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15
Random access compressed sensing for energyefficient underwater sensor networks
 IEEE Journal on Selected Areas in Communications
, 2011
"... Abstract—Inspired by the theory of compressed sensing and employing random channel access, we propose a distributed energyefficient sensor network scheme denoted by Random Access Compressed Sensing (RACS). The proposed scheme is suitable for longterm deployment of large underwater networks, in whi ..."
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Cited by 16 (1 self)
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Abstract—Inspired by the theory of compressed sensing and employing random channel access, we propose a distributed energyefficient sensor network scheme denoted by Random Access Compressed Sensing (RACS). The proposed scheme is suitable for longterm deployment of large underwater networks, in which saving energy and bandwidth is of crucial importance. During each frame, a randomly chosen subset of nodes participate in the sensing process, then share the channel using random access. Due to the nature of random access, packets may collide at the fusion center. To account for the packet loss that occurs due to collisions, the network design employs the concept of sufficient sensing probability. With this probability, sufficiently many data packets – as required for field reconstruction based on compressed sensing – are to be received. The RACS scheme prolongs network lifetime while employing a simple and distributed scheme which eliminates the need for scheduling. Index Terms—Sensor networks, compressed sensing, wireless communications, underwater acoustic networks, random access. I.
On the linear convergence of the ADMM in decentralized consensus optimization
 IEEE Transactions on Signal Processing
, 2014
"... Abstract—In decentralized consensus optimization, a connected network of agents collaboratively minimize the sum of their local objective functions over a common decision variable, where their information exchange is restricted between the neighbors. To this end, one can first obtain a problem refor ..."
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Cited by 12 (3 self)
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Abstract—In decentralized consensus optimization, a connected network of agents collaboratively minimize the sum of their local objective functions over a common decision variable, where their information exchange is restricted between the neighbors. To this end, one can first obtain a problem reformulation and then apply the alternating direction method of multipliers (ADMM). The method applies iterative computation at the individual agents and information exchange between the neighbors. This approach has been observed to converge quickly and deemed powerful. This paper establishes its linear convergence rate for the decentralized consensus optimization problem with strongly convex local objective functions. The theoretical convergence rate is explicitly given in terms of the network topology, the properties of local objective functions, and the algorithm parameter. This result is not only a performance guarantee but also a guideline toward accelerating the ADMM convergence. Index Terms—Decentralized consensus optimization, alternating direction method of multipliers (ADMM), linear convergence. I.
Distributed Covariance Estimation in Gaussian Graphical Models
"... Abstract—We consider distributed estimation of the inverse covariance matrix in Gaussian graphical models. These models factorize the multivariate distribution and allow for efficient distributed signal processing methods such as belief propagation (BP). The classical maximum likelihood approach to ..."
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Cited by 7 (3 self)
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Abstract—We consider distributed estimation of the inverse covariance matrix in Gaussian graphical models. These models factorize the multivariate distribution and allow for efficient distributed signal processing methods such as belief propagation (BP). The classical maximum likelihood approach to this covariance estimation problem, or potential function estimation in BP terminology, requires centralized computing and is computationally intensive. This motivates suboptimal distributed alternatives that tradeoff accuracy for communication cost. A natural solution is for each node to perform estimation of its local covariance with respect to its neighbors. The local maximum likelihood estimator is asymptotically consistent but suboptimal, i.e., it does not minimize mean squared estimation (MSE) error. We propose to improve the MSE performance by introducing additional symmetry constraints using averaging and pseudolikelihood estimation approaches. We compute the proposed estimates using message passing protocols, which can be efficiently implemented in large scale graphical models with many nodes. We illustrate the advantages of our proposed methods using numerical experiments with synthetic data as well as real world data from a wireless sensor network. Index Terms—Covariance estimation, distributed signal processing, graphical models. I.
Design of a random access network for compressed sensing
 Information Theory and Applications Workshop (ITA), 2011
, 2011
"... AbstractFor networks that are deployed for longterm monitoring of environmental phenomena, it is of crucial importance to design an efficient data gathering scheme that prolongs the lifetime of the network. To this end, we exploit the sparse nature of the monitored field and consider a Random Acc ..."
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Cited by 4 (2 self)
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AbstractFor networks that are deployed for longterm monitoring of environmental phenomena, it is of crucial importance to design an efficient data gathering scheme that prolongs the lifetime of the network. To this end, we exploit the sparse nature of the monitored field and consider a Random Access Compressed Sensing (RACS) scheme in which the sensors transmit at random to a fusion center which reconstructs the field. We provide an analytical framework for system design that captures packet collisions due to random access as well as packet errors due to communication noise. Through analysis and examples, we demonstrate that recovery of the field can be attained using only a fraction of the resources used by a conventional TDMA network, while employing a scheme which is simple to implement and requires no synchronization.
Sparsity Order Estimation and its Application in Compressive Spectrum Sensing for Cognitive Radios
"... Abstract—Compressive sampling techniques can effectively reduce the acquisition costs of highdimensional signals by utilizing the fact that typical signals of interest are often sparse in a certain domain. For compressive samplers, the number of samples Mr needed to reconstruct a sparse signal is d ..."
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Cited by 4 (0 self)
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Abstract—Compressive sampling techniques can effectively reduce the acquisition costs of highdimensional signals by utilizing the fact that typical signals of interest are often sparse in a certain domain. For compressive samplers, the number of samples Mr needed to reconstruct a sparse signal is determined by the actual sparsity order Snz of the signal, which can be much smaller than the signal dimension N. However,Snz is often unknown or dynamically varying in practice, and the practical sampling rate has to be chosen conservatively according to an upper bound Smax of the actual sparsity order in lieu of Snz, which can be unnecessarily high. To circumvent such wastage of the sampling resources, this paper introduces the concept of sparsity order estimation, which aims to accurately acquire Snz prior to sparse signal recovery, by using a very small number of samples Me less than Mr. A statistical learning methodology is used to quantify
On the convergence of decentralized gradient descent
, 2013
"... Consider the consensus problem of minimizing f(x) = ∑n i=1 fi(x) where each fi is only known to one individual agent i belonging to a connected network of n agents. All the agents shall collaboratively solve this problem and obtain the solution via data exchanges only between neighboring agents. Suc ..."
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Cited by 3 (1 self)
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Consider the consensus problem of minimizing f(x) = ∑n i=1 fi(x) where each fi is only known to one individual agent i belonging to a connected network of n agents. All the agents shall collaboratively solve this problem and obtain the solution via data exchanges only between neighboring agents. Such algorithms avoid the need of a fusion center, offer better network load balance, and improve data privacy. We study the decentralized gradient descent method in which each agent i updates its variable x(i), which is a local approximate to the unknown variable x, by taking the average of its neighbors ’ followed by making a local negative gradient step −α∇fi(x(i)). The iteration is x(i)(k + 1)←
Latent variables based data estimation for sensing applications
 In IEEE Intl. Conf. on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP
, 2011
"... Abstract—Recovering missing sensor data is a critical problem for sensor networks, especially when nodes duty cycle their activity or may experience periodic downtimes due to limited energy. Fortunately, sensor readings are often correlated across different nodes and sensor types. Among stateofth ..."
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Abstract—Recovering missing sensor data is a critical problem for sensor networks, especially when nodes duty cycle their activity or may experience periodic downtimes due to limited energy. Fortunately, sensor readings are often correlated across different nodes and sensor types. Among stateoftheart statistical data estimation techniques, latent variable based factor models have emerged as a powerful framework for recovering missing data. In this paper we propose the use of latent variable models to estimate missing data in heterogeneous sensor networks. Our model not only correlates data across different sensor locations and types, but also takes advantage of the temporal structure that is often present in sensor readings. We analyze how this model can effectively reconstruct missing sensor data when the individual sensor nodes have to dutycycle their activity in order to extend network lifetime. We evaluate our model on a real life sensor network consisting of 122 environmental monitoring stations that periodically collect data from 13 different sensors. Results show that our proposed model can effectively reconstruct over 50 % of missing data with less than 10 % error. I.
Compressive sensing signal detection algorithm based on location information of sparse coefficients
 JDCTA
"... doi:10.4156/jdcta.vol4. issue8.8 Without reconstructing the signal themselves, signal detection could be solved by detection algorithm, which directly processes sampling value obtained from compressive sensing signal. In current detection algorithm, as the judgment criterion, the threshold depends o ..."
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Cited by 1 (0 self)
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doi:10.4156/jdcta.vol4. issue8.8 Without reconstructing the signal themselves, signal detection could be solved by detection algorithm, which directly processes sampling value obtained from compressive sensing signal. In current detection algorithm, as the judgment criterion, the threshold depends on Monte Carlo simulations, which takes too much time, affecting detection efficiency. Therefore ， in this paper ， we propose an algorithm to detect known signal in noise. First, get the sparse coefficients position information of tobedetected signal in Transform domain. Then, acquire the position information of interested signal based on prior information. Finally, use the correlation of them as judgment criterion to complete detection. Simulation shows that under the same circumstances, compared with traditional algorithm, the algorithm this paper introduced can complete detection rapidly without reducing success rate.
RESEARCH Open Access
"... Oxymatrine induces human pancreatic cancer PANC1 cells apoptosis via regulating expression of Bcl2 and IAP families, and releasing of cytochrome c ..."
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Oxymatrine induces human pancreatic cancer PANC1 cells apoptosis via regulating expression of Bcl2 and IAP families, and releasing of cytochrome c
1 Random Access Compressed Sensing for EnergyEfficient Underwater Sensor Networks
"... Abstract—Inspired by the theory of compressed sensing and employing random channel access, we propose a distributed energyefficient sensor network scheme denoted by Random Access Compressed Sensing (RACS). The proposed scheme is suitable for longterm deployment of large underwater networks, in whi ..."
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Abstract—Inspired by the theory of compressed sensing and employing random channel access, we propose a distributed energyefficient sensor network scheme denoted by Random Access Compressed Sensing (RACS). The proposed scheme is suitable for longterm deployment of large underwater networks, in which saving energy and bandwidth is of crucial importance. During each frame, a randomly chosen subset of nodes participate in the sensing process, then share the channel using random access. Due to the nature of random access, packets may collide at the fusion center. To account for the packet loss that occurs due to collisions, the network design employs the concept of sufficient sensing probability. With this probability, sufficiently many data packets – as required for field reconstruction based on compressed sensing – are to be received. The RACS scheme prolongs network lifetime while employing a simple and distributed scheme which eliminates the need for scheduling. Index Terms—Sensor networks, compressed sensing, wireless communications, underwater acoustic networks, random access. I.