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94
Gossip algorithms for distributed signal processing
- PROCEEDINGS OF THE IEEE
, 2010
"... Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the co ..."
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Cited by 116 (30 self)
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Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmittedmessages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.
Compressive data gathering for large-scale wireless sensor networks
- in Proc. ACM Mobicom’09
, 2009
"... This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for largescale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and la ..."
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Cited by 76 (4 self)
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This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for largescale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and large-scale wireless sensor networks. We consider the scenario in which a large number of sensor nodes are densely deployed and sensor readings are spatially correlated. The proposed compressive data gathering is able to reduce global scale communication cost without introducing intensive computation or complicated transmission control. The load balancing characteristic is capable of extending the lifetime of the entire sensor network as well as individual sensors. Furthermore, the proposed scheme can cope with abnormal sensor readings gracefully. We also carry out the analysis of the network capacity of the proposed compressive data gathering and validate the analysis through ns-2 simulations. More importantly, this novel compressive data gathering has been tested on real sensor data and the results show the efficiency and robustness of the proposed scheme.
Practical recommendations on crawling online social networks
- SELECTED AREAS IN COMMUNICATIONS, IEEE JOURNAL ON
, 2011
"... Our goal in this paper is to develop a practical framework for obtaining a uniform sample of users in an online social network (OSN) by crawling its social graph. Such a sample allows to estimate any user property and some topological properties as well. To this end, first, we consider and compare ..."
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Cited by 37 (1 self)
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Our goal in this paper is to develop a practical framework for obtaining a uniform sample of users in an online social network (OSN) by crawling its social graph. Such a sample allows to estimate any user property and some topological properties as well. To this end, first, we consider and compare several candidate crawling techniques. Two approaches that can produce approximately uniform samples are the Metropolis-Hasting random walk (MHRW) and a re-weighted random walk (RWRW). Both have pros and cons, which we demonstrate through a comparison to each other as well as to the “ground truth. ” In contrast, using Breadth-First-Search (BFS) or an unadjusted Random Walk (RW) leads to substantially biased results. Second, and in addition to offline performance assessment, we introduce online formal convergence diagnostics to assess sample quality during the data collection process. We show how these diagnostics can be used to effectively determine when a random walk sample is of adequate size and quality. Third, as a case study, we apply the above methods to Facebook and we collect the first, to the best of our knowledge, representative sample of Facebook users. We make it publicly available and employ it to characterize several key properties of Facebook.
Compressive sensing over graphs
- in Proc. IEEE INFOCOM
, 2011
"... Abstract—In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are interested in recovering sparse vectors representing the properties of the edges from a graph. Unlike existing ..."
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Cited by 32 (3 self)
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Abstract—In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are interested in recovering sparse vectors representing the properties of the edges from a graph. Unlike existing compressive sensing results, the collective additive measurements we are allowed to take must follow connected paths over the underlying graph. For a sufficiently connected graph with n nodes, it is shown that, using O(k log(n)) path measurements, we are able to recover any k-sparse link vector (with no more than k nonzero elements), even though the measurements have to follow the graph path constraints. We mainly show that the computationally efficient 1 minimization can provide theoretical guarantees for inferring such k-sparse vectors with O(k log(n)) path measurements from the graph. I.
An Efficient Algorithm for Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing
, 2009
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Does compressed sensing improve the throughput of wireless sensor networks
- in ICC 2010. IEEE
"... Abstract—Although compressed sensing (CS) has been envi-sioned as a useful technique to improve the performance of wireless sensor networks (WSNs), it is still not very clear how exactly it will be applied and how big the improvements will be. In this paper, we propose two different ways (plain-CS a ..."
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Cited by 17 (1 self)
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Abstract—Although compressed sensing (CS) has been envi-sioned as a useful technique to improve the performance of wireless sensor networks (WSNs), it is still not very clear how exactly it will be applied and how big the improvements will be. In this paper, we propose two different ways (plain-CS and hybrid-CS) of applying CS to WSNs at the networking layer, in the form of a particular data aggregation mechanism. We formulate three flow-based optimization problems to compute the throughput of the non-CS, plain-CS, and hybrid-CS schemes. We provide the exact solution to the first problem corresponding to the non-CS case and lower bounds for the cases with CS. Our preliminary numerical results are only for a low-power regime. They illustrate two crucial insights: first, applying CS naively may not bring any improvement, and secondly, our hybrid-CS can achieve significant improvement in throughput. Index Terms—Wireless sensor networks, compressed sensing, data aggregation, routing, scheduling. I.
Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering
, 2010
"... We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks. The network capacity has been proven to increase proportionally to the sparsity of sensor ..."
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Cited by 17 (0 self)
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We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks. The network capacity has been proven to increase proportionally to the sparsity of sensor readings. In this paper, we further address two key problems in the CDG framework. First, we investigate how to generate RIP (restricted isometry property) preserving measurements of sensor readings by taking multi-hop communication cost into account. Excitingly, we discover that a simple form of measurement matrix [
Data gathering in wireless sensor networks through intelligent compressive sensing
- In Proceedings of the 31th IEEE International Conference on Computer Communications (INFOCOM
, 2012
"... Abstract—The recently emerged compressive sensing (CS) theory provides a whole new avenue for data gathering in wireless sensor networks with benefits of universal sampling and decentralized encoding. However, existing compressive sens-ing based data gathering approaches assume the sensed data has a ..."
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Cited by 17 (0 self)
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Abstract—The recently emerged compressive sensing (CS) theory provides a whole new avenue for data gathering in wireless sensor networks with benefits of universal sampling and decentralized encoding. However, existing compressive sens-ing based data gathering approaches assume the sensed data has a known constant sparsity, ignoring that the sparsity of natural signals vary in temporal and spatial domain. In this paper, we present an adaptive data gathering scheme by com-pressive sensing for wireless sensor networks. By introducing autoregressive (AR) model into the reconstruction of the sensed data, the local correlation in sensed data is exploited and thus local adaptive sparsity is achieved. The recovered data at the sink is evaluated by utilizing successive reconstructions, the relation between error and measurements. Then the number of measurements is adjusted according to the variation of the sensed data. Furthermore, a novel abnormal readings detection and identification mechanism based on combinational sparsity reconstruction is proposed. Internal error and external event are distinguished by their specific features. We perform extensive testing of our scheme on the real data sets and experimental results validate the efficiency and efficacy of the proposed scheme. Up to about 8dB SNR gain can be achieved over conventional CS based method with moderate increase of complexity. I.
