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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wang, Zhuping Wang, Lei Zhang, Hao Chen, Qijun Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9781538616451

In this paper, we use the combination of graph theory and consensus algorithm to realize the formation control of nonholonomic wheeled robots. A novel edge-weight function is designed so that the desired formation-shape can be achieved. In addition, the consensus problem is usually solved on the assumption that the robots are modeled as particle model, but this assumption is not suitable when we are dealing with real robots, so we design a novel algorithm to meet the nonholonomic constraints of wheeled robots. Finally, the effectiveness of the proposed method is verified by the simulation results of an example.

关键词： formation nonholonomic graph theory consensus algorithms

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Agreeing under Randomized Network Dynamics

Agreeing under Randomized Network Dynamics

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American Control Conference (ACC)

作者： Shi, Guodong Johansson, Karl Henrik Royal Inst Technol Sch Elect Engn ACCESS Linnaeus Ctr S-10044 Stockholm Sweden

ISBN: (纸本)9781457710964;9781457710957

In this paper, we study randomized consensus processing over general random graphs. At time step k, each node will follow the standard consensus algorithm, or stick to current state by a simple Bernoulli trial with success probability p(k). Connectivity-independent and arc-independent graphs are defined, respectively, to capture the fundamental independence of random graph processes with respect to a consensus convergence. Sufficient and/or necessary conditions are presented on the success probability sequence for the network to reach a global a.s. consensus under various conditions of the communication graphs. Particularly, for arc-independent graphs with simple self-confidence condition, we show that Sigma(k) p(k) = infinity is a sharp threshold corresponding to a consensus 0 - 1 law, i.e., the consensus probability is 0 for almost all initial conditions if Sigma(k) p(k) converges, and jumps to 1 for all initial conditions if Sigma(k) p(k) diverges.

关键词： consensus algorithms Random graphs Dynamics Randomization Threshold

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Radar sensor networks with distributed detection capabilities

Radar sensor networks with distributed detection capabilitie...

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2008 IEEE Radar Conference

作者： Pescosolido, Loreto Barbarossa, Sergio Scutari, Gesualdo Univ Roma La Sapienza INFO COM Dept Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9781424415380

The goal of this work is to propose a distributed detection method for a network of radars interchanging their measurements through pulse-position modulation. The proposed approach does not need the presence of a fusion center and it is then fully decentralized. The approach is robust against node failures, as the only requirement to get the network gain is that the whole network remains connected, i.e., for every pair of nodes, there is a path, possibly composed of multiple hops, joining them. The decentralized approach is based on a distributed consensus mechanism. However, when conventional consensus algorithms are implemented over realistic channels, the receiver noise gives rise to an error on the final decision statistic that linearly increases with time. We avoid this inconvenient by properly modifying the consensus algorithm to make it suitable for communications over noisy channels. We analyze the performance of the proposed system considering the presence of both observation and communication noise present in the interaction among the radars. In particular, we show that even with a non-coherent integration, the whole system is able to achieve a sensitivity gain equal to the number of radars.

关键词： Radar sensor networks distributed detection consensus algorithms

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Information-weighted consensus Filtering under Limited Communication

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IFAC-PapersOnLine 2023年第2期56卷 10546-10551页

作者： Shiraz Khan Inseok Hwang School of Aeronautics and Astronautics Purdue University West Lafayette IN 47906 USA

A distributed sensor network refers to a set of stationary or mobile agents equipped with sensors, each of which observes and estimates the state of some system, while utilizing the communication channels of the sensor network to improve the overall state estimation performance of the network. In particular, consensus-based distributed state estimation algorithms can compute accurate state estimates of the system at each agent under relaxed observability conditions, using a low communication bandwidth. A drawback of many existing consensus-based approaches is that they are developed under the presumption that a large number of consensus subiterations are used, which may not be possible in large-scale sensor networks, in the presence of communication delays and/or energy constraints. In this paper, we develop an extension of the Information-weighted consensus Filter (ICF) for the case where only a limited number of consensus subiterations are possible. Through mathematical analysis and numerical simulations, it is shown that the proposed algorithm improves upon the existing algorithms under identical computation and communication requirements.

关键词： Networked systems consensus algorithms distributed state estimation multi-agent systems wireless sensor networks

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Communication Complexity and Energy Efficient consensus Algorithm

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IFAC Proceedings Volumes 2010年第19期43卷 209-214页

作者： Yilin Mo Bruno Sinopoli Carnegie Mellon University Pittsburgh PA 15213 USA

In this paper, we analyze distributed average consensus algorithms, both deterministic and gossip based, with respect to a new metric related to the energy cost of communication among agents. We first introduce a new notion of communication complexity as a metric to assess the energy efficiency properties of consensus algorithms. We provide explicit formulas to compute the communication complexity of deterministic algorithms and gossip based algorithms depending on different stopping criteria. We also show that the gossip based algorithms have less communication complexity than the deterministic counterparts under mild conditions, usually satisfied by a large number of networks. We also show that the gossip algorithm with minimum communication complexity can be effectively computed as the solution of a convex optimization problem.

关键词： consensus algorithms Randomized algorithms Communication Complexity

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Average consensus over Mobile Wireless Sensor Networks: Weight Matrix Guaranteeing Convergence without Reconfiguration of Edge Weights

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SENSORS 2020年第13期20卷 3677.-3677.页

作者： Kenyeres, Martin Kenyeres, Jozef Slovak Acad Sci Inst Informat Dubravska Cesta 9 Bratislava 84507 45 Slovakia Sipwise GmbH Europaring F15 A-2345 Brunn Austria

Efficient data aggregation is crucial for mobile wireless sensor networks, as their resources are significantly constrained. Over recent years, the average consensus algorithm has found a wide application in this technology. In this paper, we present a weight matrix simplifying the average consensus algorithm over mobile wireless sensor networks, thereby prolonging the network lifetime as well as ensuring the proper operation of the algorithm. Our contribution results from the theorem stating how the Laplacian spectrum of an undirected simple finite graph changes in the case of adding an arbitrary edge into this graph. We identify that the mixing parameter of Best Constant weights of a complete finite graph with an arbitrary order ensures the convergence in time-varying topologies without any reconfiguration of the edge weights. The presented theorems and lemmas are verified over evolving graphs with various parameters, whereby it is demonstrated that our approach ensures the convergence of the average consensus algorithm over mobile wireless sensor networks in spite of no edge reconfiguration.

关键词： average consensus consensus algorithms data aggregation distributed algorithms mobile computing mobile wireless sensor networks sensor fusion

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Towards a Blockchain Architecture for Animal Sanitary Control 23

Towards a Blockchain Architecture for Animal Sanitary Contro...

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23rd International Conference on Enterprise Information Systems (ICEIS)

作者： Descovi, Glenio Maran, Vinicius Ebling, Denilson Machado, Alencar Univ Fed Santa Maria Technol Ctr Santa Maria RS Brazil Univ Fed Santa Maria Polytech Coll Santa Maria RS Brazil Univ Fed Santa Maria Lab Ubiquitous Mobile & Appl Comp LUMAC Santa Maria RS Brazil

ISBN: (纸本)9789897585098

It is known that Blockchain technology is widely used in cryptocurrency transactions, the technology has become popular with Bitcoin but recently, it has been applied in many areas, including the animal sanitary control. It can be said that Blockchain is an immutable ledger, where systems can store transactions, documents, history, countless data generated in any process. In Brazil, an animal sanitary control platform called Plataforma de Defesa Sanitaria Animal (PDSA-RS) was recently proposed and is widely used in the state of Rio Grande do Sul. Actually, the PDSA-RS uses a centered server with relevant information for the animal sanitary control process. This work presents the process of defining and integrating the PDSA-RS with a private Blockchain managed by a software architecture. The main goal of this integration is to give traceability, immutability and transparency in the existing process and the data generated in the certification of poultry establishments that are part of the animal sanitary control. In the evaluation of the integration process, it was possible to observe that it the blockchain extension offered persistence. anonymity and auditability of the information related to the animal sanitary control.

关键词： Animal Sanitary Control Epidemiology Blockchain consensus algorithms

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TRADEOFFS FOR TASK PARALLELIZATION IN DISTRIBUTED OPTIMIZATION 24

TRADEOFFS FOR TASK PARALLELIZATION IN DISTRIBUTED OPTIMIZATI...

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IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Tsianos, Konstantinos I. Sarwate, Anand D. Rabbat, Michael G. McGill Univ Dept ECE Montreal PQ Canada Rutgers State Univ Dept ECE Piscataway NJ USA

ISBN: (纸本)9781479936946

We consider the problem of solving multiple optimization tasks on the same data in a distributed setting. We focus on consensus-based and incremental optimization strategies. consensus-based distributed optimizers converge in fewer iterations, but the multiple tasks must be run serially. Incremental optimization algorithms, where the iterate is passed from node to node, have slower convergence guarantees but they can be parallelized to work on multiple tasks concurrently. When there are many tasks to solve, this approach can suffer from queuing delay. We provide an analysis of this delay which suggests that incremental algorithms may have superior performance for a moderate number of tasks. The main factor that controls this effect is the communication time. We show experimentally that there is a regime in which parallel instances of incremental algorithms can outperform serial instances of consensus-based algorithms.

关键词： distributed optimization machine learning consensus algorithms networks

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Distributed soft thresholding for sparse signal recovery

Distributed soft thresholding for sparse signal recovery

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IEEE Global Communications Conference (GLOBECOM)

作者： Ravazzi, C. Fosson, S. M. Magli, E. Politecn Torino Dept Elect & Telecommun Turin Italy

ISBN: (纸本)9781479913534

In this paper, we address the problem of distributed sparse recovery of signals acquired via compressed measurements in a sensor network. We propose a new class of distributed algorithms to solve Lasso regression problems, when the communication to a fusion center is not possible, e.g., due to communication cost or privacy reasons. More precisely, we introduce a distributed iterative soft thresholding algorithm (DISTA) that consists of three steps: an averaging step, a gradient step, and a soft thresholding operation. We prove the convergence of DISTA in networks represented by regular graphs, and we compare it with existing methods in terms of performance, memory, and complexity.

关键词： Distributed compressed sensing distributed optimization consensus algorithms gradient-thresholding algorithms

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From Data Integrity to Global ModeI Integrity for Decentralized Federated Learning: A Blockchain-based Approach

From Data Integrity to Global ModeI Integrity for Decentrali...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Na Zhao, Yao Qu, Youyang Cui, Lei Li, Bai Gao, Longxiang Qilu Univ Technol Shandong Comp Sci Ctr Key Lab Comp Power Network & Informat Secur Minist EducShandong Acad Sci Jinan Jinan Peoples R China Deakin Univ Sch IT Geelong Vic Australia Zhejiang Tianheng Informat Technol Co Wenzhou Peoples R China Shandong Fundamental Res Ctr Comp Sci Jinan Shandong Prov Key Lab Comp Networks Shandong Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Decentralized Federated Learning (DFL) is extensively applied in various areas, e.g., healthcare, finance, and Internet of Things (loT), offering practical solutions for distributed intelligent applications and data collaboration. In DFL systems, participants, e.g., edge devices, organizations, or nodes, collaborate in the training of a shared global model by aggregating local models from various participants. During this process, participants need to communicate frequently with a central authority/node/server to share model parameters. Such communication is vulnerable to malicious attacks or tampering, posing a significant threat to the integrity of model training. The integrity verification method can provide an integrity guarantee for the global model of DFL. However, most of the existing integrity verification schemes are centralized and not suitable for resource-constrained DFL scenarios. Therefore, how to verify the integrity of the global model becomes an important issue in DFL. To address it, we devise a global model integrity verification method for DFL. Specifically, we generate a digital signature for each global model parameter as proof of integrity, while improving the efficiency of integrity verification by electing delegates to conduct the verification process. A series of experiments is conducted to validate the performance of the proposed method. The experimental results demonstrate that our approach not only effectively ensures the integrity of the global model but also functions well under limited resources.

关键词： Decentralized federated learning Integrity verification Blockchain consensus algorithms Digital signatures

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