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A distributed Evolutionary algorithm for detecting minimum vertex cuts for wireless ad hoc and sensor networks

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2019年第Feb.期127卷 70-81页

作者： Dagdeviren, Orhan Akram, Vahid Khalilpour Farzan, Ali Ege Univ Int Comp Inst Izmir Turkey Islamic Azad Univ Comp Dept Shabestar Iran

A minimum vertex cut (MVC) of a graph G is the smallest subset of vertices whose removal creates at least two disconnected group of other vertices. Detecting nodes in MVCs in wireless ad hoc and sensor networks (WASNs) provides valuable information about their robustness and critical parts. There is a wide variety of central algorithms that find or estimate MVCs of graphs, but to the best of our knowledge the existing distributed algorithms can only estimate the cardinality of MVCs, or k, from local neighborhood information. Regardless of the fact that MVCs remain unknown in these algorithms, local estimation of k may produce wrong values, far from the real k. We propose a distributed algorithm, which uses an adapted meta heuristic method, to detect the nodes in MVCs. In the proposed algorithm, all nodes find their available paths to the sink (root node) and determine the minimum subset of nodes that their failure disconnects all detected paths. The smallest detected sets by the nodes will be MVCs of the WASN. Besides finding the union of MVCs with up to 89% average accuracy, the testbed and simulation results show that the correct detection ratio of k in the proposed algorithm is up to 37% more than the existing distributed algorithms.

关键词： Minimum vertex cut Imperialist competitive algorithm Evolutionary algorithms distributed algorithms k-connectivity Fault tolerance Reliability Wireless ad hoc and sensor networks

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distributed Clustering of Text Collections

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IEEE ACCESS 2019年 7卷 155671-155685页

作者： Zamora, Juan Allende-Cid, Hector Mendoza, Marcelo Pontificia Univ Catolica Valparaiso Inst Estadist Valparaiso 2340023 Chile Pontificia Univ Catolica Valparaiso Escuela Ingn Informat Valparaiso 2362807 Chile Univ Tecn Federico Santa Maria Ctr Cient & Tecnol Valparaiso Valparaiso 2390123 Chile

Current data processing tasks require efficient approaches capable of dealing with large databases. A promising strategy consists in distributing the data along with several computers that partially solve the undertaken problem. Finally, these partial answers are integrated to obtain a final solution. We introduce distributed shared nearest neighbors (D-SNN), a novel clustering algorithm that work with disjoint partitions of data. Our algorithm produces a global clustering solution that achieves a competitive performance regarding centralized approaches. The algorithm works effectively with high dimensional data, being advisable for document clustering tasks. Experimental results over five data sets show that our proposal is competitive in terms of quality performance measures when compared to state of the art methods.

关键词： Clustering algorithms distributed databases Partitioning algorithms Task analysis Computers Parallel processing Approximation algorithms distributed algorithms distributed text clustering high dimensional data

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A Real-Time and Fully distributed Approach to Motion Planning for Multirobot Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2019年第12期49卷 2636-2650页

作者： Zhou, Yuan Hu, Hesuan Liu, Yang Lin, Shang-Wei Ding, Zuohua Nanyang Technol Univ Sch Comp Sci & Engn Coll Engn Singapore 639798 Singapore Xidian Univ Sch Electromech Engn Xian 710071 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710054 Shaanxi Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci Hangzhou 310018 Zhejiang Peoples R China

Motion planning is one of the most critical problems in multirobot systems. The basic target is to generate a collision-free trajectory for each robot from its initial position to the target position. In this paper, we study the trajectory planning for the multirobot systems operating in unstructured and changing environments. Each robot is equipped with some sensors of limited sensing ranges. We propose a fully distributed approach to planning trajectories for such systems. It combines the model predictive control (MPC) strategy and the incremental sequential convex programming (iSCP) method. The MPC framework is applied to detect the local running environment real-timely with the concept of receding horizon. For each robot, a nonlinear programming is built in its current prediction horizon. To construct its own optimization problem, a robot first needs to communicate with its neighbors to retrieve their current states. Then, the robot predicts the neighbors' future positions in the current horizon and constructs the problem without waiting for the prediction information from its neighbors. At last, each robot solves its problem independently via the iSCP method such that the robot can move autonomously. The proposed method is polynomial in its computational complexity.

关键词： distributed algorithms motion planning multirobot systems trajectory optimization

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A distributed Algorithm for Least Squares Solutions

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第10期64卷 4217-4222页

作者： Wang, Xuan Zhou, Jingqiu Mou, Shaoshuai Corless, Martin J. Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47906 USA

In this technical note, a distributed algorithm is proposed for multiagent networks to achieve a least squares solution of a system of linear equations, in which each agent only knows part of the overall equations and communicates only with its nearby neighbors. The proposed algorithm is discrete time but does not involve small or time-varying step sizes. Given that the network is fixed, connected, and undirected, the proposed algorithm enables all agents in the network to achieve exponentially fast the same least squares solution;this is validated by simulations.

关键词： distributed algorithms least squares solutions linear equations multiagent networks

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Survey Propagation for Cell-Free Massive MIMO Pilot Assignment

Survey Propagation for Cell-Free Massive MIMO Pilot Assignme...

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IEEE Globecom Workshops

作者： Sunho Kim Hong Ki Kim Sang Hyun Lee School of Electrical Engineering Korea University Seoul Korea

ISBN: (纸本)9781665459761

This work develops a survey propagation approach to distributed pilot assignment (PA) optimization in cell-free massive multiple-input multiple-output networks. The reuse of pilot signals among multiple links incurs the pilot contamination, thereby degrading the system performance. For sustainable management of pilot sequences, the PA operation policy can be formulated in a combinatorial way. To avoid large-scale computational loads, the resulting optimization task is approached in a novel distributed framework of survey propagation, which has been originally developed to address interacting particle systems in physics. A developed algorithm demonstrates outperforming behaviors over existing schemes with respect to the overall network throughput.

关键词： System performance Partial transmit sequences Massive MIMO Throughput MIMO Task analysis distributed algorithms

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distributed Online Voltage Control in Active Distribution Networks Considering PV Curtailment

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2019年第10期15卷 5519-5530页

作者： Li, Jiayong Xu, Zhao Zhao, Jian Zhang, Chaorui Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Hunan Key Lab Intelligent Informat Anal & Integra Changsha 410082 Hunan Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China Shanghai Univ Elect Power Coll Elect Engn Shanghai 200090 Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

In this paper, we propose a distributed online voltage control algorithm for distribution networks with multiple photovoltaic (PV) systems based on dual-ascent method. Conventional distributed algorithms implement voltage control only when the algorithms converge. However, our proposed algorithm is able to carry out voltage control immediately. In particular, we derive a closed-form solution for PV controllers to locally update the active and reactive power set points aiming at minimizing the total loss and maintaining bus voltages within the acceptable ranges. The optimality is guaranteed and the convergence is established analytically. Moreover, our proposed algorithm only requires the information exchange between neighboring PV systems, thus reducing communication complexity. Finally, numerical tests on IEEE 37-bus distribution system verify the effectiveness and robustness of our proposed algorithm.

关键词： Voltage control Reactive power distributed algorithms Optimization Decentralized control Inverters Mathematical model distributed algorithm distribution networks (DNs) dual-ascent method online voltage control photovoltaic (PV) system

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Regular Topology Formation Based on Artificial Forces for distributed Mobile Robotic Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2019年第10期18卷 2415-2429页

作者： Zhao, Haitao Wei, Jibo Huang, Shengchun Zhou, Li Tang, Qi Natl Univ Def Technol Coll Elect Sci Changsha 410073 Hunan Peoples R China

The distributed mobile robotic network consists of a group of mobile nodes, such as mobile sensors, unmanned vehicles, unmanned submarines, unmanned air vehicles, or mobile robots. The mobile robotic network keeping a regular topology can utilize efficient network protocols and is also promising in many application scenarios. We propose a distributed algorithm that controls multiple distributed robotic nodes to form regular topology, including straight line, ring, triangular lattice, and square lattice. Our algorithm generates artificial forces, including the attractive force towards a reference point to gather the distributed nodes, the repulsive force from neighboring nodes to keep the desirable distance among them, the formation force to form a specific shape, and the obstacle avoidance force to avoid possible obstacles, such that each node simply follows the resultant force to move. The algorithm works in a fully distributed manner, converges fast, and is easy to deploy, requiring only one-hop local network geometry information. And, it is effective under both 2D and 3D scenarios. A computer demo is developed to demonstrate the effectiveness of the algorithm for large numbers of robotic nodes.

关键词： Topology formation mobile robotic networks artificial forces distributed algorithms

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distributed Nash equilibrium seeking under partial-decision information via the alternating direction method of multipliers

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AUTOMATICA 2019年 103卷 27-35页

作者： Salehisadaghiani, Farzad Shi, Wei Pavel, Lacra Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

In this paper, we consider the problem of finding a Nash equilibrium in a multi-player game over generally connected networks. This model differs from a conventional setting in that players have partial information on the actions of their opponents and the communication graph is not necessarily the same as the players' cost dependency graph. We develop a relatively fast algorithm within the framework of inexact-ADMM, based on local information exchange between the players. We prove its convergence to Nash equilibrium for fixed step-sizes and analyse its convergence rate. Numerical simulations illustrate its benefits when compared to a consensus-based gradient type algorithm with diminishing step-sizes. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Game theory over networks distributed algorithms Noncooperative games Communication graph

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ByRDiE: Byzantine-Resilient distributed Coordinate Descent for Decentralized Learning

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2019年第4期5卷 611-627页

作者： Yang, Zhixiong Bajwa, Waheed U. Rutgers Univ New Brunswick Dept Elect & Comp Engn 94 Brett Rd Piscataway NJ 08854 USA

distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional occur frequently in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional learning in fully distributed (also known as decentralized) settings. In this paper, an algorithm termed Byzantine-resilient distributed coordinate descent is developed and analyzed that enables distributed learning in the presence of Byzantine failures. Theoretical analysis (convex settings) and numerical experiments (convex and nonconvex settings) highlight its usefulness for high-dimensional distributed learning in the presence of Byzantine failures.

关键词： Training Optimization Machine learning distributed algorithms Training data Information processing Machine learning algorithms Byzantine failure consensus coordinate descent decentralized learning distributed optimization empirical risk minimization machine learning

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Scheduled-Asynchronous distributed Algorithm for Optimal Power Flow

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2019年第1期6卷 261-275页

作者： Chang, Chin-Yao Cortes, Jorge Martinez, Sonia Univ Calif San Diego Dept Mech & Aerosp Engn San Diego CA 92093 USA

Optimal power flow (OPF) problems are nonconvex and large-scale optimization problems with important applications in power networks. This paper proposes the scheduled-asynchronous algorithm to solve a distributed semidefinite programming formulation of the OPF problem. In this formulation, every agent seeks to solve a local optimization with its own cost function, physical constraints on its nodal power injection, voltage, and power flow of the lines it is connected to, and decision constraints on variables shared with neighbors to ensure consistency of the obtained solution. In the scheduled-asynchronous algorithm, every pair of connected nodes in the electrical network update their local variables in an alternating fashion. This strategy is asynchronous, in the sense that no clock synchronization is required, and relies on an orientation of the electrical network that prescribes the precise ordering of node updates. We establish the asymptotic convergence properties to the primal-dual optimizer when the orientation is acyclic. Given the dependence of the convergence rate on the network orientation, we also develop a distributed graph coloring algorithm that finds an orientation with diameter at most five for electrical networks. Simulations illustrate our results on various IEEE bus test cases.

关键词： distributed algorithms optimal power flow semidefinite programming

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