Scheduled System Maintenance
On Friday, October 20, IEEE Xplore will be unavailable from 9:00 PM-midnight ET. We apologize for the inconvenience.
Notice: There is currently an issue with the citation download feature. Learn more.

IEEE Transactions on Signal and Information Processing over Networks

Issue 4 • Dec. 2016

Filter Results

Displaying Results 1 - 22 of 22
  • Table of contents

    Publication Year: 2016, Page(s):C1 - C4
    Request permission for commercial reuse | PDF file iconPDF (170 KB)
    Freely Available from IEEE
  • IEEE Transactions on Signal and Information Processing over Networks publication information

    Publication Year: 2016, Page(s): C2
    Request permission for commercial reuse | PDF file iconPDF (43 KB)
    Freely Available from IEEE
  • Guest Editorial Inference and Learning over Networks

    Publication Year: 2016, Page(s):423 - 425
    Request permission for commercial reuse | PDF file iconPDF (275 KB) | HTML iconHTML
    Freely Available from IEEE
  • Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics

    Publication Year: 2016, Page(s):426 - 441
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (682 KB) | HTML iconHTML

    This paper focuses on recursive nonlinear least-squares parameter estimation in multiagent networks, where the individual agents observe sequentially over time an independent and identically distributed time-series consisting of a nonlinear function of the true but unknown parameter corrupted by noise. A distributed recursive estimator of the consensus + innovations type, namely CIWNLS, is propose... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Distributed Detection Over Adaptive Networks: Refined Asymptotics and the Role of Connectivity

    Publication Year: 2016, Page(s):442 - 460
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (819 KB) | HTML iconHTML

    We consider distributed detection problems over adaptive networks, where dispersed agents learn continually from streaming data by means of local interactions. The requirement of adaptation allows the network of detectors to track drifts in the underlying hypothesis. The requirement of cooperation allows each agent to deliver a performance superior to what would be obtained if it were acting indiv... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Uniform $varepsilon$-Stability of Distributed Nonlinear Filtering Over DNAs: Gaussian-Finite HMMs

    Publication Year: 2016, Page(s):461 - 476
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1143 KB) | HTML iconHTML

    In this paper, we study stability of distributed filtering of Markov chains with finite state space, partially observed in conditionally Gaussian noise. We consider a nonlinear filtering scheme over a distributed network of agents, which relies on the distributed evaluation of the likelihood part of the respective centralized estimator. Distributed evaluation of likelihoods is based on a particula... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Distributed Quaternion Kalman Filter With Applications to Smart Grid and Target Tracking

    Publication Year: 2016, Page(s):477 - 488
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2372 KB) | HTML iconHTML

    Recent advances in sensor and communication technologies have made the deployment of sensor networks in a variety of roles feasible, including smart grid management applications and collaborative target tracking solutions. While most research in distributed adaptive signal processing is conducted in the real and complex domains, inherently in many real-world applications the data sources are three... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Efficient Distributed Online Prediction and Stochastic Optimization With Approximate Distributed Averaging

    Publication Year: 2016, Page(s):489 - 506
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (714 KB) | HTML iconHTML Multimedia Media

    We study distributed methods for online prediction and stochastic optimization. Our approach is iterative: In each round, nodes first perform local computations and then communicate in order to aggregate information and synchronize their decision variables. Synchronization is accomplished through the use of a distributed averaging protocol. When an exact distributed averaging protocol is used, it ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization

    Publication Year: 2016, Page(s):507 - 522
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (800 KB) | HTML iconHTML

    This paper considers decentralized consensus optimization problems where different summands of a global objective function are available at nodes of a network that can communicate with neighbors only. The proximal method of multipliers is considered as a powerful tool that relies on proximal primal descent and dual ascent updates on a suitably defined augmented Lagrangian. The structure of the aug... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Data Injection Attacks in Randomized Gossiping

    Publication Year: 2016, Page(s):523 - 538
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1951 KB) | HTML iconHTML

    The subject of this paper is the detection and mitigation of data injection attacks in randomized average consensus gossip algorithms. It is broadly known that the main advantages of randomized average consensus gossip are its fault tolerance and distributed nature. Unfortunately, the flat architecture of the algorithm also increases the attack surface for a data injection attack. Even though we c... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Signal Recovery on Graphs: Fundamental Limits of Sampling Strategies

    Publication Year: 2016, Page(s):539 - 554
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1171 KB) | HTML iconHTML Multimedia Media

    This paper builds theoretical foundations for the recovery of a newly proposed class of smooth graph signals, approximately bandlimited graph signals, under three sampling strategies: uniform sampling, experimentally designed sampling, and active sampling. We then state minimax lower bounds on the maximum risk for the approximately bandlimited class under these three sampling strategies and show t... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Adaptive Least Mean Squares Estimation of Graph Signals

    Publication Year: 2016, Page(s):555 - 568
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1406 KB) | HTML iconHTML

    The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of observations over a subset of vertices. A detailed mean square analysi... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Information Diffusion of Topic Propagation in Social Media

    Publication Year: 2016, Page(s):569 - 581
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (977 KB) | HTML iconHTML

    Real-world social and/or operational networks consist of agents with associated states, whose connectivity forms complex topologies. This complexity is further compounded by interconnected information layers, consisting, for instance, documents/resources of the agents which mutually share topical similarities. Our goal in this paper is to predict the specific states of the agents, as their observe... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Metadata-Conscious Anonymous Messaging

    Publication Year: 2016, Page(s):582 - 594
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (734 KB) | HTML iconHTML

    Anonymous messaging platforms allow users to spread messages over a network (e.g., a social network) without revealing message authorship to other users. Popular demand for anonymous messaging is evidenced by the success of mobile apps like Whisper and Yik Yak. In such platforms, the spread of messages is typically modeled as a diffusion process. Recent advances in network analysis have revealed t... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Evolutionary Information Diffusion Over Heterogeneous Social Networks

    Publication Year: 2016, Page(s):595 - 610
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3274 KB) | HTML iconHTML

    A huge amount of information, created and forwarded by millions of people with various characteristics, is propagating through the online social networks every day. Understanding the mechanisms of the information diffusion over the social networks is critical to various applications including online advertisement and website management. Different from most of the existing works, we investigate the... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Dual Graph Regularized Dictionary Learning

    Publication Year: 2016, Page(s):611 - 624
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1599 KB) | HTML iconHTML

    Dictionary learning (DL) techniques aim to find sparse signal representations that capture prominent characteristics in a given data. Such methods operate on a data matrix Y ∈ RN×M, where each of its columns yi ∈ RN constitutes a training sample, and these columns together represent a sampling from the data manifold. For signals y ∈ RN residing on weighted graphs, an ad... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • On the Difficulty of Selecting Ising Models With Approximate Recovery

    Publication Year: 2016, Page(s):625 - 638
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1155 KB) | HTML iconHTML

    In this paper, we consider the problem of estimating the underlying graph associated with an Ising model given a number of independent and identically distributed samples. We adopt an approximate recovery criterion that allows for a number of missed edges or incorrectly included edges, in contrast with the widely studied exact recovery problem. Our main results provide information-theoretic lower ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • List of Reviewers

    Publication Year: 2016, Page(s):639 - 642
    Request permission for commercial reuse | PDF file iconPDF (59 KB)
    Freely Available from IEEE
  • IEEE Transactions on Signal and Information Processing over Networks Edics

    Publication Year: 2016, Page(s): 643
    Request permission for commercial reuse | PDF file iconPDF (50 KB)
    Freely Available from IEEE
  • IEEE Transactions on Multimedia information for authors

    Publication Year: 2016, Page(s):644 - 645
    Request permission for commercial reuse | PDF file iconPDF (62 KB)
    Freely Available from IEEE
  • 2016 Index IEEE Transactions on Signal and Information Processing over Networks Vol. 2

    Publication Year: 2016, Page(s):646 - 653
    Request permission for commercial reuse | PDF file iconPDF (108 KB)
    Freely Available from IEEE
  • IEEE Transactions on Signal and Information Processing over Networks

    Publication Year: 2016, Page(s): C3
    Request permission for commercial reuse | PDF file iconPDF (49 KB)
    Freely Available from IEEE

Aims & Scope

The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Petar Djuric
Stony Brook University, Electrical & Computer Engineering
Light Engineering, Room 245
Stony Brook, NY
11794-2350
USA
+1 631-632-8423
Fax: +1 631-632-8494
petar.djuric@stonybrook.edu