<![CDATA[ IEEE Transactions on Signal and Information Processing over Networks - Popular ]]>
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Popular Articles Alert for this Publication# 6884276 2017February <![CDATA[Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing]]>1289103663<![CDATA[Topology-Independent Distributed Adaptive Node-Specific Signal Estimation in Wireless Sensor Networks]]> ) algorithm is presented where each node of a wireless sensor network (WSN) is tasked with estimating a node-specific desired signal. To reduce the amount of data exchange, each node applies a linear compression to its sensors signal observations, and only transmits the compressed observations to its neighbors. The algorithm is shown to converge to the same optimal node-specific signal estimates as if each node were to transmit its raw (uncompressed) sensor signal observations to every other node in the WSN. The algorithm is first introduced in a fully connected WSN and then shown, in fact, to have the same convergence properties in any topology. When implemented in other topologies, the nodes rely on an in-network summation of the transmitted compressed observations that can be accomplished by various means. We propose a method for this in-network summation via a data-driven signal flow that takes place on a tree, where the topology of the tree may change in each iteration. This makes the algorithm less sensitive to link failures and applicable to WSNs with dynamic topologies.]]>311301441159<![CDATA[Distributed Localization and Tracking of Mobile Networks Including Noncooperative Objects]]>215771953<![CDATA[Asynchronous Optimization Over Heterogeneous Networks Via Consensus ADMM]]>311141291734<![CDATA[Optimal Power Allocation for Hybrid Energy Harvesting and Power Grid Coexisting System With Power Upper Bounded Constraints]]> solution, not an optimal solution, even with more computations. The novelty of the proposed algorithms is that they compute the exact solutions with the low degree polynomial computational complexity. To the best of the authors’ knowledge, under the same assumptions, no prior publication, including PD-IPM, can arrive at such results. Numerical examples also illustrate efficiency of the proposed algorithms.]]>311871991550<![CDATA[Sparse Signal Detection With Compressive Measurements via Partial Support Set Estimation]]>314660835<![CDATA[Mobile Access Coordinated Wireless Sensor Networks—Design and Analysis]]>31172186896<![CDATA[Decentralized Joint-Sparse Signal Recovery: A Sparse Bayesian Learning Approach]]>3129451284<![CDATA[Joint Uplink/Downlink Optimization for Backhaul-Limited Mobile Cloud Computing with User Scheduling]]>PP9911622<![CDATA[Detecting Convoys Using License Plate Recognition Data]]>233914051674<![CDATA[Adaptive Least Mean Squares Estimation of Graph Signals]]>245555681406<![CDATA[A Game-Theoretic Approach to Fake-Acknowledgment Attack on Cyber-Physical Systems]]>31111395<![CDATA[Data Falsification Attacks on Consensus-Based Detection Systems]]>a priori, we propose learning based techniques to enable an adaptive design of the local fusion or update rules. Numerical results are presented for illustration.]]>31145158794<![CDATA[QoS-Driven Energy-Efficient Resource Allocation in Multiuser Amplify-and-Forward Relay Networks]]>PP9911421<![CDATA[A Distributed Quaternion Kalman Filter With Applications to Smart Grid and Target Tracking]]>244774882372<![CDATA[Clustering and Community Detection With Imbalanced Clusters]]>3161761032<![CDATA[NEXT: In-Network Nonconvex Optimization]]>22120136682<![CDATA[Detection of Single Versus Multiple Antenna Transmission Systems Using Pilot Data]]>a priori whether the transmission is via a single antenna or multiple antennas. The receiver is assumed to be employed with a known number of antennas. In a data frame transmitted by most multiple input multiple output (MIMO) systems, some pilot or training data are inserted for symbol timing synchronization and estimation of the channel. Our goal is to perform MIMO transmit antenna classification using this pilot data. More specifically, the problem of determining the transmission system is cast as a multiple hypothesis testing problem where the number of hypotheses is equal to the maximum number of transmit antennas. Under the assumption of receiver having the exact knowledge of pilot data used for timing synchronization and channel estimation, we consider maximum likelihood (ML) and correlation test statistics to classify the MIMO transmit system. When only probabilistic knowledge of pilot data is available at the receiver, a hybrid ML-based test statistic is constructed using the expectation-maximization algorithm. The performance of the proposed algorithms is illustrated via simulations and comparative merits of different techniques in terms of the computational complexity and performance are discussed.]]>31159171897<![CDATA[An Online Parallel Algorithm for Recursive Estimation of Sparse Signals]]>1-regularized least-square problems approximately. The proposed scheme is novel in three aspects: 1) all elements of the unknown vector variable are updated in parallel at each time instant, and the convergence speed is much faster than state-of-the-art schemes which update the elements sequentially; 2) both the update direction and stepsize of each element have simple closed-form expressions, so the algorithm is suitable for online (real-time) implementation; and 3) the stepsize is designed to accelerate the convergence but it does not suffer from the common intricacy of parameter tuning. Both centralized and distributed implementation schemes are discussed. The attractive features of the proposed algorithm are also illustrated numerically.]]>23290305901<![CDATA[Active Sensing of Social Networks]]>234064191061<![CDATA[Privacy-Constrained Parallel Distributed Neyman-Pearson Test]]>317790788<![CDATA[Distortion Outage Minimization in Distributed Estimation With Estimation Secrecy Outage Constraints]]>311228739<![CDATA[A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation]]>a priori model information. Taking advantage of probe trajectories, whose capture is made possible by new measurement technologies, the present contribution extends the concept of ODM to that of link-dependent ODM (LODM). LODM also contains the flow distribution on links making specification of assignment models, e.g., by means of routing matrices, unnecessary. An original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimization problem, where the functional to be minimized consists of five convex functions, each modeling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoff's law), similarity of flows having similar origins and destinations, and positivity of traffic flows. A proximal primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations.]]>311041131104<![CDATA[Understanding Popularity Dynamics: Decision-Making With Long-Term Incentives]]>popularity dynamics, e.g., hashtags mention count dynamics, which characterize human behaviors quantitatively. It is crucial to understand the underlying mechanisms of popularity dynamics in order to utilize the valuable attention of people efficiently. In this paper, we propose a game-theoretic model to analyze and understand popularity dynamics. The model takes into account both the instantaneous incentives and long-term incentives during people's decision-making process. We theoretically prove that the proposed game possesses a unique symmetric Nash equilibrium (SNE), which can be computed via a backward induction algorithm. We also demonstrate that, at the SNE, the interaction rate first increases and then decreases, which confirms with the observations from real data. Finally, by using simulations as well as experiments based on real-world popularity dynamics data, we validate the effectiveness of the theory. We find that our theory can fit the real data well and also predict the future dynamics.]]>3191103846<![CDATA[Spectral Graph Wavelets and Filter Banks With Low Approximation Error]]>232302452741<![CDATA[Geometric Learning and Topological Inference With Biobotic Networks]]>biobots, and exploit coordinate-free local interactions among them to infer geometric and topological information about the environment, under minimal sensing and localization constraints. A metric estimation procedure is presented over a graphical representation referred to as the encounter graph in order to construct a geometric point cloud using manifold learning techniques. Topological data analysis (TDA) along with the proposed classification method is used to infer robust topological features of the space (e.g., existence of obstacles). We examine the asymptotic behavior of the proposed metric in terms of the convergence to the geodesic distances in the underlying manifold of the domain, and provide stability analysis results for the topological persistence. The proposed framework and its convergences and stability analysis are demonstrated through numerical simulations and experiments with Hexbugs.]]>312002151313<![CDATA[Dual Graph Regularized Dictionary Learning]]>246116241599<![CDATA[Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics]]>24426441682<![CDATA[Signal Recovery on Graphs: Fundamental Limits of Sampling Strategies]]>245395541171<![CDATA[Learning the Interference Graph of a Wireless Network]]>PP99112334<![CDATA[Decentralized Dynamic Optimization for Power Network Voltage Control]]>PP99113235<![CDATA[A novel networked online recursive identification method for multivariable systems with incomplete measurement information]]>PP99112547<![CDATA[Compressed Sensing in Wireless Sensor Networks without Explicit Position Information]]>PP99111222<![CDATA[Distributed Optimization Using the Primal-Dual Method of Multipliers]]>PP9911695<![CDATA[Consensus-based Algorithms for Distributed Network-State Estimation and Localization]]>PP99115570<![CDATA[Distributed Two-Step Quantized Fusion Rules Via Consensus Algorithm for Distributed Detection in Wireless Sensor Networks]]>233213352670<![CDATA[Data Denoising and Compression for Smart Grid Communication]]>222002143486<![CDATA[Stochastic Multidimensional Scaling]]>PP99112026<![CDATA[Distributed Detection Over Adaptive Networks: Refined Asymptotics and the Role of Connectivity]]>24442460819<![CDATA[Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data]]>23276289881<![CDATA[Evolutionary Information Diffusion Over Heterogeneous Social Networks]]>245956103274<![CDATA[Information Diffusion of Topic Propagation in Social Media]]>24569581977<![CDATA[Extraction of Temporal Network Structures From Graph-Based Signals]]>222152262402<![CDATA[Graph Signal Denoising via Trilateral Filter on Graph Spectral Domain]]>221371483060<![CDATA[Ensemble of distributed learners for online classification of dynamic data streams]]>131801941031<![CDATA[Cooperative Localization in WSNs: a Hybrid Convex/non-Convex Solution]]>PP99112083<![CDATA[Collision Detection by Networked Sensors]]>211151370<![CDATA[Energy-Assisted Information Detection for Simultaneous Wireless Information and Power Transfer: Performance Analysis and Case Studies]]>221491591185<![CDATA[A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization]]>24507522800<![CDATA[Localization in mobile networks via virtual convex hulls]]>PP99113958