive clustering method aiming to further reduce data transmissions and energy consumption in WSNs. Sensor nodes act as reinforcement learning (RL) agents which can observe the environment and dynamically select a cluster to join in. These RL agents are instructed by a well-designed reward scheme to join a cluster with strong data correlation and proper distance. It is also a distributed and lightweight learning method. All agents are independent and operate in parallel. Additional overheads introduced by RL are lightweight. Computations of a linear reward function and a few comparison operations are needed. It is implementable in WSNs. Simulations performed in MATLAB validate the effectiveness of the proposed method and simulation results show that the proposed algorithm achieves the desired effect as well as fine convergence. It decreases data transmissions by 16.6% and 54.4% and energy consumption by 6% and 29%, respectively, compared to the two contrastive schemes.

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“…Reference [6] focuses on business/data dictionary to improve data governance for industries and propose a method of applying these natural language processing approaches to the business/data dictionary. Reference [7] proposes an optimized data dictionary updating learning-based compressed data collection algorithm to improve data classification.…”

Section: Introductionmentioning

confidence: 99%

Industrial data fusion method based on semantic data dictionary for digital twin

Liu

Zhang²

2023

Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022)

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Industrial digital twin is the key support for the transformation of industry to intelligence. The realization of digital twin relies on the integration of whole-process data, which can provide real-time and intelligent decision-making optimization for production management. This paper proposes a digital twin data fusion method based on semantic data dictionary. Firstly, a modeling method of domain data dictionary is designed, and a semantic method is introduced to realize the construction of semantic data dictionary. Then, based on the semantic data dictionary, a semantic similarity calculation method based on multivariate distance weighting is proposed. Finally, the algorithm is tested and verified. The experimental results show that the method in this paper has a significant effect on automatic data fusion in the construction of industrial digital twins.

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Compressed Data Collection Method for Wireless Sensor Networks Based on Optimized Dictionary Updating Learning

Cited by 7 publications

References 49 publications

A Survey of Compressive Data Gathering in WSNs for IoTs

A Survey of Compressive Data Gathering in WSNs for IoTs

A Reinforcement Learning-Based Dynamic Clustering Algorithm for Compressive Data Gathering in Wireless Sensor Networks

Industrial data fusion method based on semantic data dictionary for digital twin

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