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A Fast Distributed algorithm for Coupled Utility Maximization Problem with Application for Power Control in Wireless Sensor Networks

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JOURNAL OF COMMUNICATIONS AND NETWORKS 2021年第4期23卷 271-280页

作者： Liao, Shengbin Cent China Normal Univ Natl Engn Ctr E Learning Wuhan Peoples R China

This paper investigates a method to distributively solve a Network Utility Maximization (NUM) problem with coupled variables and applies it to study power control in wireless sensor networks (WSNs). We present a dual decomposition-based consistency price algorithm to solve the coupled problem. However, the consistency price algorithm suffers from slow convergence. We then propose a two-step method to address the given issue. The first step is to build up a global consensus problem by introducing slack variables to transform the NUM problem with globally coupled variables into a NUM problem with coupled constraints. The second step is to design a distributed algorithm that combines the first-order gradient/subgradient method and a local consensus algorithm to solve the global consensus problem. The proposed algorithm is a primary algorithm which has faster convergence speed than the consistency price algorithm which is a primary-dual algorithm. Experimental results have demonstrated the effectiveness of our proposed approach.

关键词： Index Terms consensus algorithm network utility maximization power control primary-dual algorithm wireless sensor networks

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Dynamic Spectrum Sensing Under Crash and Byzantine Failure Environments for Distributed Convergence in Cognitive Radio Networks

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IEEE ACCESS 2021年 9卷 23153-23167页

作者： Mustafa, Ali Ul Islam, Muhammad Najam Ahmed, Salman Bahria Univ Dept Elect Engn Islamabad 44000 Pakistan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock 43600 Pakistan Univ Engn & Technol Dept Comp Syst Engn Peshawar 25000 Pakistan

In cognitive networks, efficient spectrum sensing is of great importance for communication of unlicensed secondary users (SU) without interfering with the communication of licensed primary users (PU). Such spectrum sensing requires robust and reliable communication between the SUs to sense the spectrum efficiently under different network circumstances and to make a quick decision for the data transmission. In this paper, we are proposing a decentralized cooperative algorithm for efficient sensing of spectrum in the networked cognitive radios. The proposed algorithm is investigated under crash and Byzantine failure environments to study their behavior and efficiency for consensus. Energy detector module is modeled for each cooperating SU in cognitive radio network for sensing the presence of PU in a dedicated spectrum. Moreover, SU is modeled as agents connected through undirected graphs to simulate communication among them related to the spectrum availability. Multiple simulation scenarios, based on autonomous SU using the proposed distributed consensus algorithm are presented to demonstrate the theoretical development of proposed algorithm to be visualized in real scenarios. The simulation results reveal that the proposed method provides a significant improvement in convergence rate, reliability, and in terms of various key performance indicators.

关键词： Sensors Cognitive radio Receivers consensus algorithm Fading channels Convergence Wireless sensor networks Multi-agent systems energy model distributed estimation unreliable communication distributed consensus control crash and Byzantine failure cognitive radios spectrum sensing

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Cluster-Based Predictive PCC Voltage Control of Large-Scale Offshore Wind Farm

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IEEE ACCESS 2021年 9卷 4630-4641页

作者： Nguyen, Thai-Thanh Kim, Hak-Man Clarkson Univ Dept Elect & Comp Engn Potsdam NY 13699 USA Incheon Natl Univ Dept Elect Engn Incheon 406772 South Korea

A large number of wind turbine generators in the large-scale offshore wind farm system poses a challenge to the wind farm control system due to the computational burden in the central control methods or the complexity of the communication network in the decentralized control strategies. A hybrid control method based on the distributed consensus control and the central model predictive control is proposed in this study to overcome the problem. Typically, the wind turbine generators in the large-scale offshore wind farm system are clustered into several groups. The consensus-based distributed reactive power coordination control is proposed to each cluster and the centralized predictive voltage control is used to manage the total reactive powers of all clusters and regulate the voltage at the point of common coupling. The gradient-descent algorithm for the optimal design of the consensus-based cluster control is presented firstly. Based on the convergence property of the consensus control, the equivalent model of the total reactive power response of each cluster is identified, which is used for the design of the centralized predictive voltage control. Eigenvalue analysis of the proposed predictive control strategy is carried out to verify the stability of the distributed and predictive control systems. The robustness of the proposed predictive controller is evaluated in the conditions of significant model errors due to the communication delay in each cluster. A comparison study with the full distributed control based on consensus algorithm is presented to demonstrate the effectiveness of the proposed control method. The feasibility of the proposed predictive controller is evaluated by the control-hardware-in-the-loop simulation using OPAL-RT Technologies. An additional comparison study in term of computation time with the central control method is also carried out. Real-time simulation results show the superior performance of the proposed hybrid method compared to the

关键词： Distributed control reactive power control consensus algorithm wind farm control predictive voltage control model predictive control

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Runtime Analysis of the Compact Genetic algorithm on the LeadingOnes Benchmark

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IEEE Transactions on Evolutionary Computation 2025年

作者： Chwialkowski, Marcel Doerr, Benjamin Krejca, Martin S. Institut Polytechnique de Paris École Polytechnique France CNRS France

The compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA)– another simple EDA–, the cGA has been subject to extensive mathematical runtime analyses, often showcasing a similar or even superior performance to competing approaches. Surprisingly though, up to date and in contrast to the UMDA and many other heuristics, we lack a rigorous runtime analysis of the cGA on the LEADINGONES benchmark–one of the most studied theory benchmarks in the domain of evolutionary computation. We fill this gap in the literature by conducting a formal runtime analysis of the cGA on LEADINGONES. For the cGA’s single parameter–called the hypothetical population size–at least polylogarithmically larger than the problem size, we prove that the cGA samples the optimum of LEADINGONES with high probability within a number of function evaluations quasi-linear in the problem size and linear in the hypothetical population size. For the best hypothetical population size, our result matches, up to polylogarithmic factors, the typical quadratic runtime that many randomized search heuristics exhibit on LEADINGONES. Our analysis exhibits some noteworthy differences in the working principles of the two algorithms which were not visible in previous works. 1997-2012 IEEE.

关键词： consensus algorithm

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An improved practical Byzantine fault tolerance algorithm for aggregating node preferences

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SCIENTIFIC REPORTS 2024年第1期14卷 1-25页

作者： Liu, Xu Zhu, Junwu Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China

consensus algorithms play a critical role in maintaining the consistency of blockchain data, directly affecting the system's security and stability, and are used to determine the binary consensus of whether proposals are correct. With the development of blockchain-related technologies, social choice issues such as Bitcoin scaling and main chain forks, as well as the proliferation of decentralized autonomous organization (DAO) applications based on blockchain technology, require consensus algorithms to reach consensus on a specific proposal among multiple proposals based on node preferences, thereby addressing the multi-value consensus problem. However, existing consensus algorithms, including Practical Byzantine Fault Tolerance (PBFT), do not support nodes expressing preferences. Instead, the proposal to reach consensus is directly decided by specific nodes, with other nodes merely verifying the proposal's validity, which can easily result in monopolistic or dictatorial outcomes. In response, we proposed the Aggregating Preferences with Practical Byzantine Fault Tolerance (AP-PBFT) consensus algorithm, which allows nodes to express preferences for multiple proposals. AP-PBFT ensures the validity of consensus results through a consensus output protocol, and incentivizes nodes to act honestly during the consensus process by incentive mechanism. First, AP-PBFT leverages Verifiable Random Function to select both consensus nodes and a primary node from the candidates. The primary node gathers proposals, assembles them into a proposal package, and broadcasts it to other consensus nodes. The consensus nodes independently vote to express their preferences for different proposals in the package, execute the consensus output protocol to reach local consensus, and the primary node aggregates these results to form the global consensus. Once the global consensus is finalized, AP-PBFT evaluates node behavior based on the consensus output protocol, penalizes nodes that acted malic

关键词： Distributed system Blockchain consensus algorithm Voting Evolutionary game Incentive mechanism

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Decentralized Rank-Adaptive Matrix Factorization - Part I: algorithm Development

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IEEE Transactions on Signal Processing 2024年

作者： Jiao, Yuchen Gu, Yuantao Chang, Tsung-Hui Luo, Zhi-Quan Tom Beijing Institute of Technology School of Information and Electronics Beijing100081 China Tsinghua University Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Beijing100084 China The Chinese University of Hong Kong CUHKSZ Shenzhen518172 China Guangdong Province Shenzhen518172 China

Factorizing a low-rank matrix into two matrix factors with low dimensions from its noisy observations is a classical but challenging problem arising from real-world applications. This paper develops decentralized matrix factorization algorithms, i.e., factorizing a matrix whose columns are stored distributively over a network without a central agent. The performance of existing algorithms relies heavily on the accuracy of the matrix rank estimate. However, acquiring an accurate estimate is difficult in the distributed setting. In this paper and its Part II, we address this problem by introducing a novel regularization into the objective function to induce the solution with correct rank. Based on this, we propose a rank-adaptive decentralized MF algorithm. In Part I, we delineate the algorithm development from the centralized with known rank, decentralized with known rank, to the rank-adaptive decentralized settings. For the centralized algorithm, we present the first globally linear convergence analysis for the alternating gradient descent method. In the Part II, we analyze conditions for which the proposed rank-adaptive decentralized MF algorithm converges to the global solution with the correct rank. Numerical experiments based on both synthetic and real-world datasets are presented in this paper to demonstrate the effectiveness of the proposed algorithms and corroborate the theoretical claims. © 1991-2012 IEEE.

关键词： consensus algorithm

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Unified line-of-sight: A guidance algorithm with integral wind-up mitigation and turning assist for USVs

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Ocean Engineering 2024年 314卷

作者： Tanakitkorn, Kantapon Phoemsapthawee, Surasak Thaweewat, Nonthipat Jungrungruengtaworn, Sirirat Department of Maritime Engineering Faculty of International Maritime Studies Kasetsart University Thailand

Guidance algorithm is a key part in waypoint navigation for USVs. In this paper, a novel guidance algorithm named the unified line-of-sight (ULOS) is introduced. The ULOS algorithm is developed based on the concept of the integral-based LOS algorithm. However, a special technique is employed in the ULOS algorithm to address the integral wind-up issue commonly found in other integral-based LOS algorithms in the literature. In addition, a heading compensation term based on sway velocity is incorporated into the algorithm. This sway compensation term enhances the vehicle's ability to execute sharp turns effectively. The ULOS algorithm was benchmarked against four existing guidance algorithms in various tasks through numerical simulations. The comprehensive results have revealed the superior performance of the ULOS algorithm over the other algorithms. © 2024 Elsevier Ltd

关键词： consensus algorithm

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A consensus-based square-root cubature Kalman filter for manoeuvring target tracking in sensor networks

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第15期42卷 3052-3062页

作者： Zhong, Zenan Zhao, Enjiao Zheng, Xin Zhao, Xinhua Harbin Engn Univ Coll Automat 145 Nantong St Harbin 150001 Heilongjiang Peoples R China Minist Educ Engn Res Ctr Nav Instruments Beijing Peoples R China Shanghai Acad Spaceflight Technol Shanghai Peoples R China

In this paper, a novel distributed tracking method is proposed for the problem of manoeuvring target tracking in sensor networks. Firstly, an adaptive adjustment tracking model is established by extended state observer (ESO) theory. Then, the consensus-based square-root cubature Kalman filter (SCKF) algorithm is proposed in order to improve the global accuracy and stability. In addition, the integrated model could reduce the influence of measurement noise. Finally, simulation is performed to verify the effectiveness of the scheme, whereby comparison results show that the estimation accuracy of the method proposed is higher than that of the traditional ESO and SCKF.

关键词： Distributed state estimation manoeuvring target tracking square-root cubature Kalman filter extended state observer consensus algorithm

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Practical Scalable Synchronization in Leader-Follower Networks of Nonlinear Heterogeneous Agents Using High-Gain Observers

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2021年第2期8卷 530-541页

作者： Chowdhury, Dhrubajit Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

We consider the synchronization problem in the leader-follower framework with a single leader in the network. For a leader-follower network, the smallest eigenvalue of the grounded Laplacian matrix decreases toward zero with an increase in the network size for undirected graphs when the nodes in the graph have bounded neighborhood (fixed number of neighbors) and bounded edge weights. This affects the controller gain of standard nonlinear control approaches used for achieving synchronization as the controller gain is inversely proportional to the smallest eigenvalue of the grounded Laplacian matrix. As a result, it becomes difficult to realize the controllers in practice due to the significant high gain. In this article, we propose a scalable distributed algorithm for the synchronization of second-order nonlinear heterogeneous multiagent systems. First, we assume that the relative state derivates are available for feedback, and we show that the synchronization error can be made arbitrarily small by tuning a particular controller parameter. It is shown that the control signal is bounded uniformly with respect to this controller parameter, and the system performance does not degrade with an increase in network size. Next, we realize the controller using a reduced-order high-gain observer, and we show that the synchronization error can be made arbitrarily small by tuning a controller and observer parameter, respectively. We show that the control signal is bounded uniformly with respect to these parameters. Finally, we demonstrate the efficacy of the proposed controller with two examples: 1) a network of oscillators on the IEEE 300-bus system and 2) a platoon of vehicles.

关键词： Laplace equations Eigenvalues and eigenfunctions Synchronization Observers Power system dynamics Vehicle dynamics consensus algorithm High-gain observer nonlinear agents platoon scalable synchronization

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BSSFFS: blockchain-based sybil-secured smart forest fire surveillance

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第5期13卷 2479-2510页

作者： Datta, Sreemana Kumar, Sonal Sinha, Ditipriya Das, Ayan Kumar Natl Inst Technol Patna 800005 Bihar India Birla Inst Technol Mesra Patna Bihar India

Forest fires can have multiple engenderers including lighting and thunder, unauthorized human exercises, and unrestrained fire projects. In addition to precautions, quick detection, immediate communication, and prompt response are critical to keeping losses to life and property at bay. This work presents Sybil-secured Smart Forest Fire Surveillance based on Blockchain Technology. This work encompasses a detailed three-layer architecture consisting of IoT Layer (sensors planted in the forest), Fog Layer (a decentralized network of fog nodes), and Cloud Layer (Blockchain-based cloud storage capable of computations, decision making, and forecasting). Further, a Blockchain technology-based framework for secured and Sybil-protected data transmission in wildfire scenarios is proposed here. Moreover, an energy-efficient Blockchain consensus algorithm has also been designed and implemented here. Numerous QoS metrics have been considered and used for the evaluation of the proposed system. This work opens the gate to a vista of further research for secure data transmission in wildfire scenarios.

关键词： Forest fire Blockchain Sybil attack consensus algorithm Fog nodes

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