Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering

Luo, Chong; Wu, Feng; Sun, Jun; Chen, Chang Wen

doi:10.1109/twc.2010.092810.100063

Cited by 190 publications

(190 citation statements)

References 33 publications

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“…Luo et al [21] present a compressive data-gathering (CDG) method, which combines data compression and routing for data collection in large-scale wireless sensor networks. The result of this combination is a balanced distribution of energy drain over the network, which improves network lifetime.…”

Section: Related Workmentioning

confidence: 99%

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

Masoum

Meratnia

Havinga

2018

Preprint

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Abstract:Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we propose an energy efficient distributed compressive sensing solution for sensor networks. Proposed solution utilizes sparsity distribution of signals to group sensor nodes into several coalitions and then implements localized compressive sensing inside coalitions. This solution improves data-gathering performance in terms of both data accuracy and energy consumption. The approach curbs both data-transmission costs and number of measurements. Coalition-based data gathering cuts transmission cost, and the number of measurements is reduced by scheduling sensor nodes and adjusting their sampling frequency. Our simulation showed that our approach enhances network performance by minimizing energy cost and improving data accuracy.

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Section: Related Workmentioning

confidence: 99%

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

Masoum

Meratnia

Havinga

2018

Preprint

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“…In [8,9,10] random walks with CS provide distributed routing methods for WSNs. Cluster based [11,12] and tree based [13,14] data collection methods significantly show the power reduced based on the combination with CS.…”

Section: Related Workmentioning

confidence: 99%

“…We assume a square sensing area with dimensions H × H. From Equation (13), the average number of sensors deployed in the area covered by each sensor…”

Section: Square Sensing Areamentioning

confidence: 99%

Energy-Efficient Mobile Sensing in Distributed Multi-Agent Sensor Networks

Nguyen¹

2017

Adv. sci. technol. eng. syst. j.

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“…The proposed scheme was suitable for longterm deployment of large underwater networks in which energy and bandwidth minimization is crucial. The authors of [11] investigated how to generate restricted isometric property while preserving measurements of sensor readings by accounting for multihop communication cost. Reference [13] minimized the number of information sensors by applying CS principles.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Distributed Compressed Sensing Data Aggregation for Cluster-Based Underwater Acoustic Sensor Networks

Wang

et al. 2016

International Journal of Distributed Sensor Networks

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Energy-efficient data aggregation is important for underwater acoustic sensor networks due to its energy constrained character. In this paper, we propose a kind of energy-efficient data aggregation scheme to reduce communication cost and to prolong network lifetime based on distributed compressed sensing theory. First, we introduce a distributed compressed sensing model for a clusterbased underwater acoustic sensor network in which spatial and temporal correlations are both considered. Second, two schemes, namely, BUTM-DCS (block upper triangular matrix DCS) and BDM-DCS (block diagonal matrix DCS), are proposed based on the design of observation matrix with strictly restricted isometric property. Both schemes take multihop underwater acoustic communication cost into account. Finally, a distributed compressed sensing reconstruction algorithm, DCS-SOMP (Simultaneous Orthogonal Matching Pursuit for DCS), is adopted to recover raw sensor readings at the fusion center. We performed simulation experiments on both the synthesized and real sensor readings. The results demonstrate that the new data aggregation schemes can reduce energy cost by more than 95 percent compared with conventional data aggregation schemes when the cluster number is 20.

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Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering

Cited by 190 publications

References 33 publications

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

Energy-Efficient Mobile Sensing in Distributed Multi-Agent Sensor Networks

Energy-Efficient Distributed Compressed Sensing Data Aggregation for Cluster-Based Underwater Acoustic Sensor Networks

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