Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Jun Ye Yu;Mark J. Coates;Michael G. Rabbat;
Pages: 404 - 417 Abstract: A key challenge in designing distributed particle filters is to minimize the communication overhead without compromising tracking performance. In this paper, we present two distributed particle filters that achieve robust performance with low communication overhead. The two filters construct a graph of the particles and exploit the graph Laplacian matrix in different manners to encode the particle log-likelihoods using a minimum number of coefficients. We validate their performance via simulations with very low communication overhead and provide a theoretical error bound for the presented filters. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Weizhi Lu;Weiyu Li;Wei Zhang;Shu-Tao Xia;
Pages: 418 - 427 Abstract: Expander recovery is an iterative algorithm designed to recover sparse signals measured with binary matrices with linear complexity. In the paper, we study the expander recovery performance of the bipartite graph with girth greater than 4, which can be associated with a binary matrix with column correlations equal to either 0 or 1. For such a graph, expander recovery is proved to achieve the same performance as the traditional basis pursuit recovery, as the signal is dissociated. Compared to random graphs widely used for expander recovery, the graph we study tends to present better empirical performance. Furthermore, its special structure enables reducing the iteration number of expander recovery from O(n log k) times to exactly k times in serial recovery, and from O(log k) times to exactly one time in parallel recovery. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Junhao Hua;Chunguang Li;
Pages: 428 - 441 Abstract: We study the problem of distributed filtering for state space models over networks, which aims to collaboratively estimate the states by a network of nodes. Most of existing works on this problem assume that both process and measurement noises are Gaussian and their covariances are known in advance. In some cases, this assumption breaks down and no longer holds. In this paper, we consider the case that both process and measurement noise covariances are unknown. A few related works have studied this problem. However, these works consider the situation of centralized processing, and they only derive smoothers which are not suitable for online processing in a network. Instead, this paper presents a novel robust Bayesian filter with unknown process and measurement noise covariances over sensor networks, which is distributed and online. A novel Bayesian model is formulated for a modified state space model. This Bayesian model is capable of dealing with outliers and heavy-tailed noises and improving the robustness of the filter to these non-Gaussian noises. Using this model, we first propose a novel centralized algorithm for the robust Bayesian filtering based on variational Bayesian methods. Then, we extend it to the distributed scenario based on the alternating direction method of multipliers (ADMM) technique. The proposed algorithm can simultaneously estimate the states and process/measurement noise covariances. Simulations on a target tracking problem are given to verify the effectiveness of the proposed model and algorithm. The results demonstrate that the proposed algorithm performs much better than the standard Kalman filter and as good as the corresponding centralized algorithm in the presence of outliers. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Marie Maros;Joakim Jaldén;
Pages: 442 - 453 Abstract: Solving optimization problems in multi-agent networks where each agent only has partial knowledge of the problem has become an increasingly important problem. In this paper, we consider the problem of minimizing the sum of n convex functions. We assume that each function is only known by one agent. We show that generalized distributed alternating direction method of multipliers (ADMM) converges Q-linearly to the solution of the mentioned optimization problem if the overall objective function is strongly convex but the functions known by each agent are allowed to be only convex. Establishing Q-linear convergence allows for tracking statements that cannot be made if only R-linear convergence is guaranteed. In other words, in scenarios in which the objective functions are time-varying at the same scale as the algorithm is updated R-linear convergence is typically insufficient. Further, we establish the equivalence between generalized distributed ADMM and proximal exact first-order algorithm (P-EXTRA) for a sub-set of mixing matrices. This equivalence yields insights in the convergence of P-EXTRA when overshooting to accelerate convergence. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Rico Mendrzik;Gerhard Bauch;
Pages: 454 - 468 Abstract: We address the problem of distributed cooperative localization in wireless networks, i.e., nodes without prior position knowledge (agents) wish to determine their own positions. In non-cooperative approaches, positioning is only based on information from reference nodes with known positions (anchors). However, in cooperative positioning, information from other agents is considered as well. Cooperative positioning requires encoding of the uncertainties of agents' positions. To cope with that demand, we consider stochastic inference for localization, which inherently takes the position uncertainties of agents into consideration. Generally, stochastic inference comes at the expense of high costs in terms of computation and information exchange. To relax the requirements of inference algorithms, we propose the framework of position-constrained stochastic inference, in which we first confine the positions of nodes to constrained regions. These regions assist inference algorithms to concentrate on the important areas of the sample space rather than the entire sample space. In contrast to many state-of-the-art approaches, our approach does not require prior knowledge on the positions of agents. We show through simulations that increased localization accuracy, reduced computational complexity, and quicker convergence can be achieved when compared to state-of-the-art non-constrained inference algorithms. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Zufan Zhang;Chun Wang;Chenquan Gan;Shaohui Sun;Mengjun Wang;
Pages: 469 - 478 Abstract: Automatic modulation classification (AMC) is becoming increasingly important in spectrum monitoring and cognitive radio. However, most existing modulation classification algorithms neglect the complementarities between different features and the importance of features fusion. To remedy these flaws, this paper presents a scheme of features fusion for AMC using convolutional neural network (CNN). The approach attempts to fuse different images and handcrafted features of signals to obtain more discriminating features. First, eight handcrafted features and different images features are both extracted. In the latter, signals are converted into two kinds of time-frequency images by smooth pseudo-wigner-ville distribution and Born-Jordan distribution, and a fine-tuned CNN model is utilized to extract image features. Second, the joint features are formed by combination of each of image and handcrafted features, and a multimodality fusion model is applied to fuse the joint features to yield further improvement. Finally, simulation results reveal the superior performance of the proposed scheme. It is worth mentioning that the classification accuracy can reach 92.5% with signal-to-noise ratio at -4 dB. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Amrit Singh Bedi;Alec Koppel;Ketan Rajawat;
Pages: 479 - 494 Abstract: We consider the problem of distributed learning from sequential data via online convex optimization. A multi-agent system is considered where each agent has a private objective but is willing to cooperate in order to minimize the network cost, which is the sum of local cost functions. Different from the classical distributed settings, where the agents coordinate through the use of consensus constraints, we allow the neighboring agent actions to be related via a non-linear proximity function. A decentralized saddle point algorithm is proposed that is capable of handling gradient delays arising from computational issues. The proposed online asynchronous algorithm is analyzed under adversarial settings by developing bounds on the regret of O(√T), which measures the cumulative loss incurred by the online algorithm against a clairvoyant, and network discrepancy of O(T3/4), which measures the cumulative constraint violation or agent disagreement. By allowing the agents to utilize stale gradient information, the proposed algorithm embraces the nuances of distributed learning and serves to be the first distributed online algorithm that can handle adversarial delays. A modified saddle point algorithm is also proposed that explicitly forces the agents to agree as per the constraint function resulting in zero network discrepancy while incurring a slightly higher regret. To showcase the efficacy of the proposed asynchronous algorithm, a spatially correlated random field estimation problem is formulated and solved. Additionally, an application of vision-based target localization with moving cameras demonstrates the benefits of this approach in practice. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Thomas Sherson;Richard Heusdens;W. Bastiaan Kleijn;
Pages: 495 - 510 Abstract: In this paper, we present a novel method for convex optimization in distributed networks called the distributed method of multipliers (DMM). The proposed method is based on a combination of a particular dual lifting and classic monotone operator splitting approaches to produce an algorithm with guaranteed asymptotic convergence in undirected networks. The proposed method allows any separable convex problem with linear constraints to be solved in undirected networks. In contrast to typical distributed approaches, the structure of the network does not restrict the types of problems that can be solved. Furthermore, the solver can be applied to general separable problems, those with separable convex objectives and constraints, via the use of an additional primal lifting approach. Finally, we demonstrate the use of DMM in solving a number of classic signal processing problems including beamforming, channel capacity maximization and portfolio optimization. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Rui Zhang;Quanyan Zhu;
Pages: 511 - 524 Abstract: Optimal transport has been extensively used in resource matching to promote the efficiency of resource usages by matching sources to targets. However, it requires a significant amount of computations and storage spaces for large-scale problems. In this paper, we take a consensus-based approach to decentralize discrete optimal transport problems and develop fully distributed algorithms with alternating direction method of multipliers. We show that our algorithms guarantee certain levels of efficiency and privacy besides the distributed nature. We further derive primal and dual algorithms by exploring the primal and dual problems of discrete optimal transport with linear utility functions and prove the equivalence between them. We verify the convergence, online adaptability, and the equivalence between the primal algorithm and the dual algorithm with numerical experiments. Our algorithms reflect the bargaining between sources and targets on the amounts and prices of transferred resources and reveal an averaging principle which can be used to regulate resource markets and improve resource efficiency. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Jielong Yang;Junshan Wang;Wee Peng Tay;
Pages: 525 - 537 Abstract: We investigate the problem of truth discovery based on opinions from multiple agents (who may be unreliable or biased) that form a social network. We consider the case where agents' reliabilities or biases are correlated if they belong to the same community, which defines a group of agents with similar opinions regarding a particular event. An agent can belong to different communities for different events, and these communities are unknown a priori. We incorporate knowledge of the agents' social network in our truth discovery framework and develop Laplace variational inference methods to estimate agents' reliabilities, communities, and the event states. We also develop a stochastic variational inference method to scale our model to large social networks. Simulations and experiments on real data suggest that when observations are sparse, our proposed methods perform better than several other inference methods, including majority voting, TruthFinder, AccuSim, the Confidence-Aware Truth Discovery method, the Bayesian classifier combination (BCC) method, and the community BCC method. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Sujay Bhatt;Vikram Krishnamurthy;
Pages: 538 - 553 Abstract: This paper presents adaptive polling algorithms and their analysis for social networks having a hierarchical influence structure. The adaptive polling problem on the social network is formulated as a partially observed Markov decision process (POMDP). Our main results exploit the structure of the polling problem to determine novel conditions for Blackwell dominance that arise in hierarchical social influence networks. The Blackwell dominance conditions enable the construction of myopic policies that provably upper bound the optimal policy of the POMDP for adaptive polling. Adaptive versions of intent polling and expectation polling are developed using Blackwell dominance, and they are inexpensive to implement. For polling problems not having a Blackwell dominance structure, the Le Cam deficiency is used to determine approximate Blackwell dominance; this is used to develop an adaptive version of the recently proposed Neighbourhood Expectation Polling algorithm. The proposed Blackwell dominance conditions also facilitate the comparison of Rényi divergence and Shannon capacity of more general channel structures that arise in polling hierarchical social influence networks. Numerical examples are provided to illustrate the adaptive polling policies with parameters estimated from YouTube data. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Alejandro Parada-Mayorga;Daniel L. Lau;Jhony H. Giraldo;Gonzalo R. Arce;
Pages: 554 - 569 Abstract: In the area of graph signal processing, a graph is a set of nodes arbitrarily connected by weighted links; a graph signal is a set of scalar values associated with each node; and sampling is the problem of selecting an optimal subset of nodes from which a graph signal can be reconstructed. This paper proposes the use of spatial dithering on the vertex domain of the graph, as a way to conveniently find statistically good sampling sets. This is done establishing that there is a family of good sampling sets characterized on the vertex domain by a maximization of the distance between sampling nodes; in the Fourier domain, these are characterized by spectrums that are dominated by high frequencies referred to as blue-noise. The theoretical connection between blue-noise sampling on graphs and previous results in graph signal processing is also established, explaining the advantages of the proposed approach. Restricting our analysis to undirected and connected graphs, numerical tests are performed in order to compare the effectiveness of blue-noise sampling against other approaches. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Lin Gao;Giorgio Battistelli;Luigi Chisci;
Pages: 570 - 584 Abstract: This paper focuses on reducing communication bandwidth and, consequently, energy consumption in the context of distributed multitarget tracking over a peer-to-peer sensor network. A consensus cardinalized probability hypothesis density (CCPHD) filter with event-triggered communication is developed by enforcing each node to broadcast its local information to the neighbors only when it is worth to, i.e., the node has gained a sufficient amount of information with respect to its latest broadcasting. To this end, each sensor node separately evaluates the discrepancies of the cardinality probability mass function (PMF) and of the spatial probability density function (PDF) between the current local posterior and the one recoverable from neighbors after the latest transmission. Then, each sensor node selectively sends the specific information on the multitarget distribution (i.e., the cardinality PMF or the spatial PDF or both) that is considered to be worth transmitting (i.e., such that the respective discrepancy exceeds a preset threshold). Two types of discrepancy measures, i.e., the Kullback-Leibler divergence and the Cauchy-Schwarz divergence, are investigated. The performance of the proposed event-triggered CCPHD filter is evaluated through simulation experiments. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Sami Ahmed Aldalahmeh;Saleh O. Al-Jazzar;Des McLernon;Syed Ali Raza Zaidi;Mounir Ghogho;
Pages: 585 - 597 Abstract: In this paper, we investigate fusion rules for distributed detection in large random clustered wireless sensor networks with a three-tier hierarchy; the sensor nodes (SNs), the cluster heads (CHs), and the fusion center (FC). The CHs collect the SNs' local decisions and relay them to the FC, which then fuses them to reach the ultimate decision. The SN-CH and the CH-FC channels suffer from additive white Gaussian noise. In this context, we derive the optimal log-likelihood ratio (LLR) fusion rule, which turns out to be intractable. So, we develop a sub-optimal linear fusion rule (LFR) that weighs the cluster's data according to both its local detection performance and the quality of the communication channels. In order to implement it, we propose an approximate maximum likelihood based LFR (LFR-aML), which estimates the required parameters for the LFR. We also derive Gaussian-tail upper bounds for the detection and false alarms probabilities for the LFR. Furthermore, an optimal CH transmission power allocation strategy is developed by solving the Karush-Kuhn-Tucker conditions for the related optimization problem. Extensive simulations show that the LFR attains a detection performance near to that of the optimal LLR and confirms the validity of the proposed upper bounds. Moreover, when compared to equal power allocation, simulations show that our proposed power allocation strategy achieves a significant power saving at the expense of a small reduction in the detection performance. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
Authors:
Jingyuan Liu;Pirathayini Srikantha;
Pages: 598 - 610 Abstract: The cyber-physical nature of the modern power grid allows active power entities to exchange information signals with one another to make intelligent local actuation decisions. Exacting effective coordination amongst these cyber-enabled entities by way of strategic signal exchanges is essential for accommodating highly fluctuating power components (e.g., renewables, electric vehicles, etc.) that are becoming prevalent in today's electric grid. As such, in this paper, we present a novel decentralized topology reconfiguration algorithm for the distribution network (DN) that allows the system to adapt in real time to unexpected perturbations and/or congestions to restore balance in loads across the feeder and improve the DN voltage profile. For this, individual agents residing in DN buses iteratively exchange signals with neighbouring nodes to infer the current state (e.g., power balance and voltage) of the system and utilize this information to make local line switching decisions. Strong convergence properties and optimality conditions of the proposed algorithm are established via theoretical studies evoking potential games and discrete concavity. Comparative simulation studies conducted on realistic DNs showcase the practical properties of the proposed algorithm. PubDate:
Sept. 2019
Issue No:Vol. 5, No. 3 (2019)
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