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Change Detection in Dynamic Networks Using Network Characteristics IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210707
Jonathan Flossdorf, Carsten JentschIn recent years, the use of dynamic networks became increasingly popular. An important task is to identify differences at particular time points, e.g. for online monitoring, changepoint detection or testing procedures. Due to the complexity of network data, the statistical analysis is challenging. Therefore, it is usually a main step to characterize the networks by one or few scalarvalued metrics

Adaptive Kernel Learning in Heterogeneous Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210614
Hrusikesha Pradhan, Amrit Singh Bedi, Alec Koppel, Ketan RajawatWe consider learning in decentralized heterogeneous networks: agents seek to minimize a convex functional that aggregates data across the network, while only having access to their local data streams. We focus on the case where agents seek to estimate a regression function that belongs to a reproducing kernel Hilbert space (RKHS). To incentivize coordination while respecting network heterogeneity,

Full Euclidean Distance Based Selection Combining for SSK DF Cooperative Diversity Systems IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210609
A. Ananth, M. D. SelvarajSpace Shift Keying (SSK) is a multipleinput multipleoutput technique that has drawn more attention in recent years because of its low complex transceiver design. In this paper, an investigation on the Symbol Error Probability (SEP) performance of SSK in a DecodeandForward (DF) cooperative relaying system is done. The DF cooperative relaying scenario considers an origin node, an intermediate node

Collision Resolution With FM0 Signal Separation for ShortRange Random MultiAccess Wireless Network IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210630
Haifeng Wu, Xiaogang Wu, Yi Li, Yu ZengIn internet of things, a central node to multiple sensor nodes is usually a shortrange communication network, which generally uses random multiple access to resolve collision at a media access control (MAC) layer. In this case, the communication efficiency is not optimal. On the other hand, its efficiency can be improved in separated collision resolution, where collision signals can be separated and

A General Framework for Distributed Inference With Uncertain Models IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210701
James Z. Hare, César A. Uribe, Lance Kaplan, Ali JadbabaieThis paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesistesting framework, where we assume that the agents seek to agree on the hypothesis (target class) that best matches the distribution of observations

Column Partition Based Distributed Algorithms for Coupled Convex Sparse Optimization: Dual and Exact Regularization Approaches IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210608
Jinglai Shen, Jianghai Hu, Eswar Kumar Hathibelagal KammaraThis paper develops column partition based distributed schemes for a class of convex sparse optimization problems, e.g., basis pursuit (BP), LASSO, basis pursuit denosing (BPDN), and their extensions, e.g., fused LASSO. We are particularly interested in the cases where the number of (scalar) decision variables is much larger than the number of (scalar) measurements, and each agent has limited memory

A Newton Tracking Algorithm With Exact Linear Convergence for Decentralized Consensus Optimization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210527
Jiaojiao Zhang, Qing Ling, Anthony ManCho SoThis paper considers the problem of decentralized consensus optimization over a network, where each node holds a strongly convex and twicedifferentiable local objective function. Our goal is to minimize the sum of the local objective functions and find the exact optimal solution using only local computation and neighboring communication. We propose a novel Newton tracking algorithm, which updates

Graph Tikhonov Regularization and Interpolation Via Random Spanning Forests IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210528
Yusuf Yiğit Pilavcı, PierreOlivier Amblard, Simon Barthelmé, Nicolas TremblayNovel Monte Carlo estimators are proposed to solve both the Tikhonov regularization (TR) and the interpolation problems on graphs. These estimators are based on random spanning forests (RSF), the theoretical properties of which enable to analyze the estimators’ theoretical mean and variance. We also show how to perform hyperparameter tuning for these RSFbased estimators. TR is a component in many

Robust Adaptive Steganography Based on Dither Modulation and Modification With ReCompression IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210518
Zhaoxia Yin, Longfei KeTraditional adaptive steganography is a technique used for covert communication with high security, but it is invalid in the case of stego images are sent to legal receivers over networks which is lossy, such as JPEG compression of channels. To deal with such problem, robust adaptive steganography is proposed to enable the receiver to extract secret messages from the damaged stego images. Previous

Optimal Curing Strategy for Competing Epidemics Spreading Over Complex Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210423
Juntao Chen, Yunhan Huang, Rui Zhang, Quanyan ZhuOptimal curing strategy of suppressing competing epidemics spreading over complex networks is a critical issue. In this paper, we first establish a framework to capture the coupling between two epidemics, and then analyze the system's equilibrium states by categorizing them into three classes, and deriving their stability conditions. The designed curing strategy globally optimizes the tradeoff between

Stochastic EventTriggered Distributed Fusion Estimation Under Jamming Attacks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210421
Li Li, Mengfei Niu, Yuanqing Xia, Hongjiu YangThe paper concentrates on the distributed fusion estimation issue of a bandwidthconstrained multisensor nonlinear networked system suffering from jamming attacks. For each communication channel, a stochastic eventtriggered transmission scheme is developed to reduce excessive communication between smart sensors and local estimators, and a Stackelberg game framework is established to analyze interactions

InterCluster Transmission Control Using Graph Modal Barriers IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210407
Leiming Zhang, Brian M. Sadler, Rick S. Blum, Subhrajit BhattacharyaIn this paper we consider the problem of transmission across a graph and develop a method for effectively controlling/restricting it with limited resources. Transmission can represent information transfer across a social network, spread of a malicious virus across a computer network, or spread of an infectious disease across communities. The key insight is to appropriately reduce the capacity for transmission

Collaborative Cloud and Edge Mobile Computing in CRAN Systems With Minimal EndtoEnd Latency IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210406
SeokHwan Park, Seongah Jeong, Jinyeop Na, Osvaldo Simeone, Shlomo ShamaiMobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed

Optimizing Thresholds of the Scan Statistic to Improve Its Worst Case Detection Performance in Sensor Detection Systems IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210329
Benedito J. B. FonsecaThis paper focuses on how to improve the detection performance of distributed sensor systems to detect an emitter using the scan statistic. Considering that the emitter location is often unknown, we adopt a conservative approach and focus on the detection performance under the worst case emitter location. To improve the worst case detection performance, we propose a modified scan statistic: while the

On Trajectory Design for Intruder Detection in Wireless Mobile Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210318
Edmond Nurellari, Daniel Bonilla Licea, Mounir Ghogho, Mario Eduardo RiveroAngelesWe address the problem of detecting the invasion of an intruder into a region of interest (ROI) which is monitored by a distributed bandwidthconstrained wireless mobile sensor network (WMSN). We design periodic trajectories for the mobile sensor nodes (MSNs) such that high detection probabilities are obtained while maintaining the MSNs’ energy consumption low. To reduce the transmission and processing

Differentially Private Distributed Resource Allocation via Deviation Tracking IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210302
Tie Ding, Shanying Zhu, Cailian Chen, Jinming Xu, Xinping GuanThis paper studies the distributed resource allocation problem where all the agents cooperatively minimize the sum of their cost functions. To prevent private information from being disclosed, agents need to keep their cost functions private against potential adversaries and other agents. We first propose a completely distributed algorithm via deviation tracking that deals with constrained resource

ImpairmentsAware Resource Allocation for FD Massive MIMO Relay Networks: Sum Rate and DeliveryTime Optimization Perspectives IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210129
Vimal Radhakrishnan, Omid Taghizadeh, Rudolf MatharIn this paper, we investigate the resource allocation problem for a fullduplex (FD) massive multipleinputmultipleoutput (mMIMO) multicarrier (MC) decode and forward (DF) relay system which serves multiple MC singleantenna halfduplex (HD) nodes. In addition to the prior studies focusing on maximizing the sumrate and energy efficiency, we focus on minimizing the overall delivery time for a given

KernelBased Graph Learning From Smooth Signals: A Functional Viewpoint IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210217
Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino SejdinovicThe problem of graph learning concerns the construction of an explicit topological structure revealing the relationship between nodes representing data entities, which plays an increasingly important role in the success of many graphbased representations and algorithms in the field of machine learning and graph signal processing. In this paper, we propose a novel graph learning framework that incorporates

Probabilistic Reconstruction of SpatioTemporal Processes Over MultiRelational Graphs IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210217
Qin Lu, Georgios B. GiannakisGiven nodal observations that can be limited due to sampling costs or privacy concerns, several networksciencerelated applications entail reconstruction of values on all network nodes by leveraging topology information. Such a semisupervised learning (SSL) task has been tackled mainly for graphs capturing a single class of interdependencies (or relations) among nodal variables. Faced with multirelational

Online ProximalADMM for TimeVarying Constrained Convex Optimization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210203
Yijian Zhang, Emiliano Dall’Anese, Mingyi HongThis paper considers a convex optimization problem with cost and constraints that evolve over time. The function to be minimized is strongly convex and possibly nondifferentiable, and variables are coupled through linear constraints. In this setting, the paper proposes an online algorithm based on the alternating direction method of multipliers (ADMM), to track the optimal solution trajectory of the

Distributed Estimation Under Sensor Attacks: Linear and Nonlinear Measurement Models IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210129
Min Meng, Xiuxian Li, Gaoxi XiaoThis paper investigates distributed estimation of an unknown vector parameter in adversarial environments. Individual agents make successive local measurements of the unknown parameter and aim at estimating the unknown parameter consistently by sharing these measurements with their neighbors over a timevarying directed communication graph even when some of the agents are under attacks and their measurements

Community Detection in Dynamic Networks: Equivalence Between Stochastic Blockmodels and Evolutionary Spectral Clustering IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210118
Abdullah Karaaslanlı, Selin AviyenteCommunity detection aims to identify densely connected groups of nodes in complex networks. Although a variety of methods have been proposed for community detection, the relationship between them is not well understood. Recently, researchers have shown the equivalence between modularity optimization and likelihood maximization in stochastic block models (SBMs) for static networks. Showing this equivalence

PowerOptimal Distributed Beamforming for MultiCarrier Asynchronous Bidirectional Relay Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201215
Sharareh Kiani, Shahram ShahbazPanahi, Min Dong, Gary BoudreauWe consider the problem of joint power allocation and distributed beamforming design for asynchronous twoway multirelay networks, where two transceivers rely on an orthogonal frequency division multiplexing (OFDM) scheme to exchange information through multiple amplifyandforward relay nodes. The network is assumed to be asynchronous in the sense that different relaying paths are subject to different

Cluster Prediction for Opinion Dynamics From Partial Observations IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201225
Zehong Zhang, Fei LuWe present a Bayesian approach to predict the clustering of opinions for a system of interacting agents from partial observations. The Bayesian formulation overcomes the unobservability of the system and quantifies the uncertainty in the prediction. We characterize the clustering by the posterior of the clusters’ sizes and centers, and we represent the posterior by samples. To overcome the challenge

IEEE Transactions on Signal and Information Processing over Networks publication information IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20210118
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Distributed Training of Graph Convolutional Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201222
Simone Scardapane, Indro Spinelli, Paolo Di LorenzoThe aim of this work is to develop a fullydistributed algorithmic framework for training graph convolutional networks (GCNs). The proposed method is able to exploit the meaningful relational structure of the input data, which are collected by a set of agents that communicate over a sparse network topology. After formulating the centralized GCN training problem, we first show how to make inference

Federated Tobit Kalman Filtering Fusion With DeadZoneLike Censoring and Dynamical Bias Under the RoundRobin Protocol IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201215
Hang Geng, Zidong Wang, Fuad E. Alsaadi, Khalid H. Alharbi, Yuhua ChengThis paper is concerned with the multisensor filtering fusion problem subject to stochastic uncertainties under the RoundRobin protocol (RRP). The uncertainties originate from three sources, namely, censored observations, dynamical biases and additive white noises. To reflect the deadzonelike censoring phenomenon, the measurement observation is described by the Tobit model where the censored region

Weighted Average ConsensusBased Optimization of AdvectionDiffusion Systems IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201215
Saber JafarizadehAs a fundamental algorithm for collaborative processing over multiagent systems, distributed consensus algorithm has been studied for optimizing its convergence rate. Due to the close analogy between the diffusion problem and the consensus algorithm, the previous trend in the literature is to transform the diffusion system from the spatially continuous domain into the spatially discrete one. In this

Adaptive Graph Filters in Reproducing Kernel Hilbert Spaces: Design and Performance Analysis IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201222
Vitor R. M. Elias, Vinay Chakravarthi Gogineni, Wallace A. Martins, Stefan WernerThis paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces. We consider both centralized and fully distributed implementations. We first define nonlinear graph filters that operate on graphshifted versions of the input signal. We then propose a centralized graph kernel least mean squares (GKLMS) algorithm to identify nonlinear graph filters’ model parameters. To reduce

Recursive Secure Filtering Over GilbertElliott Channels in Sensor Networks: The Distributed Case IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201222
Derui Ding, Zidong Wang, QingLong Han, XianMing ZhangThis paper is concerned with the recursive secure filtering problem for a class of discretetime systems subject to unreliable communication due to the security vulnerability of sensor networks. The unreliable communication, caused probably by denialofservice cyberattacks, is described by the wellknown GilbertElliott model. The addressed nonlinearities are applicable for some of the most investigated

node2coords: Graph Representation Learning with Wasserstein Barycenters IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201202
Effrosyni Simou, Dorina Thanou, Pascal FrossardIn order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relatively unstable to perturbations of the graph structure. We address these two limitations by proposing node2coords, a representation learning algorithm

Multiscale Representation Learning of Graph Data With Node Affinity IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201215
Xing Gao, Wenrui Dai, Chenglin Li, Hongkai Xiong, Pascal FrossardGraph neural networks have emerged as a popular and powerful tool for learning hierarchical representation of graph data. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of data in graph neural networks. However, most recent graph pooling methods still fail to efficiently exploit the geometry of graph data. In this paper, we propose

List of Reviewers IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201222
Presents a list of reviewers who contributed to this publication in 2020.

A ComputationEfficient Decentralized Algorithm for Composite Constrained Optimization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201116
Qingguo Lü, Xiaofeng Liao, Huaqing Li, Tingwen HuangThis paper focuses on solving the problem of composite constrained convex optimization with a sum of smooth convex functions and nonsmooth regularization terms ( ${\ell _1}$ norm) subject to locally general constraints. Motivated by the modern largescale information processing problems in machine learning (the samples of a training dataset are randomly decentralized across multiple computing nodes)

Joint Forecasting and Interpolation of TimeVarying Graph Signals Using Deep Learning IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201124
Gabriela Lewenfus, Wallace A. Martins, Symeon Chatzinotas, Björn OtterstenWe tackle the problem of forecasting networksignal snapshots using past signal measurements acquired by a subset of network nodes. This task can be seen as a combination of multivariate timeseries forecasting (temporal prediction) and graphsignal interpolation (spatial prediction). This is a fundamental problem for many applications wherein deploying a high granularity network is impractical. Our

Anytime Minibatch With Delayed Gradients IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201215
Haider AlLawati, Stark C. DraperDistributed optimization is widely deployed in practice to solve a broad range of problems. In a typical asynchronous scheme, workers calculate gradients with respect to outofdate optimization parameters while the master uses stale (i.e., delayed) gradients to update the parameters. While using stale gradients can slow the convergence, asynchronous methods speed up the overall optimization with respect

BandwidthConstrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusion IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201116
Domenico Ciuonzo, S. Hamed Javadi, Abdolreza Mohammadi, Pierluigi Salvo RossiDecentralized detection is one of the key tasks that a wireless sensor network (WSN) is faced to accomplish. Among several decision criteria, the Rao test is able to cope with an unknown (but parametricallyspecified) sensing model, while keeping computational simplicity. To this end, the Rao test is employed in this paper to fuse multivariate data measured by a set of sensor nodes, each observing

Convex Combination of Diffusion Strategies Over Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201116
Danqi Jin, Jie Chen, Cédric Richard, Jingdong Chen, Ali H. SayedCombining diffusion strategies with complementary properties enables enhanced performance when they can be run simultaneously. In this article, we first propose two schemes for the convex combination of two diffusion strategies, namely, the powernormalized scheme and the signregressor scheme. Then, we conduct theoretical analysis for one of the schemes, i.e., the powernormalized one. An analysis

Distributed State Estimation Under Random Parameters and Dynamic Quantizations Over Sensor Networks: A Dynamic EventBased Approach IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201119
Shaoying Wang, Zidong Wang, Hongli Dong, Yun ChenThis paper deals with the distributed state estimation problem for an array of discrete timevarying systems over sensor networks under dynamic eventbased transmission scheme (DETS), random parameter matrices (RPMs) and dynamic measurement quantization (DMQ). Different from the existing static eventbased transmission scheme with fixed threshold, the employed DETS introduces an auxiliary offset variable

Graph Learning for Spatiotemporal Signals With Long and ShortTerm Characterization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201117
Yueliang Liu, Wenbin Guo, Kangyong You, Lei Zhao, Tao Peng, Wenbo WangMining natural associations from highdimensional spatiotemporal signals plays an important role in various fields including biology, climatology, and financial analysis. However, most existing works have mainly studied timeindependent signals without considering the correlations of spatiotemporal signals that achieve high learning accuracy. This paper aims to learn graphs that better reflect underlying

Robust Deep Graph Based Learning for Binary Classification IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20201127
Minxiang Ye, Vladimir Stankovic, Lina Stankovic, Gene CheungConvolutional neural network (CNN)based feature learning has become the stateoftheart for many applications since, given sufficient training data, CNN can significantly outperform traditional methods for various classification tasks. However, feature learning is more challenging if training labels are noisy as CNN tends to overfit to the noisy training labels, resulting in subpar classification

MultiSlot Distributed Measurement Selection: A Sparsity Learning Approach IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200911
Qian Xia, Wei Wang, Rong Ran, Yi Gong, Zhaoyang ZhangWhen only a part of measurements are collected sequentially in largescale Internet of Things (IoT) networks, it is motivated to extract significant information from the collected data for utilizing the communication resources over incoming slots more efficiently. In this article, we study the successive measurement selection problem for sparse parameter estimation and its distributed implementation

Massively Distributed Graph Distances IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200907
Armin Moharrer, Jasmin Gao, Shikun Wang, José Bento, Stratis IoannidisGraph distance (or similarity) scores are used in several graph mining tasks, including anomaly detection, nearest neighbor and similarity search, pattern recognition, transfer learning, and clustering. Graph distances that are metrics and, in particular, satisfy the triangle inequality, have theoretical and empirical advantages. Wellknown graph distances that are metrics include the chemical or the

Ambient Noise Tomography With Common Receiver Clusters in Distributed Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200825
Sili Wang, Fangyu Li, Mark Panning, Saikiran Tharimena, Steve Vance, WenZhan SongNearsurface imaging with distributed sensor networks (DSN) is promising for planet exploration, which affordably generates a nearsurface velocity model. Recently, an Eikonal tomographybased ambient noise seismic imaging (ANSI) algorithm was implemented in a DSN to realize realtime and insitu nearsurface imaging. However, only using data from neighbors to generate a velocity map cannot have enough

FastandSecure StateEstimation in DynamicControl Over Communication Channels: A GameTheoretical Viewpoint IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200820
Makan Zamanipour“Control over noisy communicationchannels” invented by SahaiMitterandTatikonda is a prominent topic. In this context, the latencyandreliability tradeoff is considered by responding to the following: How much fast? How much secure? For a stochasticmeanfieldgame (SMFG), we assign the sourcecodes as the agents. Additionally, the totalReward is the Volume of the maximum secure lossy sourcecodingrate

On Inference of Network Topology and Confirmation Bias in CyberSocial Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200907
Yanbing Mao, Emrah AkyolThis article studies topology inference, from agent states, of a directed cybersocial network with opinion spreading dynamics model that explicitly takes confirmation bias into account. The cybersocial network comprises a set of partially connected directed network of agents at the social level, and a set of information sources at the cyber layer. The necessary and sufficient conditions for the existence

Sensor Selection and Design for Binary Hypothesis Testing in the Presence of a Cost Constraint IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200813
Berkay Oymak, Berkan Dulek, Sinan GeziciWe consider a sensor selection problem for binary hypothesis testing with costconstrained measurements. Random outputs related to a parameter vector of interest are assumed to be generated by a linear system corrupted with Gaussian noise. The aim is to decide on the state of the parameter vector based on a set of measurements collected by a limited number of sensors. The cost of each sensor measurement

Gaussian Mixture Particle JumpMarkovCPHD Fusion for Multitarget Tracking Using Sensors With Limited Views IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200813
Kai Da, Tiancheng Li, Yongfeng Zhu, Qiang FuIn this article, we propose a multisensor cardinalized probability density hypothesis (CPHD) filter for tracking an unknown number of targets that may maneuver over time by using a sensor network with partially overlapping fields of views (POFoVs). We develop a novel, Gaussian mixture particle (GMP) implementation of the jumpMarkov CPHD filter to deal with highly nonlinear/nonGaussian models and

Extended Adjacency and ScaleDependent Graph Fourier Transform via Diffusion Distances IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200811
Vitor R. M. Elias, Wallace A. Martins, Stefan WernerThis article proposes the augmentation of the adjacency model of networks for graph signal processing. It is assumed that no information about the network is available, apart from the initial adjacency matrix. In the proposed model, additional edges are created according to a Markov relation imposed between nodes. This information is incorporated into the extendedadjacency matrix as a function of

Simultaneous Detection of Multiple Change Points and Community Structures in Time Series of Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200728
Rex C. Y. Cheung, Alexander Aue, Seungyong Hwang, Thomas C. M. LeeIn many complex systems, networks and graphs arise in a natural manner. Often, time evolving behavior can be easily found and modeled using timeseries methodology. Amongst others, two common research problems in network analysis are community detection and changepoint detection. Community detection aims at finding specific substructures within the networks, and changepoint detection tries to find

Lower Bound Accuracy of BearingBased Localization for Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200731
Wenjing Zhong, Xiaoyuan Luo, Xiaolei Li, Jing Yan, Xinping GuanLocalization accuracy is an important indicator to design, and deploy the wireless sensor network. This article quantitatively investigates the bearingbased localization accuracy (BBLA) from the perspective of network geometric structure. The average geometric dilution of precision (AGDOP) is used to model the geometric structurebased BBLA, and is expressed by the geometry matrix which is based on

Interval Observer Design Under Stealthy Attacks and Improved EventTriggered Protocols IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200729
Xin Li, Guoliang Wei, Derui DingIn this article, an interval estimation problem is investigated for a class of discretetime nonlinear networked systems under stealthy attacks. An improved eventtriggered protocol with the timevarying threshold is adopted to govern the received signals of interval observer so as to reduce unnecessary data communication burden. Stealthy attacks occurring in both the sensors and the plant come typically

Recovery of TimeVarying Graph Signals via Distributed Algorithms on Regularized Problems IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200722
Junzheng Jiang, David B. Tay, Qiyu Sun, Shan OuyangThe recovery of missing samples from available noisy measurements is a fundamental problem in signal processing. This process is also sometimes known as graph signal inpainting, reconstruction, forecasting or inference. Many of the existing algorithms do not scale well with the size of the graph and/or they cannot be implemented efficiently in a distributed manner. In this paper, we develop efficient

Bearing RigidityBased Localizability Analysis for Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200724
Xiaoyuan Luo, Wenjing Zhong, Xiaolei Li, Xinping GuanThe localizability analysis for wireless sensor network is of great significance to network localization, and topology control. In this paper, the localizability problem for the bearingbased localization is investigated. An identification method for bearing rigid component is presented, and the localizability is studied for the determined bearing rigid component. In the identification process for

TACC: TopologyAware Coded Computing for Distributed Graph Processing IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200528
Başak Güler, A. Salman Avestimehr, Antonio OrtegaThis article proposes a coded distributed graph processing framework to alleviate the communication bottleneck in largescale distributed graph processing. In particular, we propose a topologyaware coded computing (TACC) algorithm that has two novel salient features: (i) a topologyaware graph allocation strategy, and (ii) a coded aggregation scheme that combines the intermediate computations for

Exploiting the Agent's Memory in Asymptotic and FiniteTime Consensus Over MultiAgent Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200615
Gianni Pasolini, Davide Dardari, Michel KiefferThis article proposes two average consensus algorithms exploiting the memory of agents. The performance of the proposed as well as of several stateoftheart consensus algorithms is evaluated considering different communication ranges, and evaluating the impact of transmission errors. To compare asymptotic and finitetime average consensus schemes, the $\varepsilon$ convergence time is adopted for

Scalable Data Association for Extended Object Tracking IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200521
Florian Meyer, Moe Z. WinTracking extended objects based on measurements provided by light detection and ranging (LIDAR) and millimeter wave radio detection and ranging (RADAR) sensors is a key task to obtain situational awareness in important applications including autonomous driving and indoor robotics. In this paper, we propose probabilistic data association methods for localizing and tracking of extended objects that originate

On Distributed Estimation in Hierarchical Power Constrained Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200518
Mojtaba Shirazi, Azadeh VosoughiWe consider distributed estimation of a random source in a hierarchical power constrained wireless sensor network. Sensors within each cluster send their measurements to a cluster head (CH). CHs optimally fuse the received signals and transmit to the fusion center (FC) over orthogonal fading channels. To enable channel estimation at the FC, CHs send pilots, prior to data transmission. We derive the

Precoder Feedback Schemes for Robust Interference Alignment With Bounded CSI Uncertainty IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200520
Navneet Garg, Aditya K. Jagannatham, Govind Sharma, Tharmalingam RatnarajahThis article presents limited feedbackbased precoder quantization schemes for Interference Alignment (IA) with bounded channel state information (CSI) uncertainty. Initially, this work generalizes the minmax mean squared error (MSE) framework, followed by the development of robust precoder and decoder designs based on worst case MSE minimization. The proposed precoder and decoder designs capture

Collaborative MultiSensing in Energy Harvesting Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.664) Pub Date : 20200518
Vini Gupta, Swades DeThis article presents an adaptive multisensing (MS) framework for a network of densely deployed solar energy harvesting wireless nodes. Each node is mounted with heterogeneous sensors to sense multiple crosscorrelated slowlyvarying parameters/signals. Inherent spatiotemporal correlations of the observed parameters are exploited to adaptively activate a subset of sensors of a few nodes and turnOFF