A Chain-Based Data Gathering Protocol Under Compressive Sensing Framework for Wireless Sensor Networks

Xie, Dongfeng; Zhou, Qianwei; Liu, Jianpo; Li, Baoqing

doi:10.1109/iccis.2013.390

Cited by 4 publications

(2 citation statements)

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“…Besides, they proposed a novel Measurement Matrix Optimization Algorithm (MMOA) for the process of acquiring the compressed data. Except for the strategies above, other energy-efficient Compressive Sensing-based Clustering schemes have emerged recently, such as the ST-HDACS [36] , the chain-based data gathering protocol [37] , and the adaptive adjustment of compressed measurements [38] .…”

Section: Ntmentioning

confidence: 99%

“…In this paper, the first-order radio model [10][11] [31] was adopted to describe the energy consumption for a node's transmission. Namely, the energy for a sensor node to transmit a k-bits packet to another node over distance d equals [37]. The Sink was deployed at the center and each ring-shaped area was d in width, just as shown in Fig.1.…”

Section: System Model and Some Assumptions (1) Energy Consumption mentioning

confidence: 99%

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An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs

et al. 2019

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A novel algorithm which combined the merits of the Clustering strategy and the Compressive Sensing-based (CS-based) scheme was proposed in this paper. The lemmas for the relationship between any two adjacent layers, the optimal size of clusters, the optimal distribution of the Cluster Head (CH) and the corresponding proofs were presented firstly. In addition, to alleviate the "Hot Spot Problem" and reduce the energy consumption resulted from the rotation of the role of CHs, a third role of Backup Cluster Head (BCH) as well as the corresponding mechanism to rotate the roles between the CH and BCH were proposed. Subsequently, the Energy-Efficient Compressive Sensing-based clustering Routing (EECSR) protocol was presented in detail. Finally, extensive simulation experiments were conducted to evaluate its energy performance. Comparisons with the existing clustering algorithms and the CS-based algorithm verified the effect of EECSR on improving the energy efficiency and extending the lifespan of WSNs.

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Section: Ntmentioning

confidence: 99%

Section: System Model and Some Assumptions (1) Energy Consumption mentioning

confidence: 99%

An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs

et al. 2019

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RETRACTED: Real-Time monitoring of Athletes training data based on wireless sensors

Deng

2021

Microprocessors and Microsystems

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An Energy-Saving Routing Integrated Economic Theory With Compressive Sensing to Extend the Lifespan of WSNs

Lin

Min

2020

IEEE Internet Things J.

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A Chain-Based Data Gathering Protocol Under Compressive Sensing Framework for Wireless Sensor Networks

Cited by 4 publications

References 5 publications

An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs

An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs

RETRACTED: Real-Time monitoring of Athletes training data based on wireless sensors

An Energy-Saving Routing Integrated Economic Theory With Compressive Sensing to Extend the Lifespan of WSNs

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