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Dispersion of mobile robots using global communication

JOURNAL OF PARALLEL AND distributed COMPUTING 2022年 161卷 100-117页

作者： Kshemkalyani, Ajay D. Molla, Anisur Rahaman Sharma, Gokarna Univ Illinois Chicago IL USA Indian Stat Inst Kolkata India Kent State Univ Kent OH 44242 USA

The dispersion problem on graphs asks k <= n robots placed initially arbitrarily on the nodes of an n-node anonymous graph to reposition autonomously to reach a configuration in which each robot is on a distinct node of the graph. This problem is of significant interest due to its relationship to other fundamental robot coordination problems, such as exploration, scattering, load balancing, and relocation of self-driven electric cars (robots) to recharge stations (nodes). In this paper, we consider dispersion using the global communication model where a robot can communicate with any other robot in the graph (but the graph is unknown to robots). We provide two novel deterministic algorithms for arbitrary graphs in a synchronous setting where all robots perform their actions in every time step. Our first algorithm is based on a DFS traversal and guarantees (i) O(k Delta) steps runtime using O (log(k + Delta))) bits at each robot and (ii) O(min(m, k Delta)) steps runtime using O(Delta + log k) bits at each robot, where m is the number of edges and Delta is the maximum degree of the graph. The second algorithm is based on a BFS traversal and guarantees O ((D + k)Delta(D + Delta)) steps runtime using O (log D + Delta log k)) bits at each robot, where D is the diameter of the graph. Our results complement the existing results established using the local communication model where a robot can communication only with other robots present at the same node. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Multi-agent systems Autonomous mobile robots Dispersion distributed algorithms Time and memory complexity

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Non-Bayesian Social Learning on Random Digraphs With Aperiodically Varying Network Connectivy

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2022年第3期9卷 1202-1214页

作者： Parasnis, Rohit Franceschetti, Massimo Touri, Behrouz Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

In this article, we study non-Bayesian social learning on random directed graphs and show that under mild connectivity assumptions, all the agents almost surely learn the true state of the world asymptotically in time if the sequence of the associated weighted adjacency matrices belongs to Class P* (a broad class of stochastic chains that subsumes uniformly strongly connected chains). We show that uniform strong connectivity, while being unnecessary for asymptotic learning, ensures that all the agents' beliefs converge to a consensus almost surely, even when the true state is not identifiable. We then provide a few corollaries of our main results, some of which apply to the variants of the original update rule, such as inertial non-Bayesian learning and learning via diffusion and adaptation. Others include the extensions of known results on social learning. We also show that if the network of influences is balanced in a certain sense, then asymptotic learning occurs almost surely even in the absence of uniform strong connectivity.

关键词： distributed algorithms multiagent systems network theory (graphs) networked control systems stochastic systems

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distributed Resource Allocation Over Dynamic Networks With Uncertainty

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第9期66卷 4378-4384页

作者： Doan, Thinh T. Beck, Carolyn L. Virginia Tech Dept Elect & Comp Engn Arlington VA 22203 USA Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA

Motivated by broad applications in various fields of engineering, we study a network resource allocation problem where the goal is to optimally allocate a fixed quantity of the resources over a network of nodes. We consider large scale networks with complex interconnection structures, thus any solution must be implemented in parallel and based only on local data resulting in a need for distributed algorithms. In this article, we study a distributed Lagrangian method for such problems. By utilizing the so-called distributed subgradient methods to solve the dual problem, our approach eliminates the need for central coordination in updating the dual variables, which is often required in classic Lagrangian methods. Our focus is to understand the performance of this distributed algorithm when the number of resources is unknown and may be time-varying. In particular, we obtain an upper bound on the convergence rate of the algorithm to the optimal value, in expectation, as a function of network topology. The effectiveness of the proposed method is demonstrated by its application to the economic dispatch problem in power systems, with simulations completed on the benchmark IEEE-14 and IEEE-118 bus test systems.

关键词： Resource management Economics distributed algorithms Convergence Uncertainty Standards Benchmark testing distributed algorithms distributed optimization multi-agent systems

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A General Framework for distributed Inference With Uncertain Models

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2021年 7卷 392-405页

作者： Hare, James Z. Uribe, Cesar A. Kaplan, Lance Jadbabaie, Ali US Army Res Lab Adelphi MD 20783 USA Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA MIT Lab Informat & Decis Syst LIDS Inst Data Syst & Soc IDSS 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Civil & Environm Engn 77 Massachusetts Ave Cambridge MA 02139 USA

This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesis-testing framework, where we assume that the agents seek to agree on the hypothesis (target class) that best matches the distribution of observations. Non-Bayesian social learning theory provides a framework that solves this problem in an efficient manner by allowing the agents to sequentially communicate and update their beliefs for each hypothesis over the network. Most existing approaches assume that agents have access to exact statistical models for each hypothesis. However, in many practical applications, agents learn the likelihood models based on limited data, which induces uncertainty in the likelihood function parameters. In this work, we build upon the concept of uncertain models to incorporate the agents' uncertainty in the likelihoods by identifying a broad set of parametric distribution that allows the agents' beliefs to converge to the same result as a centralized approach. Furthermore, we empirically explore extensions to non-parametric models to provide a generalized framework of uncertain models in non-Bayesian social learning.

关键词： distributed algorithms inference algorithms Non-Bayesian social learning uncertainty

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A conditional gradient algorithm for distributed online optimization in networks

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IET CONTROL THEORY AND APPLICATIONS 2021年第4期15卷 570-579页

作者： Shen, Xiuyu Li, Dequan Fang, Runyue Dong, Qiao Anhui Univ Sci & Technol Sch Math & Big Data Huainan 232001 Peoples R China

This paper addresses a network of computing nodes aiming to solve an online convex optimisation problem in a distributed manner, that is, by means of the local estimation and communication, without any central coordinator. An online distributed conditional gradient algorithm based on the conditional gradient is developed, which can effectively tackle the problem of high time complexity of the distributed online optimisation. The proposed algorithm allows the global objective function to be decomposed into the sum of the local objective functions, and nodes collectively minimise the sum of local time-varying objective functions while the communication pattern among nodes is captured as a connected undirected graph. By adding a regularisation term to the local objective function of each node, the proposed algorithm constructs a new time-varying objective function. The proposed algorithm also utilises the local linear optimisation oracle to replace the projection operation such that the regret bound of the algorithm can be effectively improved. By introducing the nominal regret and the global regret, the convergence properties of the proposed algorithm are also theoretically analysed. It is shown that, if the objective function of each agent is strongly convex and smooth, these two types of regrets grow sublinearly with the order of O(logT), where T is the time horizon. Numerical experiments also demonstrate the advantages of the proposed algorithm over existing distributed optimisation algorithms.

关键词： convergence global objective function communication pattern distributed online optimization connected undirected graph convergence properties convex programming gradient methods online convex optimisation problem conditional gradient algorithm local objective function local time-varying objective functions local linear optimisation oracle high time complexity minimisation distributed algorithms graph theory computational complexity

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distributed Clustering Algorithm for Adaptive Pandemic Control

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IEEE ACCESS 2021年 9卷 160688-160696页

作者： Insausti, Xabier Zarraga-Rodriguez, Marta Nolasco-Ferencikova, Carolina Gutierrez-Gutierrez, Jesus Univ Navarra Tecnun San Sebastian 20018 Spain

The COVID-19 pandemic has had severe consequences on the global economy, mainly due to indiscriminate geographical lockdowns. Moreover, the digital tracking tools developed to survey the spread of the virus have generated serious privacy concerns. In this paper, we present an algorithm that adaptively groups individuals according to their social contacts and their risk level of severe illness from COVID-19, instead of geographical criteria. The algorithm is fully distributed and therefore, individuals do not know any information about the group they belong to. Thus, we present a distributed clustering algorithm for adaptive pandemic control.

关键词： Clustering algorithms COVID-19 Privacy Tools Pandemics Laplace equations Coronaviruses Adaptive algorithm clustering algorithms COVID-19 distributed algorithms

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An Accelerated distributed Gradient-Based Algorithm for Constrained Optimization With Application to Economic Dispatch in a Large-Scale Power System

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第4期51卷 2041-2053页

作者： Guo, Fanghong Li, Guoqi Wen, Changyun Wang, Lei Meng, Ziyang Zhejiang Univ Technol Dept Automat Hangzhou 310023 Peoples R China Tsinghua Univ Ctr Brain Inspired Comp Res Dept Precis Instrument Beijing 100084 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia

In this article, we consider a convex optimization problem which minimizes the sum of local agents' cost functions subject to certain local constraints. Besides, both the local cost function and local constraints are only known by the local agent itself. To solve this problem, a new accelerated distributed gradient-based algorithm is proposed, which is inspired by the "momentum" phenomena in nature and aims to accelerate the convergence speed of conventional distributed gradient algorithms. Sufficient conditions for the stepsizes and the acceleration gains are derived to ensure the convergence of the proposed algorithm. Furthermore, based on this proposed fast distributed algorithm, a new decentralized approach is proposed to solve economic dispatch problem, especially for a large-scale power system. Based on the idea of virtual agent, it is proved that this decentralized algorithm is equivalent to the original fast distributed gradient method. Several case studies implemented on IEEE 30-bus, IEEE 118-bus power systems, and a large-scale power system consisting of 1000 generators are conducted to validate the proposed method.

关键词： Acceleration Convergence distributed algorithms Gradient methods Power systems Economics Constrained optimization decentralized gradient economic dispatch (ED) large-scale power system virtual agent

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Impact of Network Topology on the Resilience of Vehicle Platoons

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 15166-15177页

作者： Pirani, Mohammad Baldi, Simone Johansson, Karl Henrik Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada Southeast Univ Sch Math Nanjing 210096 Peoples R China Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands KTH Royal Inst Technol Div Decis & Control Syst S-11428 Stockholm Sweden

This paper presents a comprehensive study on the impact of information flow topologies on the resilience of distributed algorithms that are widely used for estimation and control in vehicle platoons. In the state of the art, the influence of information flow topology on both internal and string stability of vehicle platoons has been well studied. However, understanding the impact of information flow topology on cyber-security tasks, e.g., attack detection, resilient estimation and formation algorithms, is largely open. By means of a general graph theory framework, we study connectivity measures of several platoon topologies and we reveal how these measures affect the ability of distributed algorithms to reject communication disturbances, to detect cyber-attacks, and to be resilient against them. We show that the traditional platoon topologies relying on interaction with the nearest neighbor are very fragile with respect to performance and security criteria. On the other hand, appropriate platoon topologies, namely k-nearest neighbor topologies, are shown to fulfill desired security and performance levels. The framework we study covers undirected and directed topologies, ungrounded and grounded topologies, or topologies on a line and on a ring. We show that there is a trade-off in the network design between the robustness to disturbances and the resilience to adversarial actions. Theoretical results are validated via simulations.

关键词： Topology Resilience Network topology Robustness Estimation Security Connected vehicles Resilience of vehicle platoons graph theory network connectivity distributed algorithms

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SEALEA: Scalable and Energy Aware k-Leaders Election Algorithm in IoT Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2022年第1期125卷 209-229页

作者： Haddar, Mohamed Amine Taif Univ Coll Comp & Informat Technol At Taif Saudi Arabia

Wireless Sensor Network applications profit from or necessitate the use of leaders, elected on the basis of some quantifiable and comparable criteria. Hence, a panoply of leader election algorithms have been proposed in the literature. Even though most of the algorithms focus on lowering the control message (messages needed to elect a leader) count, there has been almost no focus on ensuring high availability of a leader despite various types of failures like battery exhaustion and sensor crash, especially, in the scenarios of rescue and warfare, where the absence of the leader, even for a short duration, may lead to havoc. To overcome the problem of electing a unique leader, in this paper, we propose an efficient protocol for electing k-leaders in a wireless sensor network. The proposed protocol, called SEALEA for A Scalable Leader Election protocol, is distributed and, by means of the exchange of messages among neighbors, terminates after informing the elected nodes. The correctness of the protocol is proven through simulation. SEALEA is implemented on the OMNET++ simulator. Our experimental evaluations demonstrate the effectiveness of SEALEA in determining network leaders swiftly and efficiently. The performance of SEALEA is compared to that of other previously proposed k-leaders election protocols WiLE [1] and K-Top Leader [2]. Results show that SEALEA determines the leader faster and consuming less energy than previous solutions. On average, SEALEA is shown to send 0.845% of the messages sent by WiLE, transmitting 0.87% of the bytes transmitted by that protocol. Against K-TOP, SEALEA sends 19.25% of the messages and transmits only 19.28% of the bytes transmitted by K-TOP.

关键词： Leader election distributed algorithms Smart cities IoT Wireless sensor networks Mesh networks

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distributed Estimation and Control of Node Centrality in Undirected Asymmetric Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第5期66卷 2304-2311页

作者： Montijano, Eduardo Oliva, Gabriele Gasparri, Andrea Univ Zaragoza Inst Invest Ingn Aragon I3A Zaragoza 50018 Spain Univ Campus Biomed Roma Unit Automat Control Dept Engn I-00128 Rome Italy Univ Roma Tre Dept Comp Sci & Automat I-00128 Rome Italy

Measures of node centrality that describe the importance of a node within a network are crucial for understanding the behavior of social networks and graphs. In this article, we address the problems of distributed estimation and control of node centrality in undirected graphs with asymmetric weight values. In particular, we focus our attention on alpha-centrality, which can be seen as a generalization of eigenvector centrality. In this setting, we first consider a distributed protocol where agents compute their alpha-centrality, focusing on the convergence properties of the method;then, we combine the estimation method with a distributed iteration to achieve a consensus value weighted by the influence of each node in the network. Finally, we formulate an alpha-centrality control problem, which is naturally decoupled, and thus, suitable for a distributed setting and we apply this formulation to protect the most valuable nodes in a network against a targeted attack, by making every node in the network equally important in terms of alpha-centrality. Simulations results are provided to corroborate the theoretical findings.

关键词： Estimation Protocols Convergence Measurement distributed algorithms Consensus algorithm Social networking (online) Decentralized control distributed algorithms networked control systems network topology

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