Compressed sensing of streaming data

Freris, Nikolaos M.; Ocal, Orhan; Vetterli, Martin

doi:10.1109/allerton.2013.6736668

Cited by 14 publications

(14 citation statements)

References 12 publications

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“…In large-scale CPS, such as transportation and sensor networks [14], real-time sensing produces really big data. It is indispensable to provide algorithms for efficiently filtering and mining big data [35,38] in the real-time [15,33].…”

Section: Design Challenges In Cpsmentioning

confidence: 99%

“…Real-Time Decisions: CPS operate subject to stringent real-time constraints in communication, computation and control. It is therefore crucial to account for deadlines in allocating resources and making decisions [21], to develop algorithms for online computing [15,33], as well as software that can facilitate a smooth operation in the real-time [27].…”

Section: Design Challenges In Cpsmentioning

confidence: 99%

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A software-defined architecture for control of IoT cyberphysical systems

Darabseh¹,

Freris

2019

Cluster Comput

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Based on software-defined principles, we propose a holistic architecture for Cyberphysical Systems (CPS) and Internet of Things (IoT) applications, and highlight the merits pertaining to scalability, flexibility, robustness, interoperability, and cyber security. Our design especially capitalizes on the computational units possessed by smart agents, which may be utilized for decentralized control and in-network data processing. We characterize the data flow, communication flow, and control flow that assimilate a set of components such as sensors, actuators, controllers, and coordinators in a systemic programmable fashion. We specifically aim for distributed and decentralized decision-making by spreading the control over several hierarchical layers. In addition, we propose a middleware layer to encapsulate units and services for time-critical operations in highly dynamic environments. We further enlist a multitude of vulnerabilities to cyberattacks, and integrate softwaredefined solutions for enabling resilience, detection and recovery. In this purview, several controllers cooperate to identify and respond to security threats and abnormal situations in a self-adjusting manner. Last, we illustrate numerical simulations in support of the virtues of a software-defined design for CPS and IoT.

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Section: Design Challenges In Cpsmentioning

confidence: 99%

Section: Design Challenges In Cpsmentioning

confidence: 99%

A software-defined architecture for control of IoT cyberphysical systems

Darabseh¹,

Freris

2019

Cluster Comput

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“…Solving (26) is one of the dominant steps in the implementation of the proposed technique. Theoretically, any sparse estimation algorithm can be used.…”

Section: A  mentioning

confidence: 99%

“…The problem studied in this paper is in essence a reconstruction of sparse time-varying signals from streaming measurements, which have been discussed in [26]- [30]. These algorithms fully exploit the measurement systems and perform sliding estimation of the sparse coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms fully exploit the measurement systems and perform sliding estimation of the sparse coefficients. The methods in [26], [27] use a sliding window for data processing and entail recursive sampling and iterative recovery. In each sliding sampling, the measurement matrix is recursively generated by permuting the previous measurement matrix.…”

Section: Introductionmentioning

confidence: 99%

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Segment-sliding reconstruction of pulsed radar echoes with sub-Nyquist sampling

Zhang

Chen

et al. 2016

Sci. China Inf. Sci.

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It has been shown that analog-to-information conversion (AIC) is an efficient scheme to perform sub-Nyquist sampling of pulsed radar echoes. However, it is often impractical, if not infeasible, to reconstruct full-range Nyquist samples because of huge storage and computational load requirements. Based on the analyses of AIC measurement system, this paper develops a novel segment-sliding reconstruction (SegSR) scheme to effectively reconstruct the Nyquist samples. The SegSR performs segment-by-segment reconstruction in a sliding mode and can be implemented in real-time. An important characteristic that distinguish the proposed SegSR from the existing methods is that the measurement matrix in each segment satisfies the restricted isometry property (RIP) condition. Partial support in the previous segment can be incorporated into the estimation of the Nyquist samples in the current segment. The effect of interference introduced from adjacent segments is theoretically analyzed, and it is revealed that the interference consists of two interference levels having different impacts to the signal reconstruction performance. With these observations, a two-step orthogonal matching pursuit (OMP) procedure is proposed for segment reconstruction, which takes into account different interference levels and partially known support of the previous segment. The proposed SegSR achieves nearly optimal reconstruction performance with a significant reduction of computational loads and storage requirements. Theoretical analyses and simulations verify its effectiveness. Index Terms-compressed sensing, analog-to-information conversion, OMP, segment-sliding reconstruction. N  sparse coefficient vector σ , y is an 1 M  measurement vector, Φ is an M N  observation matrix with M N  , Ψ is an N N 

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SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization

Wai

Freris

Nedić

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems. SUCAG constitutes an unbiased total gradient tracking technique that uses Hessian information to accelerate convergence. We analyze our method under the general asynchronous model of computation, in which each function is selected infinitely often with possibly unbounded (but sublinear) delay. For strongly convex problems, we establish linear convergence for the SUCAG method. When the initialization point is sufficiently close to the optimal solution, the established convergence rate is only dependent on the condition number of the problem, making it strictly faster than the known rate for the SAGA method.Furthermore, we describe a Markov-driven approach of implementing the SUCAG method in a distributed asynchronous multi-agent setting, via gossiping along a random walk on an undirected communication graph. We show that our analysis applies as long as the graph is connected and, notably, establishes an asymptotic linear convergence rate that is robust to the graph topology. Numerical results demonstrate the merits of our algorithm over existing methods.

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Compressed sensing of streaming data

Cited by 14 publications

References 12 publications

A software-defined architecture for control of IoT cyberphysical systems

A software-defined architecture for control of IoT cyberphysical systems

Segment-sliding reconstruction of pulsed radar echoes with sub-Nyquist sampling

SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization

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