Multivariated Bayesian Compressive Sensing in Wireless Sensor Networks

Hwang, Seung-Hoon; Ran, Rong; Yang, Janghoon; Kim, Dong Ku

doi:10.1109/jsen.2015.2508670

Cited by 32 publications

(8 citation statements)

References 21 publications

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“…The approaches used in the comparison are Bayesian compressive sensing (BCS) [12], clustered spatio-temporal Bayesian compressive sensing (STBCS) [13], Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 14 May 2018 doi:10.20944/preprints201805.0192.v1 temporal belief-propagation based compressive sensing (TBCS) [14], OMP based compressive sensing [15] and spatial Bayesian compressive sensing (SBCS) [16].…”

Section: Comparing With Other Compressive Sensing Based Methodsmentioning

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: Comparing With Other Compressive Sensing Based Methodsmentioning

confidence: 99%

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

Masoum

Meratnia

Havinga

2018

Preprint

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“…In this section, we compare the proposed spatial-temporal compressive sensing approach with the existing data compression techniques. The approaches used in the comparison are Bayesian compressive sensing (BCS) [ 46 ], clustered spatio-temporal Bayesian compressive sensing (STBCS) [ 47 ], temporal belief-propagation based compressive sensing (TBCS) [ 6 ], orthogonal matching pursuit (OMP) based compressive sensing [ 48 ] and spatial Bayesian compressive sensing (SBCS) [ 49 ].…”

Section: Performance Evaluationmentioning

confidence: 99%

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

Masoum

Meratnia

Havinga

2018

Sensors

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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. The 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 costs, 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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“…CS could also be utilized for sensor selection; that is, only part of nodes will be chosen to transmit the product of its data with a random coefficient to the sink [ 17 , 18 , 19 ]. In the context that all nodes transmitted their data directly to the sink node in one hop, Hwang et al [ 20 ] propose both centralized and decentralized algorithms for sensor selection under multi-variated noise condition. In [ 21 ], a tree-based algorithm named CDG is designed to reduce the payload falling on nodes close to the BS.…”

Section: Introductionmentioning

confidence: 99%

Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks

Chen

Xiong

Chu

2020

Sensors

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Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks based on Bayesian compressive sensing and particle swarm optimization. The algorithm we proposed is a two-tier PSO approach. In the first tier, a PSO-based clustering protocol is proposed to synthetically consider the energy consumption and uniformity of cluster head distribution. Then in the second tier, a PSO-based routing protocol is proposed to implement inner-cluster one-hop routing and outer-cluster multi-hop routing. The nodes selected to constitute i-th effective routing path decide which positions in the i-th row of the measurement matrix are nonzero. As a result, in this tier the protocol comprehensively considers energy efficiency, network balance and data recovery quality. The Bayesian Cramér-Rao Bound (BCRB) in such a case is analyzed and added in the fitness function to monitor the mean square error of the reconstructed signal. The experimental results validate that our algorithm maintains a longer life time and postpones the appearance of the first dead node while keeps the reconstruction error lower compared with the cutting-edge algorithms which are also based on distributed multi-hop compressive sensing approaches.

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Multivariated Bayesian Compressive Sensing in Wireless Sensor Networks

Cited by 32 publications

References 21 publications

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks

Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks

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