A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> ELECTRONICS (Total: 207 journals)
The end of the list has been reached or no journals were found for your choice.
Similar Journals
Journal Cover
IEEE Transactions on Signal and Information Processing over Networks
Number of Followers: 14  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2373-776X
Published by IEEE Homepage  [228 journals]
  • IEEE Signal Processing Society Information

    • Free pre-print version: Loading...

      Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • EDICS: Transactions on Signal and Information Processing Over Networks

    • Free pre-print version: Loading...

      Abstract: Presents the EDICS subject categories for this issue of the publication.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • IEEE SIGNAL PROCESSING SOCIETY

    • Free pre-print version: Loading...

      Abstract: Provides a listing of current committee members and society officers.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Improved Interval Estimation Method for Cyber-Physical Systems Under
           Stealthy Deception Attacks

    • Free pre-print version: Loading...

      Authors: Jianwei Fan;Jun Huang;Xudong Zhao;
      Pages: 1 - 11
      Abstract: This paper investigates the problem of interval estimation for cyber-physical systems subject to stealthy deception attacks. The cyber-physical system is supposed to be compromised by malicious attackers and on the basis of that, a stealthy attack strategy is formulated. Moreover, the stealthiness of the attack strategy against $chi ^{2}$-detector is analyzed. To accomplish interval estimation, the interval observer is designed by the monotone system method. Then, a novel method which combines reachable set analysis with $H_{infty }$ technique is proposed. Theoretical comparison between the monotone system method and the proposed method is presented and it shows that the proposed method is able to improve the estimation accuracy. Finally, two illustrative examples are provided to demonstrate the superiority and effectiveness of the improved method.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Scalable Perception-Action-Communication Loops With Convolutional and
           Graph Neural Networks

    • Free pre-print version: Loading...

      Authors: Ting-Kuei Hu;Fernando Gama;Tianlong Chen;Wenqing Zheng;Zhangyang Wang;Alejandro Ribeiro;Brian M. Sadler;
      Pages: 12 - 24
      Abstract: In this paper, we present a perception-action-communication loop design using Vision-based Graph Aggregation and Inference (VGAI). This multi-agent decentralized learning-to-control framework maps raw visual observations to agent actions, aided by local communication among neighboring agents. Our framework is implemented by a cascade of a convolutional and a graph neural network (CNN/GNN), addressing agent-level visual perception and feature learning, as well as swarm-level communication, local information aggregation and agent action inference, respectively. By jointly training the CNN and GNN, image features and communication messages are learned in conjunction to better address the specific task. We use imitation learning to train the VGAI controller in an offline phase, relying on a centralized expert controller. This results in a learned VGAI controller that can be deployed in a distributed manner for online execution. Additionally, the controller exhibits good scaling properties, with training in smaller teams and application in larger teams. Through a multi-agent flocking application, we demonstrate that VGAI yields performance comparable to or better than other decentralized controllers, using only the visual input modality and without accessing precise location or motion state information.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Deterministic and Randomized Diffusion Based Iterative Generalized Hard
           Thresholding (DiFIGHT) for Distributed Recovery of Sparse Signals

    • Free pre-print version: Loading...

      Authors: Samrat Mukhopadhyay;Mrityunjoy Chakraborty;
      Pages: 25 - 36
      Abstract: In this paper, we propose a distributed iterative hard thresholding algorithm, namely, DiFIGHT, for a network that uses diffusion as the means of intra-network collaboration. Subsequently, we present a modification of the proposed algorithm, namely, MoDiFIGHT, that has lesser communication complexity than DiFIGHT. We additionally propose four different strategies, namely, RP, RNP, RGP$_r$, and RGNP$_r$ that are used to randomly select a subset of nodes for taking part in DiFIGHT/MoDiFIGHT. This gives rise to further reduction in the mean number of communications during the run of the proposed distributed algorithms. We present theoretical estimates of the long run communication per unit time, both for DiFIGHT and MoDiFIGHT, with and without random selection of nodes. Also, we present theoretical analysis of the two proposed algorithms and provide provable bounds on their recovery performance with or without using the random node selection strategies. Finally we use numerical studies to show that both with and without random selections, the proposed algorithms exhibit performances far superior to the consensus based distributed IHT algorithm.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Protocol-Based Fusion Estimator Design for State-Saturated Systems With
           Dead-Zone-Like Censoring Under Deception Attacks

    • Free pre-print version: Loading...

      Authors: Hang Geng;Zidong Wang;Fuad E. Alsaadi;Khalid H. Alharbi;Yuhua Cheng;
      Pages: 37 - 48
      Abstract: This paper is concerned with the protocol-based fusion estimation problem for a class of state-saturated systems subject to dead-zone-like censoring and deception attacks. In order to curb data collisions and ease communication overheads of the shared networks, the weighted try-once-discard protocol is implemented on the sensor-to-filter channel to orchestrate multiple sensors with a prescribed dynamic transmission order. Bernoulli-distributed stochastic variables are utilized to characterize deception attacks initiated by potential adversaries. The well-known Tobit model is leveraged to characterize the dead-zone-like censoring phenomenon where censored regions are constrained by certain left- and right-censoring thresholds. The fusion estimation is implemented via two stages: at the first stage, each sensor sends its observations to the local estimator and, at the second stage, the local estimates are then transmitted to the fusion center so as to generate the fused estimate. The local estimators realize Tobit Kalman filtering algorithms such that certain upper bounds (on the local filtering error covariances) are guaranteed and then minimized via adequately determining filter gains, while the fusion center carries out the fusion estimation by resorting to the federated fusion criterion. Furthermore, the performance of the devised fusion estimator is examined through assessing the boundedness of the upper bound on the fused error covariance. The validity of the fusion estimator is finally shown via a numerical example.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Enhanced Audit Bit Based Distributed Bayesian Detection in the Presence of
           Strategic Attacks

    • Free pre-print version: Loading...

      Authors: Chen Quan;Baocheng Geng;Yunghsiang S. Han;Pramod K. Varshney;
      Pages: 49 - 62
      Abstract: This paper employs an audit bit based mechanism to mitigate the effect of Byzantine attacks on distributed Bayesian detection systems. In this framework, the optimal attacking strategy for strategic attackers is investigated for the traditional audit bit based scheme (TAS) to evaluate the robustness of the system. We show that it is possible for a strategic attacker to degrade the performance of TAS to the system without audit bits. To enhance the robustness of the system in the presence of strategic attackers, we propose an enhanced audit bit based scheme (EAS). The optimal fusion rule for the proposed scheme is derived and the detection performance of the system is evaluated via the probability of error for the system. Simulation results show that the proposed EAS improves the robustness and the detection performance of the system. Moreover, based on EAS, another new scheme called the reduced audit bit based scheme (RAS) is proposed which further improves system performance. We derive the new optimal fusion rule and the simulation results show that RAS outperforms EAS and TAS in terms of both robustness and detection performance of the system. Then, we extend the proposed RAS for a wide-area cluster based distributed wireless sensor networks (CWSNs). Simulation results show that the proposed RAS significantly reduces the communication overhead between the sensors and the FC, which prolongs the lifetime of the network.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets

    • Free pre-print version: Loading...

      Authors: Mingxing Xu;Wenrui Dai;Chenglin Li;Junni Zou;Hongkai Xiong;Pascal Frossard;
      Pages: 63 - 77
      Abstract: Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in graph representation learning. However, existing SGNNs are limited in implementing graph filters with rigid transforms and cannot adapt to signals residing on graphs and tasks at hand. In this paper, we propose a novel class of graph neural networks that realizes graph filters with adaptive graph wavelets. Specifically, the adaptive graph wavelets are learned with neural network-parameterized lifting structures, where structure-aware attention-based lifting operations are developed to jointly consider graph structures and node features. We propose to lift based on diffusion wavelets to alleviate the structural information loss induced by partitioning non-bipartite graphs. By design, the locality and sparsity of the resulting wavelet transform as well as the scalability of the lifting structure are guaranteed. We further derive a soft-thresholding filtering operation by learning sparse graph representations in terms of the learned wavelets, yielding a localized, efficient, and scalable wavelet-based graph filters. To ensure that the learned graph representations are invariant to node permutations, a layer is employed at the input of the networks to reorder the nodes according to their local topology information. We evaluate the proposed networks in both node-level and graph-level representation learning tasks on benchmark citation and bioinformatics graph datasets. Extensive experiments demonstrate the superiority of the proposed networks over existing SGNNs in terms of accuracy, efficiency, and scalability.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Graph Topology Inference With Derivative-Reproducing Property in RKHS:
           Algorithm and Convergence Analysis

    • Free pre-print version: Loading...

      Authors: Mircea Moscu;Ricardo A. Borsoi;Cédric Richard;José-Carlos M. Bermudez;
      Pages: 78 - 91
      Abstract: In many areas such as computational biology, finance or social sciences, knowledge of an underlying graph explaining the interactions between agents is of paramount importance but still challenging. Considering that these interactions may be based on nonlinear relationships adds further complexity to the topology inference problem. Among the latest methods that respond to this need is a topology inference one proposed by the authors, which estimates a possibly directed adjacency matrix in an online manner. Contrasting with previous approaches based on linear models, the considered model is able to explain nonlinear interactions between the agents in a network. The novelty in the considered method is the use of a derivative-reproducing property to enforce network sparsity, while reproducing kernels are used to model the nonlinear interactions. The aim of this paper is to present a thorough convergence analysis of this method. The analysis is proven to be sane both in the mean and mean square sense. In addition, stability conditions are devised to ensure the convergence of the analyzed method.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Secure Transmission Scheme Based on Fingerprint Positioning in Cell-Free
           Massive MIMO Systems

    • Free pre-print version: Loading...

      Authors: Jiahua Qiu;Kui Xu;Xiaochen Xia;Zhexian Shen;Wei Xie;Dongmei Zhang;Meng Wang;
      Pages: 92 - 105
      Abstract: In this paper, we mainly study how to improve the secure transmission performance of cell-free massive multiple-input multiple-output (MIMO) systems by using location technology. In cell-free massive MIMO systems, active pilot attacks will contaminate the uplink channel estimation and affect the downlink precoding of access points (APs). In order to effectively reduce the impact of active pilot attacks on user transmission, this paper respectively proposes an user location estimation method based on fingerprint positioning and a channel estimation algorithm based on location information. Firstly, under the imperfect channel state information, the location information of users and eavesdropper is obtained by using fingerprint positioning method and K-means clustering algorithm. Then, combined with location information, AP selection strategy and channel estimation method based on non-overlapping angle of arrival (AOA) criterion are proposed respectively. Based on the location information of users and eavesdropper, we use discrete Fourier transform (DFT) to distinguish the uplink channels of legitimate user and eavesdropper from the angle domain, thus eliminating the pilot contamination caused by active pilot attacks. The results show that compared with the traditional transmission method, the proposed secure transmission strategy can increase the secrecy rate of up to 2 b/s/Hz, which effectively enhances the secure transmission performance of cell-free massive MIMO in strong interference environment.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Resampling and Network Theory

    • Free pre-print version: Loading...

      Authors: Praveen B. Choppala;Marcus R. Frean;Paul D. Teal;
      Pages: 106 - 119
      Abstract: Particle filtering provides an approximate representation of a tracked posterior density which converges asymptotically to the true posterior as the number of particles used increases. The greater the number of particles, the higher the computational complexity. This complexity can be implemented by operating the particle filter in parallel architectures. However, the resampling step in the particle filter requires a high level of synchronization and extensive information interchange between the particles, which impedes the use of parallel hardware systems. This paper establishes a new perspective for understanding particle filtering — that particle filtering can be achieved by adopting the principles of information exchange within a network, the nodes of which are now the particles in the particle filter. We propose to connect particles via a minimally connected network and resample each locally. This strategy facilitates full information exchange among the particles, but with each particle communicating with only a small fixed set of other particles, thus leading to minimal communication overhead. The key benefit is that this approach facilitates the use of many particles for accurate posterior approximation and tracking accuracy.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • A Multi-Agent Collaborative Environment Learning Method for UAV Deployment
           and Resource Allocation

    • Free pre-print version: Loading...

      Authors: Zhaojun Dai;Yan Zhang;Wancheng Zhang;Xinran Luo;Zunwen He;
      Pages: 120 - 130
      Abstract: The dynamic position deployment and resource allocation of the unmanned aerial vehicle (UAV) communication networks has great significance in terms of interference management, coverage enhancement, and capacity improvement. Since the transmission power and energy resources of the UAVs are limited and the actual communication environment is complex and time-varying, it is challenging for the multiple UAVs to dynamically make decisions to ensure the communication performance of the system. Meanwhile, the centralized architecture may generate a certain degree of communication delay and affect communication efficiency. Facing this challenge, a resource allocation algorithm for the UAV networks based on multi-agent collaborative environment learning is proposed. This method is based on a distributed architecture. Each UAV is modeled as an independent agent, which improves the utility of the UAV networks through the dynamic selection decisions of its deployment position, transmission power, and occupied sub-channels. Each UAV learns the mapping of the network information to the position deployment and resource selection decisions based on the reinforcement learning algorithm according to partial of the state information it can observe. For the overall network, a multi-agent reinforcement learning method based on federated learning is designed on the purpose of realizing information interaction and combined dispatching of the UAVs. In the multi-agent system, the framework of federated learning is introduced to realize the sharing of non-privacy data among the UAVs. Simulation results indicate that the proposed method can effectively improve the network utility compared with the multi-agent deep reinforcement learning algorithm without information interaction.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Decentralized Federated Learning: Balancing Communication and Computing
           Costs

    • Free pre-print version: Loading...

      Authors: Wei Liu;Li Chen;Wenyi Zhang;
      Pages: 131 - 143
      Abstract: Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates. In this paper, we propose a general DFL framework, which implements both multiple local updates and multiple inter-node communications periodically, to strike a balance between communication efficiency and model consensus. It can provide a general decentralized SGD analytical framework. We establish strong convergence guarantees for the proposed DFL algorithm without the assumption of convex objectives. The convergence rate of DFL can be optimized to achieve the balance of communication and computing costs under constrained resources. For improving communication efficiency of DFL, compressed communication is further introduced to the proposed DFL as a new scheme, named DFL with compressed communication (C-DFL). The proposed C-DFL exhibits linear convergence for strongly convex objectives. Experiment results based on MNIST and CIFAR-10 datasets illustrate the superiority of DFL over traditional decentralized SGD methods and show that C-DFL further enhances communication efficiency.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Online Change Point Detection for Weighted and Directed Random Dot Product
           Graphs

    • Free pre-print version: Loading...

      Authors: Bernardo Marenco;Paola Bermolen;Marcelo Fiori;Federico Larroca;Gonzalo Mateos;
      Pages: 144 - 159
      Abstract: Given a sequence of random (directed and weighted) graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution. Our idea is to endow sequential change-point detection (CPD) techniques with a graph representation learning substrate based on the versatile Random Dot Product Graph (RDPG) model. We consider efficient, online updates of a judicious monitoring function, which quantifies the discrepancy between the streaming graph observations and the nominal RDPG. This reference distribution is inferred via spectral embeddings of the first few graphs in the sequence. We characterize the distribution of this running statistic to select thresholds that guarantee error-rate control, and under simplifying approximations we offer insights on the algorithm’s detection resolution and delay. The end result is a lightweight online CPD algorithm, that is also explainable by virtue of the well-appreciated interpretability of RDPG embeddings. This is in stark contrast with most existing graph CPD approaches, which either rely on extensive computation, or they store and process the entire observed time series. An apparent limitation of the RDPG model is its suitability for undirected and unweighted graphs only, a gap we aim to close here to broaden the scope of the CPD framework. Unlike previous proposals, our non-parametric RDPG model for weighted graphs does not require a priori specification of the weights’ distribution to perform inference and estimation. This network modeling contribution is of independent interest beyond CPD. We offer an open-source implementation of the novel online CPD algorithm for weighted and direct graphs, whose effectiveness and efficiency are demonstrated via (reproducible) synthetic and real network data experiments.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Graph-Based Classification With Multiple Shift Matrices

    • Free pre-print version: Loading...

      Authors: Jie Fan;Cihan Tepedelenlioglu;Andreas Spanias;
      Pages: 160 - 172
      Abstract: Due to their effectiveness in capturing similarities between different entities, graphical models are widely used to represent datasets that reside on irregular and complex manifolds. Graph signal processing offers support to handle such complex datasets. In this paper, we propose a novel graph filter design method for semi-supervised data classification. The proposed design uses multiple graph shift matrices, one for each feature, and is shown to provide improved performance when the feature qualities are uneven. We introduce three methods to optimize for the graph filter coefficients and the graph combining coefficients. The first method uses the alternating minimization approach. In the second method, we optimize our objective function by convex relaxation that provides a performance benchmark. The third method adopts a genetic algorithm, which is computationally efficient and better at controlling overfitting. In our simulation experiments, we use both synthetic and real datasets with informative and non-informative features. Monte Carlo simulations demonstrate the effectiveness of multiple graph shift operators in the graph filters. Significant improvements in comparison to conventional graph filters are shown, in terms of average error rate and confidence scores. Furthermore, we perform cross validation to show how our approach can control overfitting and improve generalization performance.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Joint State and Fault Estimation of Complex Networks Under Measurement
           Saturations and Stochastic Nonlinearities

    • Free pre-print version: Loading...

      Authors: Yang Liu;Zidong Wang;Lei Zou;Donghua Zhou;Wen-Hua Chen;
      Pages: 173 - 186
      Abstract: In this paper, the joint state and fault estimation problem is investigated for a class of discrete-time complex networks with measurement saturations and stochastic nonlinearities. The difference between the actual measurement and the saturated measurement is regarded as an unknown input and the system is thus re-organized as a singular system. An appropriate estimator is designed for each node which aims to estimate the system states and the loss of the actuator effectiveness simultaneously. In the presence of measurement saturations and stochastic nonlinearities, upper bounds of the error covariances of the fault estimates are recursively obtained and then minimized. Sufficient conditions are proposed to guarantee the existence, unbiasedness, and boundeness of the developed estimator. Our developed estimator design algorithm is distributed because it depends only on the local information and the information from the neighboring nodes, thereby avoiding the usage of a center estimator. Finally, simulation results are presented to show the performance of the proposed strategy in simultaneously estimating the states and faults.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Quantized Distributed Estimation With Adaptive Combiner

    • Free pre-print version: Loading...

      Authors: Xiaoxian Lao;Wenfei Du;Chunguang Li;
      Pages: 187 - 200
      Abstract: Distributed estimation over networks has attracted great attention for its wide applicability in source tracking, environmental monitoring, etc. In the problem of distributed estimation, a set of nodes collectively estimates some parameter from noisy measurements. Most distributed algorithms assume that the information shared among nodes has full-precision. However, this assumption is unrealistic as communication channels among nodes have limited bandwidth. Considering this, in this paper, we impose quantization constraints on distributed estimation problem. That is, nodes can only receive quantized information from neighbors. The quantization inevitably brings in errors. Each node has to decide carefully which information provided by the neighbors is more trustworthy since these information could suffer from various degrees of distortion caused by the errors. To make good use of the aggregated information, we propose an adaptive combination strategy for quantized distributed estimation algorithms. We derive sufficient conditions for convergence of the resulting algorithm through mean-square analysis. The advantages of the proposed adaptive combiner over fixed combiners are illustrated experimentally.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Reconstruction of Time-Varying Graph Signals via Sobolev Smoothness

    • Free pre-print version: Loading...

      Authors: Jhony H. Giraldo;Arif Mahmood;Belmar Garcia-Garcia;Dorina Thanou;Thierry Bouwmans;
      Pages: 201 - 214
      Abstract: Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-world graph signals are inherently time-varying and the smoothness of the temporal differences of such graph signals may be used as a prior assumption. In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples. We explore some theoretical aspects of the convergence rate of our Time-varying Graph signal Reconstruction via Sobolev Smoothness (GraphTRSS) algorithm by studying the condition number of the Hessian associated with our optimization problem. Our algorithm has the advantage of converging faster than other methods that are based on Laplacian operators without requiring expensive eigenvalue decomposition or matrix inversions. The proposed GraphTRSS is evaluated on several datasets including two COVID-19 datasets and it has outperformed many existing state-of-the-art methods for time-varying graph signal reconstruction. GraphTRSS has also shown excellent performance on two environmental datasets for the recovery of particulate matter and sea surface temperature signals.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Statistical Consistency for Change Point Detection and Community
           Estimation in Time-Evolving Dynamic Networks

    • Free pre-print version: Loading...

      Authors: Cong Xu;Thomas C. M. Lee;
      Pages: 215 - 227
      Abstract: Suppose a time sequence of networks is observed. It is known that the probabilistic behaviors of the networks do not change over time, except at a few time points. These time points are usually called change points, whose number and locations are unknown. This paper proposes a method for automatically estimating such change points and the community structures of the networks. The proposed method invokes the minimum description length principle to derive a model selection criterion, where the best estimates are defined as its minimizer. It is shown that this selection criterion yields consistent estimates for the change points as well as the community structures. For practical minimization of the selection criterion, a bottom-up search algorithm that combines the EM-algorithm with variational approximation is developed. The promising empirical properties of the proposed method are illustrated via a sequence of numerical experiments and applications to some real datasets. To the best of the authors’ knowledge, this method is one of the earliest that provides consistent estimates in the context of change point detection for time-evolving networks.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Revisiting the Primal-Dual Method of Multipliers for Optimisation Over
           Centralised Networks

    • Free pre-print version: Loading...

      Authors: Guoqiang Zhang;Kenta Niwa;W. Bastiaan Kleijn;
      Pages: 228 - 243
      Abstract: The primal-dual method of multipliers (PDMM) was originally designed for solving a decomposable optimisation problem over a general network. In this paper, we revisit PDMM for optimisation over a centralised network. We first note that the recently proposed method FedSplit [1] implements PDMM for a centralised network. In [1], Inexact FedSplit (i.e., gradient based FedSplit) was also studied both empirically and theoretically. We identify the cause for the poor reported performance of Inexact FedSplit, which is due to the suboptimal initialisation in the gradient operations at the client side. To fix the issue of Inexact FedSplit, we propose two versions of Inexact PDMM, which are referred to as gradient-based PDMM (GPDMM) and accelerated GPDMM (AGPDMM), respectively. AGPDMM accelerates GPDMM at the cost of transmitting two times the number of parameters from the server to each client per iteration compared to GPDMM. We provide a new convergence bound for GPDMM for a class of convex optimisation problems. Our new bounds are tighter than those derived for Inexact FedSplit. We also investigate the update expressions of AGPDMM and SCAFFOLD to find their similarities. It is found that when the number K of gradient steps at the client side per iteration is K=1, both AGPDMM and SCAFFOLD reduce to vanilla gradient descent with proper parameter setup. Experimental results indicate that AGPDMM converges faster than SCAFFOLD when K>1 while GPDMM converges slightly slower than SCAFFOLD.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • A Method Exploiting the Channel-Training Phase to Achieve Secrecy in a
           Fading Broadcast Channel

    • Free pre-print version: Loading...

      Authors: Gustavo Anjos;Daniel Castanheira;Adão Silva;Atílio Gameiro;
      Pages: 244 - 257
      Abstract: This work considers a time-varying Rayleigh block fading broadcast channel formed by two users and an eavesdropper, external to the legitimate network. One of the users is the legitimate/trusted user and the other an untrusted user. The transmitter uses superposition coding to broadcast information to both users. The aim is to ensure the confidentiality of the legitimate user information against the untrusted user and eavesdropper. To satisfy these secrecy constraints, this work proposes an innovative secure channel-training method applied to a cooperative jamming scenario. The jammer uses interference alignment to achieve secrecy against the untrusted user; meanwhile, the developed channel-training method is designed exploiting the time-varying nature of the channel to extend the system secrecy also to the external eavesdropper. An information theoretical evaluation of the proposed scheme demonstrates that (1/4, 1/4) positive secure degrees of freedom are achievable in the presence of both untrusted users and external eavesdroppers.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Adaptive Event-Triggered Quantized Communication-Based Distributed
           Estimation Over Sensor Networks With Semi-Markovian Switching Topologies

    • Free pre-print version: Loading...

      Authors: Fengzeng Zhu;Ju H. Park;Li Peng;
      Pages: 258 - 272
      Abstract: This paper presents a distributed state estimation method for nonlinear systems over sensor networks with Semi-Markovian switching topologies (S-MSTs). An adaptive event-triggered quantization scheme (AETQS) is developed to reduce the communication and computation burden for bandwidth-constrained sensor networks, where the quantified measurement data is determined by the specific event triggering condition. The filtering network topology evolves over time, which is assumed to be governed by a Semi-Markov chain. Based on the Semi-Markov kernel theory and Lyapunov stability theory, sufficient conditions are obtained to guarantee that the error dynamics has $sigma$-error mean square stability and ${H_infty }$ performance, which is given in the form of linear matrix inequalities. Then, the optimal disturbance attenuation level, initial triggering thresholds, and elapsed-time-dependent distributed filter gains can be determined by addressing a convex optimization problem. Finally, two numerical examples are presented to verify the effectiveness of the proposed approach.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Learning Graphs From Smooth and Graph-Stationary Signals With Hidden
           Variables

    • Free pre-print version: Loading...

      Authors: Andrei Buciulea;Samuel Rey;Antonio G. Marques;
      Pages: 273 - 287
      Abstract: Network-topology inference from (vertex) signal observations is a prominent problem across data-science and engineering disciplines. Most existing schemes assume that observations from all nodes are available, but in many practical environments, only a subset of nodes is accessible. A natural (and sometimes effective) approach is to disregard the role of unobserved nodes, but this ignores latent network effects, deteriorating the quality of the estimated graph. Differently, this paper investigates the problem of inferring the topology of a network from nodal observations while taking into account the presence of hidden (latent) variables. Our schemes assume the number of observed nodes is considerably larger than the number of hidden variables and build on recent graph signal processing models to relate the signals and the underlying graph. Specifically, we go beyond classical correlation and partial correlation approaches and assume that the signals are smooth and/or stationary in the sought graph. The assumptions are codified into different constrained optimization problems, with the presence of hidden variables being explicitly taken into account. Since the resulting problems are ill-conditioned and non-convex, the block matrix structure of the proposed formulations is leveraged and suitable convex-regularized relaxations are presented. Numerical experiments over synthetic and real-world datasets showcase the performance of the developed methods and compare them with existing alternatives.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Permutation Entropy for Graph Signals

    • Free pre-print version: Loading...

      Authors: John Stewart Fabila-Carrasco;Chao Tan;Javier Escudero;
      Pages: 288 - 300
      Abstract: Entropy metrics (for example, permutation entropy) are nonlinear measures of irregularity in time series (one-dimensional data). Some of these entropy metrics can be generalised to data on periodic structures such as a grid or lattice pattern (two-dimensional data) using its symmetry, thus enabling their application to images. However, these metrics have not been developed for signals sampled on irregular domains, defined by a graph. Here, we define for the first time an entropy metric to analyse signals measured over irregular graphs by generalising permutation entropy, a well-established nonlinear metric based on the comparison of neighbouring values within patterns in a time series. Our algorithm is based on comparing signal values on neighbouring nodes, using the adjacency matrix. We show that this generalisation preserves the properties of classical permutation for time series and the recent permutation entropy for images, and it can be applied to any graph structure with synthetic and real signals. We expect the present work to enable the extension of other nonlinear dynamic approaches to graph signals.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Modeling and Analysis of Opinion Dynamics in Social Networks Using
           Multiple-Population Mean Field Games

    • Free pre-print version: Loading...

      Authors: Reginald A. Banez;Hao Gao;Lixin Li;Chungang Yang;Zhu Han;H. Vincent Poor;
      Pages: 301 - 316
      Abstract: The dominanceof social networks has advanced immensely as many users become more dependent on these networks to be able to engage on social discussions and activities. The behavior of these users about a specific topic or issue can be extracted from their own belief or opinion as well as that of their connections. In order to derive meaningful and important behavioral information, these users can be modeled and analyzed together according to similarities in attributes such as political orientation, race, gender, and age. In this research work, the opinion dynamics of a multiple-population social network is investigated through the application of multiple-population mean field game (MPMFG) for behavior modeling and analysis. As a consequence of the proposed MPMFG model, information can be gained on the behavior of social network users belonging to different populations or groups. Specifically, the proposed MPFMG model can be utilized to estimate and predict the behavior of a social network group as well as their effect on the belief and opinion of other groups. Simulations are provided to demonstrate the belief and opinion dynamics of social network users in multiple-population settings. Moreover, theoretical and experimental results as well as comprehensive performance analysis are presented to demonstrate the effectiveness and validity of the proposed MPMFG approach in modeling and analyzing the evolution of opinions in multiple-population social networks.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Distributed Detection Fusion in Clustered Sensor Networks Over Multiple
           Access Fading Channels

    • Free pre-print version: Loading...

      Authors: Sami A. Aldalahmeh;Domenico Ciuonzo;
      Pages: 317 - 329
      Abstract: In this paper, we tackle decision fusion for distributed detection in a randomly-deployed clustered Wireless Sensor Networks (WSNs) operating over a non-ideal multiple access channels (MACs), i.e. considering Rayleigh fading, path loss and additive noise. To mitigate fading, we propose the distributed equal gain transmit combining (dEGTC) and distributed maximum ratio transit combining (dMRTC). The first and second order statistics of the received signals were analytically computed via stochastic geometry tools. Then the distribution of the received signal over the MAC are approximated by Gaussian and log-normal distributions via moment matching. This enabled the derivation of moment matching optimal fusion rules (MOR) for both distributions. Moreover, suboptimal simpler fusion rules were also proposed, in which all the CHs data are equally weighed, which is termed moment matching equal gain fusion rule (MER). It is shown by simulations that increasing the number of clusters improve the performance. Moreover, MOR-Gaussian based algorithms are better under free-space propagation whereas their lognormal counterparts are more suited in the ground-reflection case. Also, the latter algorithms show better results in low SNR and SN numbers conditions. We have proved that the received power at the CH in MAC is proportional $mathcal {O}(lambda ^{2} R^{2})$ and to $mathcal {O}(lambda ^{2} ln ^{2} R)$ in the free-space propagation and the ground-reflection cases respectively, where $lambda$ is SN deployment intensity and $R$ is the cluster radius. This implies that having more clusters decreases the required transmission power for a given SNR at the receiver.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • $k$ -Center+From+Noisy+Distance+Samples&rft.title=IEEE+Transactions+on+Signal+and+Information+Processing+over+Networks&rft.issn=2373-776X&rft.date=2022&rft.volume=8&rft.spage=330&rft.epage=343&rft.aulast=Moharir;&rft.aufirst=Neharika&rft.au=Neharika+Jali;Nikhil+Karamchandani;Sharayu+Moharir;">Greedy $k$ -Center From Noisy Distance Samples

    • Free pre-print version: Loading...

      Authors: Neharika Jali;Nikhil Karamchandani;Sharayu Moharir;
      Pages: 330 - 343
      Abstract: We study a variant of the canonical $k$-center problem over a set of vertices in a metric space, where the underlying distances are apriori unknown. Instead, we can query an oracle which provides noisy/incomplete estimates of the distance between any pair of vertices. We consider two oracle models: Dimension Sampling where each query to the oracle returns the distance between a pair of points in one dimension; and Noisy Distance Sampling where the oracle returns the true distance corrupted by noise. We propose active algorithms, based on ideas such as UCB, Thompson Sampling and Track-and-Stop developed in the closely related Multi-Armed Bandit problem, which adaptively decide which queries to send to the oracle and are able to solve the $k$-center problem within an approximation ratio of two with high probability. We analytically characterize instance-dependent query complexity of our algorithms and also demonstrate significant improvements over naive implementations via numerical evaluations on two real-world datasets (Tiny ImageNet and UT Zappos50 K).
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Optimal Memory Scheme for Accelerated Consensus Over Multi-Agent Networks

    • Free pre-print version: Loading...

      Authors: Jiahao Dai;Jing-Wen Yi;Li Chai;
      Pages: 344 - 352
      Abstract: Consensus overmulti-agent networks can be accelerated by utilizing agent’s memory to the control protocol. In this paper, a general protocol with memory information from the node and its neighbors is designed. We aim to provide an optimal memory scheme to accelerate consensus. The contributions of this paper include: (i) For the one-tap memory scheme, we prove that the memory information of neighbors is unnecessary for the optimal convergence. (ii) It is proved that in the worst-case scenario, one-tap node memory is sufficient to achieve the optimal convergence rate, that is, adding more taps of past information from neighbors can not improve the rate further. (iii) It is found that the convergence rate with two-tap memory can be further improved on star networks. Numerical examples are presented to illustrate the validity and correctness of the obtained results.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Wasserstein-Based Graph Alignment

    • Free pre-print version: Loading...

      Authors: Hermina Petric Maretic;Mireille El Gheche;Matthias Minder;Giovanni Chierchia;Pascal Frossard;
      Pages: 353 - 363
      Abstract: A novel method for comparing non-aligned graphs of various sizes is proposed, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices. Specifically, a new formulation for the one-to-many graph alignment problem is casted, which aims at matching a node in the smaller graph with one or more nodes in the larger graph. By incorporating optimal transport into our graph comparison framework, a structurally-meaningful graph distance, and a signal transportation plan that models the structure of graph data are generated. The resulting alignment problem is solved with stochastic gradient descent, where a novel Dykstra operator is used to ensure that the solution is a one-to-many (soft) assignment matrix. The performance of our novel framework is demonstrated on graph alignment, graph classification and graph signal transportation. Our method is shown to lead to significant improvements with respect to the state-of-the-art algorithms on each ofthese tasks.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Deep Reinforcement Learning-Based Cloud-Edge Collaborative Mobile
           Computation Offloading in Industrial Networks

    • Free pre-print version: Loading...

      Authors: Siguang Chen;Jiamin Chen;Yifeng Miao;Qian Wang;Chuanxin Zhao;
      Pages: 364 - 375
      Abstract: With the rapid development of mobile industrial applications and due to the limited coverage of static edge servers, traditional edge computing technology has great limitations in dynamic environmental applications. This paper proposes a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading mechanism for satisfying the dynamic service requirements in industrial networks. Specifically, a three-layer network model of digital twins and a decentralized network of task resources are first constructed to handle the mobility of user terminals and the relevance of tasks. Then, based on the comprehensive consideration of mobility, associated tasks, computing resources and offloading decisions, an optimization problem is formulated to minimize the weighted sum of the execution delay and energy consumption of all tasks for all users. Additionally, a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) algorithm is proposed to solve this optimization problem. Based on the differences in each edge cloud, this algorithm sets the priority of the shared experience pool and selects the most effective experience samples to complete better learning and training. It also utilizes a distributed learning method to learn the probability of an approximate reward distribution and optimizes network parameters through cloud-edge collaboration to achieve faster optimal offloading decision. Finally, a large number of simulation results show that the proposed algorithm has the characteristics of fast convergence and high stability, and it can obtain the optimal offloading decision with the lowest total cost.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Hyper-Laplacian Regularized Nonconvex Low-Rank Representation for
           Multi-View Subspace Clustering

    • Free pre-print version: Loading...

      Authors: Shuqin Wang;Yongyong Chen;Linna Zhang;Yigang Cen;Viacheslav Voronin;
      Pages: 376 - 388
      Abstract: Multi-view subspace clustering methods used consensus and supplementary principles to learn the shared self-representation matrix or tensor have been applied to multiple fields. The existing advanced multi-view subspace clustering methods are mainly based on the extension of low-rank representation from matrix to tensor. However, the tensor optimization methods have two limitations: they cannot retain the local geometric structure of data features residing in multiple nonlinear subspaces; they represent the low-rank structure based on the tensor nuclear norm, which will cause undesirable low-rank approximation. To solve these problems, we propose a hyper-Laplacian regularized Nonconvex Low-rank Representation (HNLR) method for multi-view subspace clustering. HNLR uses hyper-Laplacian regularizer to capture the high-order local geometry structure of each view. In addition, by introducing a nonconvex Laplace function to replace the tensor nuclear norm, HNLR can greatly improve the approximate performance of the global low-rank structure. Based on the alternating direction method of multiplier, we design an effective alternate iteration strategy to optimize HNLR model. Experimental results on eight real datasets have proved the superiority of our proposed method.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Gaussian Kernel Variance for an Adaptive Learning Method on Signals Over
           Graphs

    • Free pre-print version: Loading...

      Authors: Yue Zhao;Ender Ayanoglu;
      Pages: 389 - 403
      Abstract: This paper discusses a special kind of a simple yet possibly powerful algorithm, called single-kernel Gradraker (SKG), which is an adaptive learning method predicting unknown nodal values in a network using known nodal values and the network structure. We aim to find out how to configure the special kind of the model in applying the algorithm. To be more specific, we focus on SKG with a Gaussian kernel and specify how to find a suitable variance for the kernel. To do so, we introduce two variables with which we are able to set up requirements on the variance of the Gaussian kernel to achieve (near-) optimal performance and can better understand how SKG works. Our contribution is that we introduce two variables as analysis tools, illustrate how predictions will be affected under different Gaussian kernels, and provide an algorithm finding a suitable Gaussian kernel for SKG with knowledge about the training network. Simulation results on real datasets are provided.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Staleness Analysis in Asynchronous Optimization

    • Free pre-print version: Loading...

      Authors: Haider Al-Lawati;Stark Draper;
      Pages: 404 - 420
      Abstract: Distributed optimization is widely used to solve large-scale optimization problems by parallelizing gradient-based algorithms across multiple computing nodes. In asynchronous optimization, the optimization parameter is updated using stale gradients, which are gradients computed with respect to outdated parameters. Although large degrees of staleness can slow convergence, little is known about the impact of staleness and its relation to other system parameters. In this work, we analyze asynchronous optimization when implemented using either hub-and-spoke or shared memory architectures. We show that the process of gradient arrival to the master node is similar in nature to a renewal process. We derive the bandwidth requirement of the system. For the hub-and-spoke setup, we derive bounds on the expected gradient staleness and show its connection to other system parameters such as the number of workers, expected compute time, and communication delays. Our derivations reveal that it is possible to adjust gradient staleness by tuning certain parameters such as minibatch size or the number of workers. For the shared memory architecture, we show that the expected staleness is equivalent to the number of workers. Our derivations can be used in existing convergence analyses to express convergence rates in terms of other known system parameters. Such an expression gives further details on what factors impact convergence.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Strategic DoS Attack in Continuous Space for Cyber-Physical Systems Over
           Wireless Networks

    • Free pre-print version: Loading...

      Authors: Mengyu Huang;Kam Fai Elvis Tsang;Yuzhe Li;Li Li;Ling Shi;
      Pages: 421 - 432
      Abstract: In cyber-physical systems (CPSs), it is typical that a sensor observes a dynamical process and transmits the state estimate to a remote estimator wirelessly. Security risks arise when a denial-of-service (DoS) attacker generates extra noise at some power level to reduce the successful transmission rate. Investigating the capability of such an attacker to endanger the system is an important research line in CPS security. However, most previous works have two restrictions, one is that the attacker has complete knowledge of the system, which is usually difficult to achieve, and the other is that the attack power level set is small and discrete, which reduces the attack effectiveness and is hard to be implemented in multi-process systems due to the curse of dimensionality. In this paper, we tackle these restrictions by establishing a continuous attack power design for a DoS attacker with limited information. We propose deep deterministic policy gradient (DDPG)-based attack designs in single-process and multi-process systems, respectively. Numerical simulations illustrate the advantages of DDPG-based attack designs over heuristic baselines and existing learning methods.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • An Efficient Distributed Kalman Filter Over Sensor Networks With Maximum
           Correntropy Criterion

    • Free pre-print version: Loading...

      Authors: Chen Hu;Badong Chen;
      Pages: 433 - 444
      Abstract: We consider the distributed Kalman filtering (DKF) with non-Gaussian noises problem, where each sensor exchanges information between its neighbors with limited communication. Inspired by the ability to capture higher-order statistics of maximum correntropy criterion (MCC) to deal with non-Gaussian noises, we utilizes a matrix weight instead of a scalar obtained by MCC to improve the estimation performance comparing with existing MCC based DKFs. We approximate the centralized estimate by the covariance intersection method, and propose a new MCC based distributed Kalman filter, named CI-DMCKF. The proposed algorithm only needs to communicate once with neighbors in a sampling period, which is more efficient for low bandwidth communication than existing MCC based DKFs. Under the condition of global observability, we show that the consistency, stability, and asymptotic unbiasedness properties of proposed CI-DMCKF algorithm. Finally, we experimentally demonstrate the effectiveness of the proposed algorithm on a cooperating target tracking task.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Distributed Adaptive Tobit Kalman Filter for Networked Systems Under
           Sensor Delays and Censored Measurements

    • Free pre-print version: Loading...

      Authors: Jiahao Zhang;Su Zhao;
      Pages: 445 - 458
      Abstract: The distributed adaptive Tobit Kalman filter (DATKF) is derived in this article for the discrete time networked system with multiple sensors under sensor delays and censored measurements. In the modified measurement model, the phenomena of sensor delays and censored measurements are characterized by the random variables, which obey Bernoulli distribution. Then, based on measurement residual and modified probability density function (pdf) of measurement variables, an adaptive probability selection strategy is derived to eliminate the approximate error and initial error for censoring probability and time-delay probability, respectively. Next, based on weighted average consensus (WAC), the DATKF is provided for the discrete time networked system to obtain the fused state estimates. The adaptive Tobit Kalman filter (ATKF) is selected as the local state estimator, and the filtering error covariance of ATKF is acquired through searching its upper bound to eliminate the approximate error of the filtering gain. To enhance the precision of information fusion within limited consensus steps, the weighted rule is derived on the foundation of the measurement residual and censoring probability. Finally, the filtering accuracy and computation efficiency are verified for DATKF through several simulations.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Distributed Extended Object Tracking Using Coupled Velocity Model From WLS
           Perspective

    • Free pre-print version: Loading...

      Authors: Zhifei Li;Yan Liang;Linfeng Xu;
      Pages: 459 - 474
      Abstract: This study proposes a coupled velocity model (CVM) that establishes the relation between the orientation and velocity using their correlation, avoiding that the existing extended object tracking (EOT) models treat them as two independent quantities. As a result, CVM detects the mismatch between the prior dynamic model and actual motion pattern to correct the filtering gain, and simultaneously becomes a nonlinear and state-coupled model with multiplicative noise. The study considers CVM to design a feasible distributed weighted least squares (WLS) filter. The WLS criterion requires a linear state-space model containing only additive noise about the estimated state. To meet the requirement, we derive such two separate pseudo-linearized models by using the first-order Taylor series expansion. The separation is merely in form, and the estimates of interested states are embedded as parameters into each other’s model, which implies that their interdependency is still preserved in the iterative operation of two linear filters. With the two models, we first propose a centralized WLS filter by converting the measurements from all nodes into a summation form. Then, a distributed consensus scheme, which directly performs an inner iteration on the priors across different nodes, is proposed to incorporate the cross-covariances between nodes. Under the consensus scheme, a distributed WLS filter over a realistic network with “naive” node is developed by proper weighting of the priors and measurements. Finally, the performance of proposed filters in terms of accuracy, robustness, and consistency is testified under different prior situations.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Fair Contrastive Learning on Graphs

    • Free pre-print version: Loading...

      Authors: Oyku Deniz Kose;Yanning Shen;
      Pages: 475 - 488
      Abstract: Node representation learning plays a critical role in learning over graphs. Specifically, the success of contrastive learning methods in unsupervised node representation learning has been demonstrated for various tasks, which has led to increase in attention towards the field. Despite the increasing popularity, fairness is widely under-explored in the area. Motivated by this, this study proposes novel fairness-aware graph augmentations based on adaptive feature masking and edge deletion, in order to mitigate the bias in graph contrastive learning. Different fairness notions on graphs are introduced in the study to guide the designs of the proposed adaptive augmentation schemes. Moreover, it is quantitatively shown that the proposed feature masking scheme can reduce the intrinsic bias. Experimental results on four real-world networks are presented to show that the introduced augmentation frameworks can improve group fairness measures together with comparable classification accuracy to state-of-the-art graph contrastive learning studies for node classification.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Model-Free Adaptive Control for Nonlinear Multi-Agent Systems With
           Encoding-Decoding Mechanism

    • Free pre-print version: Loading...

      Authors: Shuhua Zhang;Lifeng Ma;Xiaojian Yi;
      Pages: 489 - 498
      Abstract: This paper is concerned with the consensus tracking problem for a class of nonlinear discrete-time multi-agent systems (MASs). The dynamic linearization method is used to approximate the nonlinear dynamics of the addressed MASs, resulting in an equivalent linear time-varying data model. With the purpose of mitigating the effects from limited communication bandwidth, a uniform-quantization-based encoding-decoding mechanism is exploited. A model-free adaptive distributed control protocol is put forward to deal with the tracking problem, which is totally data-driven without any requirement of model information except for I/O data. Finally, two illustrative simulation examples are utilized to demonstrate the effectiveness of the proposed control scheme.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Explaining Graph Neural Networks With Topology-Aware Node Selection:
           Application in Air Quality Inference

    • Free pre-print version: Loading...

      Authors: Esther Rodrigo Bonet;Tien Huu Do;Xuening Qin;Jelle Hofman;Valerio Panzica La Manna;Wilfried Philips;Nikos Deligiannis;
      Pages: 499 - 513
      Abstract: Graph neural networks (GNNs) have proven their ability in modelling graph-structured data in diverse domains, including natural language processing and computer vision. However, like other deep learning models, the lack of explainability is becoming a major drawback for GNNs, especially in health-related applications such as air pollution estimation, where a model’s predictions might directly affect humans’ health and habits. In this paper, we present a novel post-hoc explainability framework for GNN-based models. More concretely, we propose a novel topology-aware kernelised node selection method, which we apply over the graph structural and air pollution information. Thanks to the proposed model, we are able to effectively capture the graph topology and, for a certain graph node, infer its most relevant nodes. Additionally, we propose a novel topological node embedding for each node, capturing in a vector-shape the graph walks with respect to every other graph node. To prove the effectiveness of our explanation method, we include commonly employed evaluation metrics as well as fidelity, sparsity and contrastivity, and adapt them to evaluate explainability on a regression task. Extensive experiments on two real-world air pollution data sets demonstrate and visually show the effectiveness of the proposed method.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Distributed Sparse Optimization With Weakly Convex Regularizer: Consensus
           Promoting and Approximate Moreau Enhanced Penalties Towards Global
           Optimality

    • Free pre-print version: Loading...

      Authors: Kei Komuro;Masahiro Yukawa;Renato Luís Garrido Cavalcante;
      Pages: 514 - 527
      Abstract: We propose a promising framework for distributed sparse optimization based on weakly convex regularizers. More specifically, we pose two distributed optimization problems to recover sparse signals in networks. The first problem formulation relies on statistical properties of the signals, and it uses an approximate Moreau enhanced penalty. In contrast, the second formulation does not rely on any statistical assumptions, and it uses an additional consensus promoting penalty (CPP) that convexifies the cost function over the whole network. To solve both problems, we propose a distributed proximal debiasing-gradient (DPD) method, which uses the exact first-order proximal gradient algorithm. The DPD method features a pair of proximity operators that play complementary roles: one sparsifies the estimate, and the other reduces the bias caused by the sparsification. Owing to the overall convexity of the whole cost functions, the proposed method guarantees convergence to a global minimizer, as demonstrated by numerical examples. In addition, the use of CPP improves the convergence speed significantly.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Generalized Locally Most Powerful Tests for Distributed Sparse Signal
           Detection

    • Free pre-print version: Loading...

      Authors: Abdolreza Mohammadi;Domenico Ciuonzo;Ali Khazaee;Pierluigi Salvo Rossi;
      Pages: 528 - 542
      Abstract: In this paper we tackle distributed detection of a localized phenomenon of interest (POI) whose signature is sparse via a wireless sensor network. We assume that both the position and the emitted power of the POI are unknown, other than the sparsity degree associated to its signature. We consider two communication scenarios in which sensors send either ($i$) their compressed observations or ($ii$) a 1-bit quantization of them to the fusion center (FC). In the latter case, we consider non-ideal reporting channels between the sensors and the FC. We derive generalized (i.e. based on Davies’ framework (Davies, 1977)) locally most powerful detectors for the considered problem with the aim of obtaining computationally-efficient fusion rules. Moreover, we obtain their asymptotic performance and, based on such result, we design the local quantization thresholds at the sensors by solving a 1-D optimization problem. Simulation results confirm the effectiveness of the proposed design and highlight only negligible performance loss with respect to counterparts based on the (more-complex) generalized likelihood ratio.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • A Unified Approach for Simultaneous Graph Learning and Blind Separation of
           Graph Signal Sources

    • Free pre-print version: Loading...

      Authors: Aref Einizade;Sepideh Hajipour Sardouie;
      Pages: 543 - 555
      Abstract: In the nascent and challenging problem of the blind separation of the sources (BSS) supported by graphs, i.e., graph signals, along with the statistical independence of the sources, additional dependency information can be interpreted from their graph structure. To the best of our knowledge, in these cases, only GraDe and GraphJADE methods have been proposed to exploit the graph dependencies and/or Graph Signal Processing (GSP) techniques to improve the separation quality. Despite the significant advantages of these graph-based methods, they assume that the underlying graphs are known, which is a serious drawback, especially in many real-world applications. To address this issue, in this paper, we propose a Unified objective function for GraphJADE with Graph Learning (GL), namely U-GraphJADE-GL, and use the Block Coordinate Descent (BCD) to optimize it, which along with the separation task, the underlying graphs are learned simultaneously. We compare the performance of the U-GraphJADE-GL with the GraDe with GL (U-GraDe-GL) and the conventional BSS methods in the BSS task and also analyze the GL performance. Besides, as well as the theoretical and experimental convergence analysis, we derive/state the Cramér-Rao bound (CRB) on the estimation of the mixing and unmixing matrices and also on the attainable Interference-to-Source Ratio (ISR), and compare the asymptotic performance of the proposed method with the optimal CRB estimators. To investigate the applicability in real applications, the proposed method is also successfully applied for denoising the epileptic Electroencephalogram (EEG) signals and also for the audio speech source separation task.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • $H_{infty+}$ -Consensus+State+Estimation+Over+Sensor+Networks+Under+Hybrid+Attacks:+Dynamic+Event-Triggered+Scheme&rft.title=IEEE+Transactions+on+Signal+and+Information+Processing+over+Networks&rft.issn=2373-776X&rft.date=2022&rft.volume=8&rft.spage=556&rft.epage=570&rft.aulast=Alharbi;&rft.aufirst=Fei&rft.au=Fei+Han;Zidong+Wang;Hongli+Dong;Fuad+E.+Alsaadi;Khalid+H.+Alharbi;">A Local Approach to Distributed $H_{infty }$ -Consensus State Estimation
           

    • Free pre-print version: Loading...

      Authors: Fei Han;Zidong Wang;Hongli Dong;Fuad E. Alsaadi;Khalid H. Alharbi;
      Pages: 556 - 570
      Abstract: This paper deals with the distributed $H_{infty }$-consensus state estimation problem for a class of discrete time-varying systems with integral measurements over sensor networks. The addressed target plant has its output modeled as integral measurement so as to account for the interval time taken for sample collection. The signal transmissions between an individual sensor node and its neighboring nodes are first scheduled by a dynamic event-triggered scheme (ETS) for the purpose of energy saving, and such transmissions are further prone to hybrid cyber-attacks (comprising denial-of-service and deception attacks). A distributed estimator is constructed for each node by using the available information from itself and its neighboring nodes such that the estimation error dynamics achieves the prescribed $H_{infty }$-consensus performance in mean square sense. A local performance analysis method is developed to establish sufficient conditions that ensure the existence of the desired distributed estimators, and the corresponding estimator gains are then obtained by solving certain recursive matrix inequalities. The effectiveness of the proposed distributed estimation algorithm is illustrated through extensive simulation studies, where comparative experiments are conducted on time-triggered scheme, static ETS and dynamic ETS.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Likelihood-Based Inference for Modelling Packet Transit From Thinned Flow
           Summaries

    • Free pre-print version: Loading...

      Authors: Prosha Rahman;Boris Beranger;Scott Sisson;Matthew Roughan;
      Pages: 571 - 583
      Abstract: Network traffic speeds and volumes present practical challenges to analysis. Packet thinning and flow aggregation protocols provide smaller structured data summaries, but conversely impede statistical inference. Methods which model traffic propagation typically do not account for the packet thinning and aggregation in their analysis and are of limited practical use. We introduce a likelihood-based analysis which fully incorporates packet thinning and flow aggregation. Inferences can hence be made for models on the level of individual packets while only observing thinned flow summaries. We establish consistency of the resulting maximum likelihood estimator, derive bounds on the volume of traffic which should be observed to achieve a desired degree of efficiency, and identify an ideal family of models. The robust performance of the estimator is examined through simulated analyses and an application on a publicly accessible trace which captured in excess of 36 m packets over a 1 minute period.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Recursive Distributed Filter Design for 2-D Systems Over Sensor Networks:
           On Component-Based, Node-Wise and Dynamic Event-Triggered Scheme

    • Free pre-print version: Loading...

      Authors: Fan Wang;Zidong Wang;Jinling Liang;Steven X. Ding;
      Pages: 584 - 596
      Abstract: In this paper, the recursive distributed filtering problem is investigated for a class of discrete shift-varying two-dimensional systems over sensor networks. To alleviate the resource consumption, a new component-based, node-wise yet dynamic event-triggered scheme is proposed to regulate data transmissions among the neighboring sensor nodes over the sensor-to-filter channels. The aim is to devise a distributed filter to ensure the existence of an upper bound on the filtering error variance and subsequently minimize such a bound at each iteration. In virtue of stochastic analysis techniques and mathematical induction principle, a recursive algorithm is developed to calculate the desired upper bound which is then locally minimized by appropriately parameterizing the filter gains. Furthermore, the effect of the event-triggered scheme on the estimation accuracy is rigorously evaluated. The validity of the established filter design strategy is finally demonstrated by a numerical simulation.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Uncertainty Quantification in Graphon Estimation Using Generalized
           Fiducial Inference

    • Free pre-print version: Loading...

      Authors: Yi Su;Jan Hannig;Thomas C. M. Lee;
      Pages: 597 - 609
      Abstract: Network data can be modeled as an exchangeable graph model (ExGM), and graphon is a two-dimensional function that generates an ExGM. The problem of graphon estimation has been popular in recent years, and several consistent estimation methods have been proposed. However, statistical inference on graphon has not been intensively studied. In this paper, we propose applying the generalized fiducial inference (GFI) methodology to the framework of graphon and perform the uncertainty quantification task. GFI is a branch of inference methods that utilizes the “switching principle” of the parameter and the data, and it seeks for a distribution estimator of the parameters without the need of a prior. We propose an easy-to-implement algorithm to generate fiducial samples of a graphon, which are then used to construct confidence sets. We establish theoretical guarantees of the GFI confidence intervals, and use synthetic graphons to demonstrate its empirical performance for finite sample size. When the labels are unknown, we extend our algorithm and discuss its asymptotic properties. We also apply the proposed method to Facebook social network data and unveil some interesting patterns.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • BRIDGE: Byzantine-Resilient Decentralized Gradient Descent

    • Free pre-print version: Loading...

      Authors: Cheng Fang;Zhixiong Yang;Waheed U. Bajwa;
      Pages: 610 - 626
      Abstract: Machine learning has begun to play a central role in many applications. A multitude of these applications typically also involve datasets that are distributed across multiple computing devices/machines due to either design constraints or computational/privacy reasons. Such applications often require the learning tasks to be carried out in a decentralized fashion, in which there is no central server that is directly connected to all nodes. In real-world decentralized settings, nodes are prone to undetected failures due to malfunctioning equipment, cyberattacks, etc., which are likely to crash non-robust learning algorithms. The focus of this paper is on robustification of decentralized learning in the presence of nodes that have undergone Byzantine failures. The Byzantine failure model allows faulty nodes to arbitrarily deviate from their intended behaviors, thereby ensuring designs of the most robust of algorithms. But the study of Byzantine resilience within decentralized learning, in contrast to distributed learning, is still in its infancy. In particular, existing Byzantine-resilient decentralized learning methods either do not scale well to large-scale machine learning models, or they lack statistical convergence guarantees that help characterize their generalization errors. In this paper, a scalable, Byzantine-resilient decentralized machine learning framework termed Byzantine-resilient decentralized gradient descent (BRIDGE) is introduced. Algorithmic and statistical convergence guarantees are also provided in the paper for both strongly convex problems and a class of nonconvex problems. In addition, large-scale decentralized learning experiments are used to establish that the BRIDGE framework is scalable and it delivers competitive results for Byzantine-resilient convex and nonconvex learning.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • A Decentralized Stochastic Algorithm for Coupled Composite Optimization
           With Linear Convergence

    • Free pre-print version: Loading...

      Authors: Qingguo Lü;Xiaofeng Liao;Shaojiang Deng;Huaqing Li;
      Pages: 627 - 640
      Abstract: In this article, we consider a multi-node sharing problem, where each node possesses a local smooth function that is further considered as the average of several constituent functions, and the network aims to minimize a finite-sum of all local functions plus a coupling function (possibly non-smooth). Decentralized optimization to solve this problem has been a significant focus within engineering research due to its advantages in scalability, robustness, and flexibility. To this aim, an equivalent saddle-point problem of this problem is first formulated, which is amenable to decentralized solutions. Then, a novel decentralized stochastic algorithm, named VR-DPPD, is proposed, which combines the variance-reduction technique of SAGA with the decentralized proximal primal-dual method. We provide a convergence analysis and show that VR-DPPD converges linearly to the exact optimal solution in expectation if smooth local functions are strongly convex. Our work makes progress towards resolving a general composite optimization problem with a convex (possibly non-smooth) coupling function, giving a novel linear convergent algorithm for achieving low computation cost. Numerical examples are presented to demonstrate the viability and performance of VR-DPPD.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • User Association and Hybrid Beamforming Designs for Cooperative mmWave
           MIMO Systems

    • Free pre-print version: Loading...

      Authors: Pengfei Ni;Rang Liu;Ming Li;Qian Liu;
      Pages: 641 - 654
      Abstract: Hybrid analog and digital beamforming has emerged as a key enabling technology for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems since it can balance the trade-off between system performance and hardware efficiency. Owing to the strong ability of central control, cooperative networks show great potential to enhance the spectral efficiency of mmWave communications. In this paper, we consider cooperative mmWave MIMO systems and propose user association and hybrid beamforming design algorithms for three typical hybrid beamforming architectures. The central processing unit (CPU) of the cooperative networks first matches the service pairs of base stations (BSs) and users. Then, an iterative hybrid beamforming design algorithm is proposed to maximize the weighted achievable sum-rate performance of the mmWave MIMO system with fully connected hybrid beamforming architecture. Moreover, a heuristic analog beamforming design algorithm is introduced for the fixed subarray hybrid beamforming architecture. In an effort to further exploit multiple-antenna diversities, we also consider the dynamic subarray architecture and propose a novel antenna design algorithm for the analog beamforming design. Simulation results illustrate that the proposed hybrid beamforming algorithms achieve a significant performance improvement than other existing approaches and the dynamic subarray architecture has great advantages of improving the energy efficiency (EE) performance.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Localization of Data Injection Attacks on Distributed M-Estimation

    • Free pre-print version: Loading...

      Authors: Or Shalom;Amir Leshem;Anna Scaglione;
      Pages: 655 - 669
      Abstract: This paper examines data injection attacks on distributed statistical estimation. We consider a dynamically changing distributed network consisting of N agents exchanging information over time. The N agents share the common goal of minimizing a joint objective function, which is the average of the private objective functions in a distributed manner. The private objective function is a realization of an objective function known to all the agents, but uses private data known to the agent alone. The agents’ data are independent and identically distributed. We have previously proposed a novel data injection attack on the Distributed Projected Gradient (DPG) algorithm which is performed locally by malicious nodes in the network that steer the network’s final state to a state of their choice. The proposed attack cannot be detected using previously described techniques. We propose a new detection and localization scheme, performed in a single instance unlike other methods that require the algorithm to run for many instances to acquire statistics over time. This detection and localization scheme is performed by each agent and is purely local, and does not involve decisions made by other agents. Whenever an agent suspects another agent to be an attacker, it will block its data, and maintain convergence to the true optimal state. We provide exponential bounds for the probability of false alarm and probability of attacker detection and localization. Simulations show that when all the attackers are detected and isolated by each agent, the network will recover and converge to the true optimal state.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Analysis of the Spatio-Temporal Dynamics of COVID-19 in Massachusetts via
           Spectral Graph Wavelet Theory

    • Free pre-print version: Loading...

      Authors: Ru Geng;Yixian Gao;Hongkun Zhang;Jian Zu;
      Pages: 670 - 683
      Abstract: The rapid spread of COVID-19 disease has had a significant impact on the world. In this paper, we study COVID-19 data interpretation and visualization using open-data sources for 351 cities and towns in Massachusetts from December 6, 2020 to September 25, 2021. Because cities are embedded in rather complex transportation networks, we construct the spatio-temporal dynamic graph model, in which the graph attention neural network is utilized as a deep learning method to learn the pandemic transition probability among major cities in Massachusetts. Using the spectral graph wavelet transform (SGWT), we process the COVID-19 data on the dynamic graph, which enables us to design effective tools to analyze and detect spatio-temporal patterns in the pandemic spreading. We design a new node classification method, which effectively identifies the anomaly cities based on spectral graph wavelet coefficients. It can assist administrations or public health organizations in monitoring the spread of the pandemic and developing preventive measures. Unlike most work focusing on the evolution of confirmed cases over time, we focus on the spatio-temporal patterns of pandemic evolution among cities. Through the data analysis and visualization, a better understanding of the epidemiological development at the city level is obtained and can be helpful with city-specific surveillance.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Robust Fault Detection for Uncertain Delayed Systems With Measurement
           Outliers Under Stochastic Communication Protocol

    • Free pre-print version: Loading...

      Authors: Weilu Chen;Jun Hu;Xiaoyang Yu;Dongyan Chen;Zhihui Wu;
      Pages: 684 - 701
      Abstract: In this paper, the robust fault detection (RFD) problem is studied for uncertain networked systems with time delay and measurement outliers under limited communication condition, where the time delay is presumed to occur randomly and the occurrence probability is considered to be uncertain. Specifically, in order to reduce the communication burden, the stochastic communication protocol (SCP) is introduced to adjust the transmission of measurement signals. Moreover, a fault detection filter (FDF) with saturation constraints is constructed to achieve the purpose of properly eliminating possible measurement outliers. Subsequently, a generalized sector condition is utilized to derive the stability criteria of the system on the basis of appropriate Lyapunov functional. In addition, a traditional FDF is also designed to compare with the saturation-constrained FDF. It is shown that the FDF with saturation constraints can properly avoid the influence of outliers on the detection effect. Finally, three simulations are presented to show the superiority and the practicality of the developed saturation-constrained fault detection (FD) filtering algorithm.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • An Exploratory Distributed Localization Algorithm Based on 3D Barycentric
           Coordinates

    • Free pre-print version: Loading...

      Authors: Yinqiu Xia;Chengpu Yu;Chengyang He;
      Pages: 702 - 712
      Abstract: This paper studies the exploratory distributed localization of networked mobile agents using only distance measurements in a GPS-Denied 3D environment. To deal with this challenging problem, an analytic solution is firstly provided to calculate the 3D barycentric coordinates for agents outside the convex hull formed by anchors. Then, a distributed Jacobi Under-Relaxation Iteration (JURI) algorithm is developed to address the static 3D localization problem in a noisy environment. The proposed algorithm is extended for the localization of mobile targets, especially when the mobile agents cannot get enough neighbors' support. Since the proposed algorithms do not suffer from the constraint of the convex hull (exploratory localization); as a result, the proposed method can be scaled to the localization of large-scale networked agents. Finally, several simulation examples are given to validate the effectiveness of the proposed algorithm in different scenarios.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Novel Information-Theoretic Game-Theoretical Insights to Broadcasting

    • Free pre-print version: Loading...

      Authors: Makan Zamanipour;
      Pages: 713 - 725
      Abstract: E.g. for the Internet-of-unmanned aerial vehicles (UAVs) some challenges in broadcasting and from new points of view are explored. In this paper, first, we investigate a single broadcast transceiver. From a control of noisy-channel viewpoint, we consider: (i) Alice sends $mathscr{X}$ to Bob as more efficient as possible while she wishes Bob not to get access to the private message $mathscr{S}$ regarding the correlation between $mathscr{S}$ and $mathscr{X}$ $-$ i.e., Alice purposefully sends a turbulent-flow of the information to Bob; and (ii) where $(Theta _{1};Theta _{2})$ is the control-action-pair which actualise a pursuit-Evasion. We consider dissipativity in our system due to the memory effect relating to the previous states. We thus propose a federated-learning based Blahut-Arimoto algorithm while a 2-D dissipativity-theoretic continuous-Mean-Field-Game (MFG) is proposed with regard to (w.r.t.) a joint probability-distribution-function (PDF) of the population distribution $-$ relating to a continuous-control-law. We also analyse what if Alice is owed to multiple Bobs in a multi-user scenario which we apply a bankruptcy based $3-$level nested game for.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Kernel Regression for Matrix-Variate Gaussian Distributed Signals Over
           Sample Graphs

    • Free pre-print version: Loading...

      Authors: Xiaoyu Miao;Aimin Jiang;Tao Liu;Yanping Zhu;Hon Keung Kwan;
      Pages: 726 - 738
      Abstract: Recent advances of kernel regression assume that target signals lie over a feature graph such that their values can be predicted with the assistance of the graph learned from training data. In this article, we propose a novel kernel regression framework whose outputs follow a matrix-variate Gaussian distribution (MVGD) such that the kernel matrix can be viewed as the column covariance matrix of outputs, and the hyperparameters of a chosen kernel can be optimized using gradient methods. Furthermore, in contrast to the state-of-the-art kernel regression algorithms over graph (KRG), a sample graph of target outputs is introduced to work with regression coefficients and hyperparameters of a chosen kernel in our algorithms. The proposed KRG framework is decomposed into two stages, including the estimation of row and column covariance matrices of MVGD and graph learning along with the estimation of regression coefficients. Numerical approaches are developed to tackle the corresponding optimization problems. Experimental results over synthetic and real-world datasets demonstrate that the performance of the proposed algorithms is superior to that of the state-of-the-art methods.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Graphical Evolutionary Game Theoretic Modeling of Strategy Evolution Over
           Heterogeneous Networks

    • Free pre-print version: Loading...

      Authors: Yuejiang Li;H. Vicky Zhao;Yan Chen;
      Pages: 739 - 754
      Abstract: The rapid development of the Internet and networking technologies greatly facilitates the interactions among heterogeneous agents, while it also causes problems, such as the breakout of rumors and online bullying. Thus, it is critical to study the strategy evolution process in complex networks, that is, how heterogeneous agents interact with each other, update their opinions, and make decisions. In the literature, there have been numerous works on the modeling and analysis of decision making and opinion dynamics in social networks. However, most works assume that agents are homogeneous or only consider one single attribute in agent heterogeneity. In complex social networks, agents differ in many attributes and they constantly influence each other's decisions. How strategy evolves in complex networks with heterogeneous agents remains unknown. In this work, we consider three different attributes of agents: influence, susceptibility, and interest. We use graphical evolutionary game theory to theoretically analyze the impact of different attributes on the strategy evolution process and the evolutionary stable states (ESS). Both theoretical analysis and simulation results show that the influence attribute alone cannot change the ESS of the strategy evolution, while super agents who are both influential and stubborn have the largest impact on the ESS. Furthermore, real data validation shows that our proposed model can effectively model the information diffusion process in online social networks. This study is critical to the better understanding of agents' decision making process, and provides important guidelines on the management of social networks.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • A Hessian Inversion-Free Exact Second Order Method for Distributed
           Consensus Optimization

    • Free pre-print version: Loading...

      Authors: Dušan Jakovetić;Nataša Krejić;Nataša Krklec Jerinkić;
      Pages: 755 - 770
      Abstract: We consider a standard distributed consensus optimization problem where a set of agents connected over an undirected network minimize the sum of their individual (local) strongly convex costs. Alternating Direction Method of Multipliers (ADMM) and Proximal Method of Multipliers (PMM) have been proved to be effective frameworks for design of exact distributed second order methods (involving calculation of local cost Hessians). However, existing methods involve explicit calculation of local Hessian inverses at each iteration that may be very costly when the dimension of the optimization variable is large. In this article, we develop a novel method, termed Inexact Newton method for Distributed Optimization (INDO), that alleviates the need for Hessian inverse calculation. INDO follows the PMM framework but, unlike existing work, approximates the Newton direction through a generic fixed point method (e.g., Jacobi Overrelaxation) that does not involve Hessian inverses. We prove exact global linear convergence of INDO and provide analytical studies on how the degree of inexactness in the Newton direction calculation affects the overall method's convergence factor. Numerical experiments on several real data sets demonstrate that INDO's speed is on par (or better) as state of the art methods iteration-wise, hence having a comparable communication cost. At the same time, for sufficiently large optimization problem dimensions $n$ (even at $n$ on the order of couple of hundreds), INDO achieves savings in computational cost by at least an order of magnitude.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Multiple Hypothesis Testing Framework for Spatial Signals

    • Free pre-print version: Loading...

      Authors: Martin Gölz;Abdelhak M. Zoubir;Visa Koivunen;
      Pages: 771 - 787
      Abstract: The problem of identifying regions of spatially interesting, different or adversarial behavior is inherent to many practical applications involving distributed multisensor systems. In this work, we develop a general framework stemming from multiple hypothesis testing to identify such regions. A discrete spatial grid is assumed for the monitored environment. The spatial grid points associated with different hypotheses are identified while controlling the false discovery rate at a pre-specified level. Measurements are acquired using a large-scale sensor network. We propose a novel, data-driven method to estimate local false discovery rates based on the spectral method of moments. Our method is agnostic to specific spatial propagation models of the underlying physical phenomenon. It relies on a broadly applicable density model for local summary statistics. In between sensors, locations are assigned to regions associated with different hypotheses based on interpolated local false discovery rates. The benefits of our method are illustrated by applications to spatially propagating radio waves.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Large Graph Signal Denoising With Application to Differential Privacy

    • Free pre-print version: Loading...

      Authors: Elie Chedemail;Basile de Loynes;Fabien Navarro;Baptiste Olivier;
      Pages: 788 - 798
      Abstract: Over the last decade, signal processing on graphs has become a very active area of research. Specifically, the number of applications, for instance in statistical or deep learning, using frames built from graphs, such as wavelets on graphs, has increased significantly. We consider in particular the case of signal denoising on graphs via a data-driven wavelet tight frame methodology. This adaptive approach is based on a threshold calibrated using Stein's unbiased risk estimate adapted to a tight-frame representation. We make it scalable to large graphs using Chebyshev-Jackson polynomial approximations, which allow fast computation of the wavelet coefficients, without the need to compute the Laplacian eigendecomposition. However, the overcomplete nature of the tight-frame, transforms a white noise into a correlated one. As a result, the covariance of the transformed noise appears in the divergence term of the SURE, thus requiring the computation and storage of the frame, which leads to an impractical calculation for large graphs. To estimate such covariance, we develop and analyze a Monte-Carlo strategy, based on the fast transformation of zero mean and unit variance random variables. This new data-driven denoising methodology finds a natural application in differential privacy. A comprehensive performance analysis is carried out on graphs of varying size, from real and simulated data.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Decentralized and Model-Free Federated Learning: Consensus-Based
           Distillation in Function Space

    • Free pre-print version: Loading...

      Authors: Akihito Taya;Takayuki Nishio;Masahiro Morikura;Koji Yamamoto;
      Pages: 799 - 814
      Abstract: This paper proposes a fully decentralized federated learning (FL) scheme for Internet of Everything (IoE) devices that are connected via multi-hop networks. Because FL algorithms hardly converge the parameters of machine learning (ML) models, this paper focuses on the convergence of ML models in function spaces. Considering that the representative loss functions of ML tasks e.g., mean squared error (MSE) and Kullback-Leibler (KL) divergence, are convex functionals, algorithms that directly update functions in function spaces could converge to the optimal solution. The key concept of this paper is to tailor a consensus-based optimization algorithm to work in the function space and achieve the global optimum in a distributed manner. This paper first analyzes the convergence of the proposed algorithm in a function space, which is referred to as a meta-algorithm, and shows that the spectral graph theory can be applied to the function space in a manner similar to that of numerical vectors. Then, consensus-based multi-hop federated distillation (CMFD) is developed for a neural network (NN) to implement the meta-algorithm. CMFD leverages knowledge distillation to realize function aggregation among adjacent devices without parameter averaging. An advantage of CMFD is that it works even with different NN models among the distributed learners. Although CMFD does not perfectly reflect the behavior of the meta-algorithm, the discussion of the meta-algorithm's convergence property promotes an intuitive understanding of CMFD, and simulation evaluations show that NN models converge using CMFD for several tasks. The simulation results also show that CMFD achieves higher accuracy than parameter aggregation for weakly connected networks, and CMFD is more stable than parameter aggregation methods
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Deep Transfer Reinforcement Learning for Beamforming and Resource
           Allocation in Multi-Cell MISO-OFDMA Systems

    • Free pre-print version: Loading...

      Authors: Xiaoming Wang;Gaoxiang Sun;Yuanxue Xin;Ting Liu;Youyun Xu;
      Pages: 815 - 829
      Abstract: Orthogonal frequency division multiple access (OFDMA) is one of the promising technologies to satisfy the huge access demand and high data-rate requirement of the fifth generation (5G) networks. In this paper, we study the joint beamforming coordination and resource allocation in the downlink multi-cell multiple-input single-output OFDMA (MISO-OFDMA) systems. First, we divide the allocation framework into beamforming coordination and power allocation (BCPA) module and subcarrier allocation (SA) module. Then, we design a multi-agent deep Q-network (MADQN) algorithm for the allocation framework. Furthermore, we propose a MADQN-based transfer learning framework using knowledge distillation, which is called transfer learning-MADQN (TL-MADQN), to improve the adaptability of neural networks for different wireless schemes. TL-MADQN exploits neural networks and their parameters distilled from pre-trained agents and the experience collected from new agents so that the new agents complete their training process effectively and quickly in the new network environment. Finally, we adjust the allocation policy to maximize the sum data-rate for all users by updating the weights of each neural network. Simulation results show that the proposed MADQN algorithm achieves better performance than the baseline algorithms. Moreover, our TL-MADQN framework further improves the convergence speed and data-rate, which validates its effectiveness and superiority.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Learning to Model the Relationship Between Brain Structural and Functional
           Connectomes

    • Free pre-print version: Loading...

      Authors: Yang Li;Gonzalo Mateos;Zhengwu Zhang;
      Pages: 830 - 843
      Abstract: Recent neuroimaging advances along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level. In this direction, we develop a supervised graph representation learning framework to model the relationship between brain structural connectivity (SC) and functional connectivity (FC) via a graph encoder-decoder system, where the SC is used as input to predict empirical FC. A trainable graph convolutional encoder captures direct and indirect interactions between brain regions-of-interest that mimic actual neural communications, as well as to integrate information from both the structural network topology and nodal (i.e., region-specific) attributes. The encoder learns node-level SC embeddings which are combined to generate (whole brain) graph-level representations for reconstructing empirical FC networks. The proposed end-to-end model utilizes a multi-objective loss function to jointly reconstruct FC networks and learn discriminative graph representations of the SC-to-FC mapping for downstream subject (i.e., graph-level) classification. Comprehensive experiments demonstrate that the learnt representations of said relationship capture valuable information from the intrinsic properties of the subject's brain networks and lead to improved accuracy in classifying a large population of heavy drinkers and non-drinkers from the Human Connectome Project. Our work offers new insights on the relationship between brain networks that support the promising prospect of using graph representation learning to discover more about brain function.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Distributed Filter With Biased Measurements: A Scalable Bias-Correction
           Approach

    • Free pre-print version: Loading...

      Authors: Xiaocheng Zhang;Wenchao Xue;Xingkang He;Haitao Fang;
      Pages: 844 - 854
      Abstract: This paper addresses the distributed state estimation over a large scale sensor network wherein each sensor may have an unknown measurement bias. In order to mitigate the influence of biases to state estimation, a bias estimator only depending on the structure of local sensor is constructed to ensure the algorithm's scalability for sensor networks. It is proved that the proposed bias estimator can effectively realize the bias estimation for the slow time-varying measurement bias. In addition, a distributed Kalman filter embedded with local bias estimator is developed to efficiently correct the biased measurements. Both consistency and stability of the filter are guaranteed under a general collective observability condition for linear time-varying systems. A numerical simulation is provided to illustrate the effectiveness of the developed results.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Inferring Hidden Structures in Random Graphs

    • Free pre-print version: Loading...

      Authors: Wasim Huleihel;
      Pages: 855 - 867
      Abstract: We study the two inference problems of detecting and recovering an isolated community of general structure planted in a random graph. The detection problem is formalized as a hypothesis testing problem, where under the null hypothesis, the graph is a realization of an Erdős-Rényi random graph ${mathcal G}(n,q)$ with edge density $qin (0,1)$; under the alternative, there is an unknown structure $Gamma _{k}$ on $k$ nodes, planted in ${mathcal G}(n,q)$, such that it appears as an induced subgraph. In case of a successful detection, we are concerned with the task of recovering the corresponding structure. For these problems, we investigate the fundamental limits from both the statistical and computational perspectives. Specifically, we derive lower bounds for detecting/recovering the structure $Gamma _{k}$ in terms of the parameters $(n,k,q)$, as well as certain properties of $Gamma _{k}$, and exhibit computationally unbounded optimal algorithms that achieve these lower bounds. We also consider the problem of testing in polynomial-time. As is customary in many similar structured high-dimensional problems, our model undergoes an “easy-hard-impossible” phase transition and computational constraints can severely penalize the statistical performance. To provide an evidence for this phenomen-n, we show that the class of low-degree polynomials algorithms match the statistical performance of the polynomial-time algorithms we develop.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Dynamic Event-Triggered Feedback Fusion Estimation for Nonlinear
           Multi-Sensor Systems With Auto/Cross-Correlated Noises

    • Free pre-print version: Loading...

      Authors: Li Li;Mingyang Fan;Yuanqing Xia;Qing Geng;
      Pages: 868 - 882
      Abstract: This paper aims to solve the distributed fusion estimation problem for a nonlinear system with auto/cross-correlated noises. An equivalent nonlinear system with uncorrelated noises is obtained by means of a de-correlation method. Due to the nonlinear characteristics, the order of de-correlation affects whether the noises are completely uncorrelated or not. In order to improve accuracy of fusion estimation while avoiding the increase of communication burden, fusion predictions are fed back to local filters according to a dynamic event-triggered scheduling (DETS). The feedback frequency is reduced by introducing real-time adjusted offset variables into the DETS, which makes the event-triggered scheduling more strict. Subsequently, a local filter in the form of unscented Kalman filter (UKF) is designed using the measurement and received feedback information. Based on the Kalman-like fusion strategy, a distributed fusion estimation algorithm subject to auto/cross-correlated noises is developed, and boundedness of the fusion error covariance as well as complexity of the fusion algorithm are analyzed. Finally, performance of the proposed fusion estimation algorithm is verified by a numerical simulation.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Auxiliary Particle Filtering Over Sensor Networks Under Protocols of
           Amplify-and-Forward and Decode-and-Forward Relays

    • Free pre-print version: Loading...

      Authors: Yang Liu;Zidong Wang;Cunjia Liu;Matthew Coombes;Wen-Hua Chen;
      Pages: 883 - 893
      Abstract: In this paper, the particle filtering problem is investigated for a class of stochastic systems with multiple sensors under signal relays. To improve the performance of signal transmissions, a relay is deployed between each sensor and the remote filter. Both amplify-and-forward (AF) and decode-and-forward (DF) relays are considered under certain transmission protocols. Stochastic series are employed to describe multiplicative channel gains and additive transmission noises. Novel likelihood functions are derived based on the AF/DF relay models under different protocols. With the measurements collected from all the sensor nodes, a new centralized auxiliary particle filter (APF) is designed by resorting to the statistical information of the channel gains and transmission noises. Next, a consensus-based distributed APF is further established at each node that requires only locally available information. Finally, the effectiveness of the proposed filtering approach is demonstrated through target tracking simulation examples in different situations.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Task-Aware Connectivity Learning for Incoming Nodes Over Growing Graphs

    • Free pre-print version: Loading...

      Authors: Bishwadeep Das;Alan Hanjalic;Elvin Isufi;
      Pages: 894 - 906
      Abstract: Data processing over graphs is usually done on graphs of fixed size. However, graphs often grow with new nodes arriving over time. Knowing the connectivity information of these nodes, and thus, the expanded graph is crucial for processing data over the expanded graph. In its absence, its inference and the subsequent data processing become essential. This paper provides contributions along this direction by considering task-driven data processing for incoming nodes without connectivity information. We model the incoming node attachment as a random process dictated by the parameterized vectors of probabilities and weights of attachment. The attachment is driven by the existing graph topology, the corresponding graph signal, and an associated processing task. We consider two such tasks, one of interpolation at the incoming node, and that of graph signal smoothness. We show that the model bounds implicitly the spectral perturbation between the nominal topology of the expanded graph and the drawn realizations. In the absence of connectivity information our topology, task, and data-aware stochastic attachment performs better than purely data-driven and topology driven stochastic attachment rules, as is confirmed by numerical results over synthetic and real data.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Controller Synthesis of Asynchronous Periodic Event-Triggered Networked
           Control Systems Subject to Quantization Effects

    • Free pre-print version: Loading...

      Authors: Chunyu Wu;Xudong Zhao;Chengchao Li;Ning Zhao;
      Pages: 907 - 919
      Abstract: This paper aims at analyzing the input-to-state stability (ISS) of the networked control systems (NCSs) where the sensor-to-controller and the controller-to-actuator channels are equipped with different dynamic quantizers and event-triggering laws. In the scenarios of quantization effects and external disturbances, we propose to use an asynchronous periodic event-triggering mechanism (PETM) and an improved dynamic quantization scheme. The PETM has potential to reduce the network transmissions compared with the time-based one on the one hand, and is natural for the practical applications (e.g. the digital platforms) on the other hand. The asynchronous PETM, dynamic quantization effects and external disturbances are incorporated by a hybrid dynamical framework. With the help of a technique combining the hybrid system theory and a novel constructed Lyapunov function, the co-design problem of asynchronous periodic event-triggering conditions, dynamic output-feedback control and dynamic quantizer is firstly addressed thereby ensuring the ISS of the NCSs. Moreover, the proposed controller has an intuitive and explicit form, which can be solved easily. To assess the feasibility of the proposed method, a simulation is provided.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
  • Further Studies on Sampled-Data Consensus of Multi-Agent Systems With
           Communication Delays

    • Free pre-print version: Loading...

      Authors: Yajuan Liu;Jiayu Li;Fang Fang;Ju H. Park;
      Pages: 920 - 931
      Abstract: To lower the computational burden and weaken the impact of uncertainties of controllers on system stability, a novel nonfragile sampled-data control scheme is presented for multi-agent systems (MASs) with communication delays. A modified looped-functional, where some matrices are not required to be positive, is introduced by fully utilizing the state information of some intervals. Furthermore, based on the constructed Lyapunov functional, together with the free weighting matrix method, the improved sufficient conditions for consensus of the MASs are derived in the form of linear matrix inequalities (LMIs). Finally, two numerical simulations are used to illustrate the better performance of the proposed methods than other existing literature.
      PubDate: 2022
      Issue No: Vol. 8 (2022)
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 44.210.85.190
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-