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distributed multi-agent temporal-difference learning with full neighbor information

Control Theory and Technology 2020年第4期18卷 379-389页

作者： Zhinan Peng Jiangping Hu Rui Luo Bijoy K.Ghosh School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina Department of Mathematics and Statistics Texas Tech UniversityLubbockTX 79409-1042USA

This paper presents a novel distributed multi-agent temporal-difference learning framework for value function approximation,which alows agents using all the neighbor information instead of the information from only one *** full neighbor information,the proposed framework(1)has a faster convergence rate,and(2)is more robust compared to the state of-the art *** we propose a distributed multi-agent discounted temporal dfferene algorithm and a distributed muli-agent average cost temporal diference leaming algorithm based on th ***,the two proposed algorthms'theoretical convergence proofs are *** simulation resuts show that our proposed algorihms are superior to the gossip-based algorithm in convergence speed,robustness to noise and time-varying network topology.

关键词： distributed algorithm Reinforcement learning Temprel-lifferene learning Multi-agent systems

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Trilayer Stackelberg Game Approach for Robustly Power Management in Community Grids

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第6期17卷 4073-4083页

作者： Qiu, Haifeng Gu, Wei Wang, Lu Pan, Guangsheng Xu, Yinliang Wu, Zhi Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

Multiuncertainties in community grids bring enormous challenges to system scheduling. To reply to renewable uncertainties on the prosumer side, this article proposes a trilayer stackelberg game (SG) based robust power management method for a typical community grid. Firstly, in the upper-layer, the supplier acts as a leader to determine the trading prices according to prosumers' power demands in the normal scenario. The scheduling of prosumers before and after uncertainties is formulated as an inner-loop SG model in the middle- and lower-layers, and a linear robust counterpart model is developed to address the nonlinearity and nonconvexity of traditional robust models, thereby ensuring the convergence of the robust equilibrium solution. Second, to solve the trilayer SG model effectively, the existence and uniqueness of the equilibrium solution are proved based on a multistep backward induction method. Third, a novel two-stage distributed iterative algorithm is further exploited to avoid the nested iterations in existing solution methods. Furthermore, the possible oscillation in iterative optimization is also prevented to improve the computational efficiency. Case studies illustrate the effectiveness of the proposed trilayer SG model, and the developed distributed algorithm requires fewer iterations and less solution time comparing to other algorithms. Besides, this method shows better scalability and practicality with the expansion of the grid-scale.

关键词： Robustness Job shop scheduling Optimal scheduling Games Uncertainty Iterative methods distributed algorithm optimal power scheduling robust optimization stackelberg game (SG) transactive pricing

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Arbitrary pattern formation by asynchronous opaque robots with lights

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THEORETICAL COMPUTER SCIENCE 2021年 849卷 138-158页

作者： Bose, Kaustav Kundu, Manash Kumar Adhikary, Ranendu Sau, Buddhadeb Jadavpur Univ Dept Math Kolkata India Gayeshpur Govt Polytech Kalyani W Bengal India

The ARBITRARY PATTERN FORMATION problem asks for a distributed algorithm that moves a set of autonomous mobile robots to form any arbitrary pattern given as input. The robots are assumed to be autonomous, anonymous and identical. They operate in Look-Compute Move cycles under an asynchronous scheduler. The robots do not have access to any global coordinate system. The movement of the robots is assumed to be rigid, which means that each robot is able to reach its desired destination without interruption. The existing literature that investigates this problem, considers robots with unobstructed visibility. This work considers the problem in the more realistic obstructed visibility model, where the view of a robot can be obstructed by the presence of other robots. The robots are assumed to be punctiform and equipped with visible lights that can assume a constant number of predefined colors. We have studied the problem in two settings based on the level of consistency among the local coordinate systems of the robots: two axis agreement (they agree on the direction and orientation of both coordinate axes) and one axis agreement (they agree on the direction and orientation of only one coordinate axis). In both settings, we have provided a full characterization of initial configurations from where any arbitrary pattern can be formed. (C) 2020 Elsevier B.V. All rights reserved.

关键词： distributed algorithm Arbitrary pattern formation Leader election Autonomous robots Opaque robots Luminous robots Obstructed visibility Asynchronous scheduler Look-compute-move cycle

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distributed Alternating Direction Method of Multipliers for Multi-Objective Optimization

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Advances in Pure Mathematics 2022年第4期12卷 249-259页

作者： Hui Deng Yangdong Xu Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing China

In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.

关键词： Alternating Direction Method of Multipliers distributed algorithm Multi-Objective Optimization Multi-Agent System

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distributed (Δ+1)-COLORING VIA ULTRAFAST GRAPH SHATTERING

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SIAM JOURNAL ON COMPUTING 2020年第3期49卷 497-539页

作者： Chang, Yi-Jun Li, Wenzheng Pettie, Seth Swiss Fed Inst Technol Inst Theoret Studies CH-8092 Zurich Switzerland Stanford Univ Comp Sci Dept Stanford CA 94305 USA Univ Michigan EECS Ann Arbor MI 48109 USA

Vertex coloring is one of the classic symmetry breaking problems studied in distributed computing. In this paper we present a new algorithm for (Delta+1)-list coloring in the randomized LOCAL model running in O(Det(d)(polylogn)) time, where Det(d)(n') is the deterministic complexity of (deg+1)-list coloring on n'-vertex graphs. (In this problem, each v has a palette of size deg(v)+1.) This improves upon a previous randomized algorithm of Harris, Schneider, and Su [J. ACM, 65 (2018), 19] with complexity O(root log Delta+log log n+Det(d)(poly log n)) = O(root log n). Unless Delta is small, it is also faster than the best known deterministic algorithm of Fraigniaud, Heinrich, and Kosowski [Proceedings of the 57th Annual IEEE Symposium on Foundations of Computer Science (FOCS), 2016] and Barenboim, Elkin, and Goldenberg [Proceedings of the 38th Annual ACM Symposium on Principles of distributed Computing (PODC), 2018], with complexity O(root Delta log log* Delta + log* n). Our algorithm's running time is syntactically very similar to the Omega(Det(poly log n)) lower bound of Chang, Kopelowitz, and Pettie [SIAM J. Comput., 48 (2019), pp. 122-143], where Det(n') is the deterministic complexity of (Delta + 1)-list coloring on n'-vertex graphs. Although distributed coloring has been actively investigated for 30 years, the best determin- istic algorithms for (deg +1)- and (Delta + 1)-list coloring (that depend on n' but not Delta) use a black-box application of network decompositions. The recent deterministic network decomposition algorithm of Rozhoil and Ghaffari [Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing (STOC), 2020] implies that Det(d)(n') and Det(n') are both poly(log n'). Whether they are asymptotically equal is an open problem.

关键词： distributed algorithm local model graph coloring

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distributed Nash Equilibrium Seeking for Aggregative Games over Weight-Unbalanced Digraphs

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IFAC-PapersOnLine 2022年第3期55卷 90-95页

作者： Dong Wang Pan Chen Mingfei Chen Jie Lian Wei Wang The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

This paper investigates a Nash equilibrium seeking problem of aggregative games with linear coupling constraints, where the cost function of each player is coupled through an aggregative function. All players seek the Nash equilibrium by exchanging decision information with neighbors over strongly connected and weight-unbalanced digraphs. A novel distributed continuous-time algorithm is designed by using the Lagrangian multiplier method and the dynamic average consensus method. Then, we show exponential convergence of the designed algorithm by utilizing stability theory of nonlinear systems. Finally, a numerical simulation is provided to verify the effectiveness of our algorithm.

关键词： distributed algorithm aggregative games coupling constraints digraphs

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Adaptive distributed optimization algorithms for Euler-Lagrange systems

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AUTOMATICA 2020年 119卷 109060-109060页

作者： Zou, Yao Meng, Ziyang Hong, Yiguang Tsinghua Univ Dept Precis Instrument Beijing 100084 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

This paper investigates the distributed optimization problem of a group of Euler-Lagrange (EL) systems subject to unavailable inertial parameters. A local cost function is assigned to each agent and the sum of all the local cost functions is considered as the global one. Under widely used assumptions, an adaptive distributed algorithm is proposed such that all the agent states converge to the specified point minimizing the global cost function in a cooperative manner. In particular, by introducing a novel auxiliary system with adaptive gains, the proposed optimization algorithm is privacy-preserving such that no actual state of any agent is necessary for other agents. Moreover, the proposed optimization algorithm is fully distributed in the sense that the optimization objective is achieved without knowledge of global graph information, explicit global cost function as well as strongly convex and Lipschitz constants associated with all local cost functions. Numerical simulations are illustrated to validate the theoretical results. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： distributed algorithm distributed optimization Euler-Lagrange system Gain adaptation

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Privacy-Preserving Database Assisted Spectrum Access for Industrial Internet of Things: A distributed Learning Approach

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2020年第8期67卷 7094-7103页

作者： Zhang, Mengyuan Chen, Jiming He, Shibo Yang, Lei Gong, Xiaowen Zhang, Junshan Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Univ Alibaba Zhejiang Univ Joint Inst Frontier Technol Hangzhou 310027 Peoples R China Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

Industrial Internet of Things (IIoT) has been shown to be of great value to the deployment of smart industrial environment. With the immense growth of Internet of Things (IoT) devices, dynamic spectrum sharing is introduced, envisaged as a promising solution to the spectrum shortage in IIoT. Meanwhile, cyber-physical safety issue remains to be a great concern for the reliable operation of IIoT system. In this article, we consider the dynamic spectrum access in IIoT under a received signal strength-based adversarial localization attack. We employ a practical and effective power perturbation approach to mitigate the localization threat on the IoT devices and cast the privacy-preserving spectrum sharing problem as a stochastic channel selection game. To address the randomness induced by the power perturbation approach, we develop a two-timescale distributed learning algorithm that converges almost surely to the set of correlated equilibria of the game. The numerical results show the convergence of the algorithm and corroborate that the design of two-timescale learning process effectively alleviates the network throughput degradation brought by the power perturbation procedure.

关键词： distributed algorithm game theory industrial cyber-physical system security spectrum access

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distributed Energy Harvesting Management algorithm in Internet of Things Devices 6

Distributed Energy Harvesting Management Algorithm in Intern...

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IEEE International Conference on Consumer Electronics-Taiwan (IEEE ICCE-TW)

作者： Lee, Chao Yang Natl Formosa Univ Dept Aeronaut Engn Yunlin Cty Taiwan

ISBN: (纸本)9781728132792

Nowadays, we have seen rapid growth in the field of the internet of things (IoT). The IoT technologies can provide ubiquitous and diverse applications. Because of the requirements of IoT devices are usually lightweight and portable, so most of the energy source in an IoT system is powered from finite battery-operated energy. Thereby, the limited lifespan of the battery is the major limitations on operational network lifetime. The energy harvesting methods can be applied to extend their lifetimes for power. However, due to the renewable energy characteristics of low recharging rates in harvesting ambient energy, energy harvesting is unable to provide sufficient energy for system operation. In this version, this study designs the distributed energy harvesting management strategy which aims to prolong the IoT system lifetime to make the harvesting ambient energy useful. Each device builds a sub-network and carries out the linear programming to management the energy utilization. The proposed algorithm can dynamically control energy consumption in each device to prolong the network lifetime for the battery-based IoT system.

关键词： Internet of Things Energy Harvesting Energy Management distributed algorithm

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Data dependability based bimodal encryption scheme for distributed routing in wireless sensor networks

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2020年第4期13卷 1142-1151页

作者： Brindha, P. Senthilkumar, A. MGR Coll Dept Comp Sci Hosur TN India Arignar Anna Govt Arts Coll Dept Comp Sci Namakkal India

The development of smart cities and their fast-paced deployment leads to the creation of massive amount of data at exceptional rates. Due to the data gathering and tracking nature of wireless sensor networks (WSN), it finds applications in various domains. Presently, security is considered as an important design issue of developing multipath routing protocols in WSN. The existing routing techniques either concentrate on energy efficiency or security issues with maximum resource utilization. This paper focuses on the idea of incorporating encryption algorithms on multihop routing mechanism in WSN. Here, routing takes place by the Augmented Tree Based Routing (ATR) which is a tree-based multipath routing method. To attain higher level of security, two encryption algorithms namely lightweight encryption algorithm (LEA) and two phase hybrid cryptography algorithm (THCA) are employed based on the data dependability. The encryption algorithms are utilized based on data dependability where general data is encrypted by LEA and other confidential data are encrypted by THCA. i.e. the LEA is used to send proactive data and the important reactive data is encrypted by THCA. For routing purposes, Augmented Tree Based Routing (ATR) is used. From experimentation, it is evident that the presented method exhibits enhanced performance by means of throughput, control overhead and network lifetime even though there exist some misbehaving nodes in the network.

关键词： distributed algorithm Big data WSN Encryption Security Reliability Multipath routing

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