Spatiotemporal Data Gathering Based on Compressive Sensing in WSNs

Zhang, Ce; Ou, Li; Tong, Xin; Ke, Ke; Li, Mingxuan

doi:10.1109/lwc.2019.2912883

Cited by 15 publications

(4 citation statements)

References 18 publications

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“…Wireless Communications and Mobile Computing was adopted to choose the sensory data in the temporal domain. Considering both spatial and temporal correlation of sensing data can enhance accuracy of recovery data and reduce required CS measurement M. So Reference [34] combined Kronecker compressed sensing (KCS) and cluster topology to exploit spatial and temporal correlations simultaneously. The cluster topology can exploit spatial correlation of sensing data.…”

Section: Cut Down Network Data Transmissions and Promote Energy Efficiency By Reducing Participated Nodes In Cdgmentioning

confidence: 99%

“…So it is a 2-dimensional compressive sensing problem and can be solved by KCS. Besides, the BDM in [29] was used in [34] to take advantage of the spatial correlation among clusters.…”

Section: Cut Down Network Data Transmissions and Promote Energy Efficiency By Reducing Participated Nodes In Cdgmentioning

confidence: 99%

“…The collected data from nodes visited by the mobile sink is temporal CS measurements. So the proposed method also exploits spatial-temporal correlation of collected data that is similar with [34]. Reference [37] adopted the standard random walk strategy and the kernel method to develop a sparse representation basis.…”

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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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: Cut Down Network Data Transmissions and Promote Energy Efficiency By Reducing Participated Nodes In Cdgmentioning

confidence: 99%

“…So it is a 2-dimensional compressive sensing problem and can be solved by KCS. Besides, the BDM in [29] was used in [34] to take advantage of the spatial correlation among clusters.…”

Section: Cut Down Network Data Transmissions and Promote Energy Efficiency By Reducing Participated Nodes In Cdgmentioning

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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“…matrix optimization technique to reduce the data redundancy resulted from the Spatial-temporal correlation, such as the Spatio-Temporal Hierarchical Data Aggregation using Compressive Sensing (ST-HDACS) [241] , the Spatial-Temporal Compressive Data Gathering algorithm (ST-CDGA) [242] , and the Spatio-Temporal Kronecker Compressive Sensing method (STKCS) [243] . In addition, the Dispersion Wavelet Transform Matrix (DWTM) is applied in the Measurement Matrix Optimization Algorithm (MMOA) to achieve Spatial-temporal Compressive Sensing [244] .…”

Section: Acm T S Nmentioning

confidence: 99%

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

Lin

Wang

Min

et al. 2020

ACM Trans. Sen. Netw.

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A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) which are not equipped with any energy harvesting modules was conducted in this paper. Firstly, a novel generic mathematical definition of Energy Efficiency was proposed, which took the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, it is for the first time that the Energy Efficiency of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network were expounded at length. Accordingly, the concepts concerning the Energy Efficiency, namely the Energy-efficient Means, the Energy-efficient Tier, and the Energy-efficient Perspective, were proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 were tracked and reviewed. Specifically, they were classified into five categories, i.e., the Energy-Efficient Media Access Control (EEMAC) protocol, the Mobile Node Assistance Scheme (MNAS), the Energy-Efficient Clustering Scheme (EECS), the Energy-Efficient Routing Scheme (EERS), and the Compress Sensing based Scheme (CSS) respectively. A detailed elaboration on both the basic principle and the evolution of them was made. Finally, further analysis on the categories was made and the related conclusion was drawn. To be specific, the interdependence among them, the relationships between each of them and the Energy-efficient Means, the Energy-efficient Tier as well as and the Energy-efficient Perspective were analyzed in detail. Besides, the specific applicable scenarios for each of them and the relevant statistics analysis were detailed. The proportion and the number of citations of each category were illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm, and the feasible direction concerning the energy efficiency in the future were pointed out.

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Correlation-based wireless sensor networks performance: the compressed sensing paradigm

Xifilidis

Psannis

2021

Cluster Comput

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Spatiotemporal Data Gathering Based on Compressive Sensing in WSNs

Cited by 15 publications

References 18 publications

A Survey of Compressive Data Gathering in WSNs for IoTs

A Survey of Compressive Data Gathering in WSNs for IoTs

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

Correlation-based wireless sensor networks performance: the compressed sensing paradigm

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