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Network Localization and Navigation With Scalable Inference and Efficient Operation

IEEE TRANSACTIONS ON MOBILE COMPUTING 2022年第6期21卷 2072-2087页

作者： Teague, Bryan Liu, Zhenyu Meyer, Florian Conti, Andrea Win, Moe Z. MIT Lincoln Lab 244 Wood St Lexington MA 02173 USA MIT Wireless Informat & Network Sci Lab 77 Massachusetts Ave Cambridge MA 02139 USA Univ Calif San Diego Scripps Inst Oceanog Dept Elect & Comp Engn La Jolla CA 92093 USA Univ Ferrara Dept Engn Ferrara Italy Univ Ferrara CNIT Ferrara Italy MIT Lab Informat & Decis Syst Cambridge MA 02139 USA

Location-aware networks enable new services and applications in fields such as autonomous driving, smart cities, and the Internet-of-Things. Network localization and navigation (NLN) is a recently proposed paradigm for accurate ubiquitous localization without the need for extensive infrastructure. In NLN, devices form an interconnected network for the purpose of cooperatively localizing one another. This paper introduces Peregrine, a system that combines real-time distributed NLN algorithms with ultra-wideband (UWB) sensing and communication. The Peregrine software integrates three NLN algorithms to jointly perform 3-D localization and network operation in a technology agnostic manner, leveraging both spatial and temporal cooperation. Peregrine hardware is composed of compact low-cost devices that comprise a microprocessor and a UWB radio. The contribution of each algorithmic component is characterized through indoor network experimentation. Results show that Peregrine is robust, scalable, and capable of sub-meter accuracy in challenging wireless environments.

关键词： Wireless communication Atmospheric measurements Particle measurements Wireless sensor networks Mobile computing Hardware Inference algorithms Localization navigation distributed algorithms Internet-of-Things wireless networks

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distributed Online Linear Regressions

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第1期67卷 616-639页

作者： Yuan, Deming Proutiere, Alexandre Shi, Guodong Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China KTH Royal Inst Technol Dept Automat Control SE-10044 Stockholm Sweden Univ Sydney Sydney Inst Robot & Intelligent Syst Australian Ctr Field Robot Sydney NSW 2006 Australia

We study online linear regression problems in a distributed setting, where the data is spread over a network. In each round, each network node proposes a linear predictor, with the objective of fitting the network-wide data. It then updates its predictor for the next round according to the received local feedback and information received from neighboring nodes. The predictions made at a given node are assessed through the notion of regret, defined as the difference between their cumulative network-wide square errors and those of the best off-line network-wide linear predictor. Various scenarios are investigated, depending on the nature of the local feedback (full information or bandit feedback), on the set of available predictors (the decision set), and the way data is generated (by an oblivious or adaptive adversary). We propose simple and natural distributed regression algorithms, involving, at each node and in each round, a local gradient descent step and a communication and averaging step where nodes aim at aligning their predictors to those of their neighbors. We establish regret upper bounds typically in O(T-3/4) when the decision set is unbounded and in O(root T) in case of bounded decision set.

关键词： distributed algorithms online linear regression long-term constraints regret

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distributed approximate k-core decomposition and min-max edge orientation: Breaking the diameter barrier

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JOURNAL OF PARALLEL AND distributed COMPUTING 2021年 147卷 87-99页

作者： Chan, T-H Hubert Sozio, Mauro Sun, Bintao Univ Hong Kong Dept Comp Sci Pokfulam Rd Hong Kong Peoples R China Telecom Paristech Comp Sci Dept INFRES 46 Rue Barrault F-75013 Paris France

We design distributed algorithms to compute approximate solutions for several related graph optimization problems. All our algorithms have round complexity being logarithmic in the number of nodes of the underlying graph and in particular independent of the graph diameter. By using a primal-dual approach, we develop a 2(1 + epsilon)-approximation algorithm for computing the coreness values of the nodes in the underlying graph, as well as a 2(1 + epsilon)-approximation algorithm for the min-max edge orientation problem, where the goal is to orient the edges so as to minimize the maximum weighted in-degree. We provide lower bounds showing that the aforementioned algorithms are tight both in terms of the approximation guarantee and the round complexity. Additionally, motivated by the fact that the densest subset problem has an inherent dependency on the diameter of the graph, we study a weaker version that does not suffer from the same limitation. Finally, we conduct experiments on large real-world graphs to evaluate the effectiveness of our algorithms. (C) 2020 Elsevier Inc. All rights reserved.

关键词： distributed algorithms Coreness Round complexity

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A distributed Optimization Algorithm Based on Multiagent Network for Economic Dispatch With Region Partitioning

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第5期51卷 2466-2475页

作者： Liu, Qingshan Le, Xinyi Li, Kaixuan Southeast Univ Sch Math Nanjing 210096 Peoples R China Southeast Univ Jiangsu Prov Key Lab Networked Collect Intelligen Nanjing 210096 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

In this article, a discrete-time distributed optimization algorithm is proposed for solving the economic dispatch (ED) problem with some groups of generator units to communicate over a connected graph, which is independent of the power system. The ED problem is converted to a distributed optimization problem with an objective of the sum of individual convex functions and constraints of local generators. Based on the optimal conditions, a class of distributed algorithms is designed to find the solution to the ED problem. The distributed algorithm can be realized as a multiagent system with a connected graph, whose convergence can be proved using the dynamic analysis method. Moreover, experiments with simulations are presented to demonstrate the performance of the proposed algorithm.

关键词： Optimization distributed algorithms Partitioning algorithms Heuristic algorithms Power generation Multi-agent systems Generators Convergence distributed algorithm economic dispatch (ED) multiagent system

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Learning in Potential Games for Electric Power Grids: Models, Dynamics, and Outlook

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IEEE SYSTEMS JOURNAL 2022年第3期16卷 5079-5091页

作者： Wang, Shengyi Du, Liang Marden, Jason R. Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

Noncooperative game-theoretic methods have been widely utilized in system-level engineering applications as they are capable of aggregating interests, information, and behaviors of many independent, self-interested entities with conflicting objectives via payoffs. Another major advantage is the existence of adaptive learning dynamics that involve realistic, constrained decision making, and converge to Nash equilibrium. If players are not fully rational (e.g., constrained or regulated), such convergence is generally not guaranteed except in some special games, such as potential games (PGs). Within each PG, there exists a potential function that connects the global objective with players' payoffs and actions such as optimizers of the global objective align with Nash equilibrium. Therefore, PGs have been widely utilized in many engineering domains and recently to power grids. This article summarizes the state-of-the-art existing literature on applying various PG models in power grids and provides an overlook of potential models and algorithms that have not received sufficient attention but possess unique capabilities to solve existing challenges in power grids.

关键词： Games Power grids Power system dynamics Convex functions Behavioral sciences Analytical models Electric potential Constrained optimization distributed algorithms noncooperative games potential games (PGs)

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distributed Sequential Hypothesis Testing With Byzantine Sensors

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 3044-3058页

作者： Li, Zishuo Mo, Yilin Hao, Fei Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ BNRist Beijing 100084 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

This paper considers the problem of sequential binary hypothesis testing based on observations from a network of m sensors where a subset of the sensors is compromised by a malicious adversary. The asymptotic average sample number required to reach a certain level of error probability is selected as the performance metric of the system. We propose an asymptotically optimal voting algorithm for the sensor network with a fusion center and generalize it to fully-distributed networks, where the algorithm stays asymptotically optimal under the weak assumption that the sensor network is connected. Moreover, we prove that both of the proposed algorithms are asymptotically optimal in the presence of Byzantine sensors, in the sense that each of them forms a Nash equilibrium with the worst-case attack (flip-attack). Compared to existing distributed detection strategies, the proposed scheme has a low message complexity, which is independent of the error probability and the sample number, by taking advantage of the sparsity of votes. The results are corroborated by numerical simulations.

关键词： Sequential analysis distributed algorithms wireless sensor networks Byzantine attack fault tolerant systems asymptotic optimality

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A Flexible distributed Stochastic Optimization Framework for Concurrent Tasks in Processing Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2021年第5期29卷 2045-2058页

作者： Shi, Zai Eryilmaz, Atilla Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

distributed stochastic optimization has important applications in the practical implementation of machine learning and signal processing setup by providing means to allow interconnected network of processors to work towards the optimization of a global objective with intermittent communication. Existing works on distributed stochastic optimization predominantly assume all the processors storing related data to perform updates for the optimization task in each iteration. However, such optimization processes are typically executed at shared computing/data centers along with other concurrent tasks. Therefore, it is necessary to develop efficient optimization methods that possess the flexibility to share the computing resources with other ongoing tasks. In this work, we propose a new first-order framework that allows this flexibility through a probabilistic computing resource allocation strategy while guaranteeing the satisfactory performance of distributed stochastic optimization. Our results, both analytical and numerical, show that by controlling a flexibility parameter, our suite of algorithms (designed for various scenarios) can achieve the lower computation and communication costs of distributed stochastic optimization than their inflexible counterparts. This framework also enables the fair sharing of the common resources with other concurrent tasks being processed by the processing network.

关键词： Optimization Program processors Stochastic processes Task analysis Servers Convergence Machine learning distributed algorithms optimization methods

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distributed Optimal Control of Energy Hubs for Micro-Integrated Energy Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第4期51卷 2145-2158页

作者： Qu, Ming Ding, Tao Jia, Wenhao Zhu, Shanying Yang, Yongheng Blaabjerg, Frede Xi An Jiao Tong Univ Dept Elect Engn Xian 710049 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

Integrated energy systems become more and more important to realize the energy complementary property. Micro-integrated energy system, served as the terminal integrated energy system, will have the electricity delivered directly to the local customers by energy hubs (EHs). Here, the data and information of the EHs during the operation are confidential and should be kept by each owner. Therefore, this article designs a dual-decomposition-based distributed algorithm to address this problem, where the optimal consensus problem is used for the dual problem to update the multipliers. The primary and dual problems are alternatively solved until the Karush-Kuhn-Tucher condition is satisfied. For the proposed distributed algorithm, the feasibility can be strictly guaranteed during the iteration process. Moreover, theorems and lemmas are proved for the linear convergence rate. The numerical results verify the effectiveness of the proposed algorithm.

关键词： Natural gas Cost function distributed algorithms Resistance heating Economics distributed optimal control dual decomposition (DD) energy hubs (EHs) micro-integrated energy systems (m-IESs)

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distributed Composite Optimization Over Relay-Assisted Networks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第10期51卷 6587-6598页

作者： Shi, Chong-Xiao Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Key Lab Integrated Automat Proc Ind Minist Educ Shenyang 110819 Peoples R China

This article studies the distributed composite optimization problem over relay-assisted networks (DCOP-RNs). By combining the local primal-dual updates with a data fusion strategy, an effective distributed algorithm is developed to solve the DCOP-RN. Compared with the existing linearized alternating direction method of multipliers (ADMMs), this article provides a novel interpretation of the proposed algorithm. More specifically, it is shown that the proposed algorithm can be interpreted as a simplified proximal augmented Lagrangian method. Within this framework, a simplified convergence analysis of the algorithm is conducted, based on which the convergence to an optimal solution to the DCOP-RN is proved. Further, taking into account the limited bandwidth in real networks, this article proposes a quantized distributed algorithm to solve the DCOP-RN. Especially, a relationship between the quantization resolution and the convergence accuracy of the algorithm is established. Finally, simulation examples verify the theoretical results.

关键词： Optimization Convergence Signal processing algorithms Bandwidth Protocols distributed algorithms Convex functions Algorithm analysis distributed optimization primal-dual methods relay-assisted networks

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distributed Average Tracking in Weight-Unbalanced Directed Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第9期66卷 4436-4443页

作者： Sun, Shan Chen, Fei Ren, Wei Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China

This article studies a distributed average tracking (DAT) problem, in which a collection of agents work collaboratively, subject to local communication, to track the average of a set of reference signals, each of which is available to a single agent. Our primary objective is to seek a design methodology for DAT under possibly weight-unbalanced directed networks-the most general and thus most challenging case from the network topology perspective, which has few results in the literature. For this purpose, we propose a distributed algorithm based on a chain of two integrators that are coupled with a distributed estimator. It is found that the convergence depends on not only the network topology but also the deviations among the reference signal accelerations. Another primary interest of this article stems from the dynamics perspective-a point perceived as a main source of control design difficulty for multiagent systems. Indeed, we devise a nonlinear algorithm that is capable of achieving DAT under weight-unbalanced directed networks for agents subject to high-order integrator dynamics. The results show that the convergence to the vicinity of the average of the reference signals is guaranteed as long as the signals' states and control inputs are all bounded. Both algorithms are robust to initialization errors, i.e., DAT is insured even if the agents are not correctly initialized, enabling the potential applications in a wider spectrum of application domains.

关键词： Heuristic algorithms Directed graphs Laplace equations Eigenvalues and eigenfunctions distributed algorithms Convergence Network topology distributed average tracking multiagent system weight-unbalanced directed graphs

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