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A distributed and Robust Security-Constrained Economic Dispatch Algorithm Based on Blockchain

IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第1期37卷 691-700页

作者： Chen, Sijie Zhang, Ling Yan, Zheng Shen, Zeyu Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China

distributed optimization algorithms for security-constrained economic dispatch (SCED) problems have been the subject of significant research interest in recent years. However, existing distributed SCED algorithms can be ineffective in the presence of malicious participants and inefficient in the absence of a coordinator. On the other hand, blockchain, an emerging technique known as the trust machine, has not shown its potential to address the above challenges in state-of-the-art literature. This paper proposes a blockchain-based distributed SCED algorithm. Using blockchain to form a coordination committee and enable balance among committee members, the proposed method allows the use of hierarchical SCED algorithms in the absence of a coordinator and can disable malicious participants. Numerical results show the robustness and necessity of the proposed blockchain-based SCED algorithm, by comparing the SCED results with and without blockchain.

关键词： Blockchain Optimization Economics Technological innovation Power transmission lines Mathematical model distributed algorithms distributed optimization security-constrained economic dispatch blockchain energy trading robust and resilient algorithm

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distributed Algorithm Design for Aggregative Games of Euler-Lagrange Systems and Its Application to Smart Grids

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第8期52卷 8315-8325页

作者： Deng, Zhenhua Cent South Univ Sch Automat Changsha 410075 Peoples R China

The aggregative games are addressed in this article, in which there are coupling constraints among decisions and the players have Euler-Lagrange (EL) dynamics. On the strength of gradient descent, state feedback, and dynamic average consensus, two distributed algorithms are developed to seek the variational generalized Nash equilibrium (GNE) of the game. This article analyzes the convergence of two algorithms by utilizing singular perturbation analysis and variational analysis. The two algorithms exponentially and asymptotically converge to the variational GNE of the game, respectively. Moreover, the results are applied to the electricity market games of smart grids. By the algorithms, turbine-generator systems can seek the variational GNE of electricity markets autonomously. Finally, simulation examples verify the methods.

关键词： Games Heuristic algorithms Electricity supply industry Smart grids Couplings distributed algorithms Communication networks Aggregative games cyber-physical systems (CPSs) Euler-Lagrange systems generalized Nash equilibrium (GNE) multiagent systems

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distributed Generalized Nash Equilibrium Seeking and Its Application to Femtocell Networks

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第4期52卷 2505-2517页

作者： Li, Zhongguo Li, Zhenhong Ding, Zhengtao Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL Lancs England Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

In this article, distributed algorithms are developed to search the generalized Nash equilibrium (NE) with global constraints. Relations between the variational inequality and the NE are investigated via the Karush-Kuhn-Tucker (KKT) optimal conditions, which provide the underlying principle for developing the distributed algorithms. Two time-varying consensus schemes are proposed for each agent to estimate the actions of others, by which a distributed framework is established. The algorithm with fixed-gains is designed with certain system knowledge, while the adaptive algorithm is proposed to address the problem when the system parameters are not available. The asymptotic convergence to the NE is established through the Lyapunov theory and the consensus theory. The power control problem in a femtocell network is formulated as a Nash game and is solved by the proposed algorithms. The simulation results are provided to verify the effectiveness of theoretical development.

关键词： Games Convergence Cost function Femtocell networks distributed algorithms Power control Nash equilibrium Consensus distributed algorithm femtocell networks game theory multiagent systems (MASs) power control

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Jacobi-Style Iteration for distributed Submodular Maximization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第9期67卷 4687-4702页

作者： Du, Bin Qian, Kun Claudel, Christian Sun, Dengfeng Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China Univ Texas Austin Dept Civil Architectural & Environm Engn Austin TX 78712 USA Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47906 USA

This article presents a novel Jacobi-style iteration algorithm for solving the problem of distributed submodular maximization, in which multiple agents determine their strategies from the private sets so that a global, nonseparable submodular objective function is jointly maximized. Building on the multilinear extension of the submodular function, we expect to achieve the solution from a probabilistic, rather than deterministic, perspective, and thus, transfer the considered problem from the discrete domain into the continuous domain. Since it is observed that an unbiased estimation of the gradient of multilinear extension function can be obtained by sampling the agents' local strategies, a projected stochastic gradient algorithm is proposed to solve the problem. Our algorithm enables simultaneous updates among all individual agents and guarantees to converge asymptotically to a desirable equilibrium solution. Such an equilibrium solution is shown to be at least 1/2 suboptimal, which is comparable to the state-of-art in the literature. The convergence rate, which is characterized by the running average of gradient mapping, is proved to be O(1/T), where T is the number of iterations. Moreover, we further enhance the proposed algorithm by handling the scenario in which agents' communication delays are present. The enhanced algorithm admits a more realistic distributed implementation of our approach. Finally, a movie recommendation task is conducted on a real-world movie rating dataset to validate the numerical performance of our algorithms.

关键词： distributed algorithms nash equilibrium stochastic gradient descent submodular maximization

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distributed Deployment in UAV-Assisted Networks for a Long-Lasting Communication Coverage

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IEEE SYSTEMS JOURNAL 2022年第3期16卷 4130-4138页

作者： Liu, Xiaojie Wang, Xingwei Jia, Jie Lv, Jianhui Bartolini, Novella Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China Tsinghua Univ Int Grad Sch Shenzhen Shenzhen 518057 Peoples R China Sapienza Univ Rome Dept Comp Sci & Technol I-110819 Rome Italy

Due to the advantage of flexible and quick movement, unmanned aerial vehicles (UAVs) have been widely utilized in assisting wireless communications. A challenging problem is how to deploy UAVs to provide communication services for more user devices for a long term while minimizing the number of UAVs. In this article, we propose a UAV network system with UAV deletion due to redundancy or energy exhaustion and UAV insertion to provide long-lasting communication services. Then, a distributed algorithm based on the virtual Coulomb force and Voronoi diagram is proposed to deploy UAVs improving the communication coverage and turning redundant UAVs off. On the one hand, in order to improve the communication coverage, we propose two moving schemes to move UAVs. On the other hand, in order to save energy, we propose a sleeping scheme to turn several redundant UAVs off. Particularly, the redundancy of a UAV is evaluated according to a proposed definition of the average local coverage rate. Simulation results demonstrate that the proposed algorithm can deploy a minimal number of UAVs to provide enough communication coverage and the presented network system can offer long-lasting service.

关键词： Unmanned aerial vehicles Network systems Wireless communication Redundancy Force Sensors distributed algorithms Communication coverage distributed deploy-ment long-lasting service unmanned aerial vehicle (UAV) assisted networks

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distributed computation and reconfiguration in actively dynamic networks

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distributed COMPUTING 2022年第2期35卷 185-206页

作者： Michail, Othon Skretas, George Spirakis, Paul G. Univ Liverpool Dept Comp Sci Ashton St Liverpool L69 3BX Merseyside England Univ Patras Comp Engn & Informat Dept Patras Greece

We study here systems of distributed entities that can actively modify their communication network. This gives rise to distributed algorithms that apart from communication can also exploit network reconfiguration to carry out a given task. Also, the distributed task itself may now require a global reconfiguration from a given initial network G(s) to a target network G(f) from a desirable family of networks. To formally capture costs associated with creating and maintaining connections, we define three edge-complexity measures: the total edge activations, the maximum activated edges per round, and the maximum activated degree of a node. We give (poly)log(n) time algorithms for the task of transforming any G(s) into a G(f) of diameter (poly)log(n), while minimizing the edge-complexity. Our main lower bound shows that Omega(n) total edge activations and Omega(n/ log n) activations per round must be paid by any algorithm (even centralized) that achieves an optimum of Theta(log n) rounds. We give three distributed algorithms for our general task. The first runs in O(log n) time, with at most 2n active edges per round, a total of O(n log n) edge activations, a maximum degree n - 1, and a target network of diameter 2. The second achieves bounded degree by paying an additional logarithmic factor in time and in total edge activations. It gives a target network of diameter O(log n) and uses O(n) active edges per round. Our third algorithm shows that if we slightly increase the maximum degree to polylog(n) then we can achieve o(log(2) n) running time.

关键词： distributed algorithms Dynamic networks Reconfiguration Transformation Polylogarithmic time Edge complexity

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Robust distributed Optimization With Randomly Corrupted Gradients

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 3484-3498页

作者： Turan, Berkay Uribe, Cesar A. Wai, Hoi-To Alizadeh, Mahnoosh Univ Calif Santa Barbara Elect & Comp Engn Dept Santa Barbara CA 93106 USA Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA Chinese Univ Hong Kong Syst Engn & Engn Management Shatin Hong Kong Peoples R China

In this paper, we propose a first-order distributed optimization algorithm that is provably robust to Byzantine failures-arbitrary and potentially adversarial behavior, where all the participating agents are prone to failure. We model each agent's state over time as a two-state Markov chain that indicates Byzantine or trustworthy behaviors at different time instants. We set no restrictions on the maximum number of Byzantine agents at any given time. We design our method based on three layers of defense: 1) temporal robust aggregation, 2) spatial robust aggregation, and 3) gradient normalization. We study two settings for stochastic optimization, namely Sample Average Approximation and Stochastic Approximation. We provide convergence guarantees of our method for strongly convex and smooth non-convex cost functions.

关键词： Optimization Convergence Gradient methods distributed databases Robustness Markov processes Behavioral sciences Cyber-physical systems distributed algorithms gradient methods multi-agent systems optimization methods robustness convergence

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distributed Dynamic Weighted Average Consensus for Disturbed Multiagent Systems in Fixed Time

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第6期9卷 4277-4286页

作者： Li, Zhiyong Chen, Gang Chongqing Univ Coll Automat Chongqing 400044 Peoples R China

This article addresses the dynamic weighted average consensus (DWAC) problem of disturbed first-order multiagent systems, where a group of agents work cooperatively to track the weighted average of multiple time-varying signals. A class of distributed continuous-time algorithms are proposed to achieve DWAC within a fixed settling time, regardless of the initial conditions of the agents' states. Our first algorithm achieves accurate tracking of the desired trajectory by taking advantage of the variable structure property of sign functions. The second algorithm achieves uniformly bounded tracking with adjustable tracking error bounds by applying the continuous control law. Lyapunov method based stability analysis shows that both the algorithms achieve the fixed-time tracking control even when the single-integrator agent dynamics are affected by bounded disturbances. Moreover, the relationships between controller parameters and tracking performance are derived and the upper bounds of settling time are estimated. Finally, the proposed algorithms are applied to solve the distributed time-varying quadratic optimization problem, and the simulation results confirm the effectiveness of the proposed distributed fixed-time DWAC algorithms.

关键词： Heuristic algorithms Trajectory Stability analysis Multi-agent systems Time-varying systems Convergence Consensus algorithm distributed algorithms Bounded disturbances dynamic weighted average consensus (DWAC) distributed algorithms fixed-time convergence multiagent systems

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A Contraction Analysis of Primal-Dual Dynamics in distributed and Time-Varying Implementations

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第7期67卷 3560-3566页

作者： Cisneros-Velarde, Pedro Jafarpour, Saber Bullo, Francesco Univ Calif Santa Barbara Santa Barbara CA 93106 USA

In this article, we provide an overarching analysis of primal-dual dynamics associated with linear equality-constrained optimization problems using contraction analysis. For the well-known standard version of the problem, we establish convergence under convexity and the contracting rate under strong convexity. Then, for a canonical distributed optimization problem, we use partial contractivity to establish global exponential convergence of its primal-dual dynamics. As an application, we propose a new distributed solver for the least-squares problem with the same convergence guarantees. Finally, for time-varying versions of both centralized and distributed primal-dual dynamics, we exploit their contractive nature to establish bounds on their tracking error. To support our analyses, we introduce novel results on contraction theory.

关键词： Convergence Optimization Heuristic algorithms Trajectory Linear programming Standards Time-varying systems Contraction theory distributed algorithms least squares primal-dual (PD) time-varying optimization

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Implementing crossplatform distributed algorithms using standard web technologies 40

Implementing crossplatform distributed algorithms using stan...

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40th Latin American Computing Conference (CLEI)

作者： Martinez, Gonzalo Val, Leonardo UCUDAL FIT Dept Informat & Ciencias Computac Montevideo Uruguay

ISBN: (纸本)9781479961306

This paper presents the development of a distributed computing framework based on standard web technologies. The workload is specified as Javascript code, split into pieces served via HTTP and executed by web workers inside the clients' web browser. Initially developed to run genetic algorithms in a parallel fashion, our implementation has been generalized into a system powerful enough to run any kind of computationally intensive work. It allows running a distributed algorithm in a huge number of different devices. There are no special software requirements since every modern platform with a powerful CPU and a network connection has at least one web browser available. The tests show tasks can be run in web browsers in a secure and efficient manner. We show the great portability and performance achieved using a simple test bed problem.

关键词： distributed algorithms distributed computing world wide web cross-platform Javascript

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