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检索条件"主题词=distributed algorithms"

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Localized Identification of Malicious Nodes in Wireless Sensor Networks

Localized Identification of Malicious Nodes in Wireless Sens...

引用

IEEE Signal Processing and Communications Applications (SIU)

作者： Vahid Khalilpour Akram Yusuf Murat Erten Izmir Bakircay University Izmir Turkey

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

Securing a wireless sensor network against the various attacks with minimal energy consumption is a challenging task because the nodes usually have limited energy source, are distributed in harsh environments and communicate over standard radio channels. Adding malicious nodes to an existing wireless sensor network is one of the common types of attacks. The malicious nodes can reduce the reliability of a network by flooding heavy traffic, sending fake data or counterfeiting the paths. This study proposes a new localized algorithm for identifying the malicious nodes in WSNs where the existing verified nodes detect the malicious nodes using neighborhood information and signature of messages. The testbed experiments and simulation results show that the proposed algorithm is a feasible and efficient approach for detecting the malicious nodes.

关键词： Wireless sensor networks Ciphers Base stations Receivers distributed algorithms History Floods

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A Linearly Convergent Algorithm for Multi-Agent Quasi-Nonexpansive Operators in Real Hilbert Spaces

A Linearly Convergent Algorithm for Multi-Agent Quasi-Nonexp...

引用

IEEE Conference on Decision and Control

作者： Xiuxian Li Min Meng Lihua Xie School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

This paper studies the fixed point finding problem for a global operator over a directed and unbalanced multi-agent network, where the global quasi-nonexpansive operator is sum separable and composed of a family of local operators. In this problem, each local operator is privately known to only each individual agent, and all local operators are assumed to be Lipschitz continuous. To deal with this problem, a distributed (or decentralized) algorithm, called distributed quasi-averaged Operator Tracking algorithm (DOT), is proposed and rigorously analyzed, and it is shown that the algorithm can converge to a fixed point of the global operator at a linear (or exponential) rate under a bounded linear regularity condition, which is strictly weaker than the function's strong convexity in convex optimization. To validate the proposed algorithm, a numerical example is provided finally.

关键词： distributed algorithms Optimization Hilbert space Convergence US Department of Transportation Stochastic processes Game theory

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Privacy-Preserving Event-Triggered Quantized Average Consensus

Privacy-Preserving Event-Triggered Quantized Average Consens...

引用

IEEE Conference on Decision and Control

作者： Apostolos I. Rikos Themistoklis Charalambous Karl H. Johansson Christoforos N. Hadjicostis Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Aalto University Espoo Finland University of Cyprus Nicosia Cyprus

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

In this paper, we propose a privacy-preserving event-triggered quantized average consensus algorithm that allows agents to calculate the average of their initial values without revealing to other agents their specific value. We assume that agents (nodes) interact with other agents via directed communication links (edges), forming a directed communication topology (digraph). The proposed distributed algorithm can be followed by any agent wishing to maintain its privacy (i.e., not reveal the initial value it contributes to the average) to other, possibly multiple, curious but not malicious agents. Curious agents try to identify the initial values of other agents, but do not interfere in the computation in any other way. We develop a distributed strategy that allows agents while processing and transmitting quantized information, to preserve the privacy of their initial quantized values and at the same time to obtain, after a finite number of steps, the exact average of the initial values of the nodes. Illustrative examples demonstrate the validity and performance of our proposed algorithm.

关键词： Privacy Protocols Topology Quantization (signal) distributed algorithms Convergence Network topology

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Scaling Betweenness Approximation to Billions of Edges by MPI-based Adaptive Sampling

Scaling Betweenness Approximation to Billions of Edges by MP...

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International Symposium on Parallel and distributed Processing (IPDPS)

作者： Alexander van der Grinten Henning Meyerhenke Department of Computer Science Humboldt-Universität zu Berlin Berlin Germany

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

Betweenness centrality is one of the most popular vertex centrality measures in network analysis. Hence, many (sequential and parallel) algorithms to compute or approximate betweenness have been devised. Recent algorithmic advances have made it possible to approximate betweenness very efficiently on shared-memory architectures. Yet, the best shared-memory algorithms can still take hours of running time for large graphs, especially for graphs with a high diameter or when a small relative error is *** this work, we present an MPI-based generalization of the state-of-the-art shared-memory algorithm for betweenness approximation. This algorithm is based on adaptive sampling; our parallelization strategy can be applied in the same manner to adaptive sampling algorithms for other problems. In experiments on a 16-node cluster, our MPI-based implementation is by a factor of 16.1x faster than the state-of-the-art shared-memory implementation when considering our parallelization focus - the adaptive sampling phase - only. For the complete algorithm, we obtain an average (geom. mean) speedup factor of 7.4x over the state of the art. For some previously very challenging inputs, this speedup is much higher. As a result, our algorithm is the first to approximate betweenness centrality on graphs with several billion edges in less than ten minutes with high accuracy.

关键词： Approximation algorithms Clustering algorithms Data structures Instruction sets Computer science distributed algorithms Probabilistic logic

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More Consensus is not always Beneficial

More Consensus is not always Beneficial

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American Control Conference

作者： Xuan Wang Shaoshuai Mou School of Aeronautics and Astronautics Purdue University West Lafayette IN USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

This paper shows that more consensus is not always beneficial to the convergence rate of consensus-based distributed computational algorithms. Specifically, we focus on the consensus-based distributed algorithm for solving linear equations, which aims to enable multiple agents in a network to cooperatively find a solution to large-scale linear equations. Such algorithms have two key components: local computation that happens within each agent;and global consensus that happens among connected agents through the networks. Intuitively, one expects more consensus in each iteration should speed up the convergence of such algorithms. However, according to the theoretical analysis and numerical simulations provided in this paper, such assumption does not hold. Counter-intuitively, we show that more consensus is not always beneficial to consensus-based distributed algorithms, and sometimes slow down the convergence.

关键词： distributed algorithms Linear equation Converge Agents algorithms iterative methods Theoretical analysis NETWORKING AGENT

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A Game Theoretical Pricing Scheme for Vehicles in Vehicular Edge Computing

A Game Theoretical Pricing Scheme for Vehicles in Vehicular ...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Chaogang Tang Chunsheng Zhu Huaming Wu Xianglin Wei Qing Li Joel J. P. C. Rodrigues School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of the Ministry of Education China University of Mining and Technology Xuzhou China SUSTech Institute of Future Networks Southern University of Science and Technology Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Center for Applied Mathematics Tianjin University Tianjin China The 63rd Research Institute National University of Defense Technology Changsha China The Hong Kong Polytechnic University Hong Kong Federal University of Piauí Teresina - Pi Brazil Instituto de Telecomunicações Portugal

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Vehicular edge computing (VEC) brings the computing resources to the edge of the networks and thus provisions better computing services to the vehicles in terms of response latency. Meanwhile, the edge server can earn their revenues by leasing the computing resources. However, a higher price does not always bring forth more benefits for the edge server in VEC, since it may discourage vehicles from renting more computing resources from VEC. To the best of our knowledge, few of previous works have focused on the real-time pricing problem for VEC. We investigate in this paper the pricing problem from the viewpoints of both vehicles and the edge server, so as to optimize the utility values and revenues of vehicles and the edge server, respectively. We resort to the Stackelberg game for modeling the interactions between vehicles and edge server, and a distributed algorithm for this pricing problem is proposed in the paper. Experimental results have displayed the efficiency and effectiveness of the proposed algorithm.

关键词： Computational modeling Pricing Games Sensors Servers distributed algorithms Edge computing

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Multi-Agent Formation with Size Scaling via Self-Position Feedback

Multi-Agent Formation with Size Scaling via Self-Position Fe...

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International Conference on Science and Technology-Computer (ICST)

作者： Djati Wibowo Djamari Muhamad Rausyan Fikri Malik Athafarras Nadhifatul Maulidati Steven Mark Levy Abdul Dwi Nur Cahyo Maria Anggi Octaviani Mohammad Farhan Ramdhany Farid Triawan Sampoerna University Jakarta Indonesia Mechanical Engineering Sampoerna University Jakarta Indonesia

ISBN: (纸本)9781728194738

This work studies formation control of Multi-Agent Systems (MASs) where its formation size is scalable via a scaling factor (scalable formation). Past works on scalable formation are limited to either fixed undirected graph or fixed directed graph with k-rooted graph condition. This work proposes a new distributed algorithm for scalable formation based on self-position feedback. When agent is assumed to be able to measure its own position, the proposed algorithm is shown to work under directed switching graph. Under directed fixed graph setting, a distributed observer is designed such that only relative position is needed in the algorithm. Spanning tree condition on directed fixed graph setting is shown to be the necessary and sufficient condition to achieve the scalable formation.

关键词： Sufficient conditions Tree graphs Directed graphs Switches Position measurement Observers distributed algorithms

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An Algorithm for Multi-Objective Multi-Agent Optimization

An Algorithm for Multi-Objective Multi-Agent Optimization

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American Control Conference

作者： Maude J. Blondin Matthew Hale University of Florida Gainesville FL

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

Multi-agent optimization problems with many objective functions have drawn much interest over the past two decades. Many works on the subject minimize the sum of objective functions, which implicitly carries a decision about the problem formulation. Indeed, it represents a special case of a multi-objective problem, in which all objectives are prioritized equally. To the best of our knowledge, multi-objective optimization applied to multi-agent systems remains largely unexplored. Therefore, we propose a distributed algorithm that allows the exploration of Pareto optimal solutions for the non-homogeneously weighted sum of objective functions. In the problems we consider, each agent has one objective function to minimize based on a gradient method. Agents update their decision variables by exchanging information with other agents in the network. Information exchanges are weighted by each agent's individual weights that encode the extent to which they prioritize other agents' objectives. This paper provides a proof of convergence, performance bounds, and explicit limits for the results of their computations. Simulation results with different sizes of networks demonstrate the efficiency of the proposed approach and how the choice of weights impacts the agents' final result.

关键词： Objective function Agents Problem Formulation distributed algorithms optimal solution

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Effect of nearest neighbors on convergence rate of periodic gossip algorithms in WSNs

Effect of nearest neighbors on convergence rate of periodic ...

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： Sateeshkrishna Dhuli Y.N. Singh Priya Ranjan Department of ECE SRM University Amaravati A.P India Department of EE IIT Kanpur U.P India

ISBN: (数字)9781728149769

ISBN: (纸本)9781728149776

distributed algorithms are extremely useful in wireless sensor network to compute the global statistics using local computations. Periodic gossip algorithm is a distributed consensus algorithm, where neighbouring nodes gossip at every time instant. Convergence rate of periodic gossip algorithms determines the time sensor nodes will take to reach consensus. In this paper, we model the WSN as a r-nearest neighbour network and study the effect of nearest neighbours on convergence rate of the gossip algorithms. The ` r' in nearest neighbour network models the node transmission radius and overhead in wireless sensor networks (WSN). We consider the both even and odd number of nodes and observe the convergence rate drastically increasing with the increase in the number of nearest neighbours until the network is fully connected.

关键词： Wireless Sensor Networks Periodic Gossip algorithms distributed algorithms Wireless sensor networks Nearest neighbor distributed algorithms Gossip Odd number algorithms Converge Ganglia Periodic

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Improved algorithms for distributed Boosting 55

Improved Algorithms for Distributed Boosting

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55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

作者： Cooper, Jeff Reyzin, Lev Google Inc Mountain View CA 94043 USA Univ Illinois Dept Math Stat & Comp Sci Chicago IL 60607 USA

ISBN: (纸本)9781538632666

We introduce two distributed boosting algorithms. Our first algorithm uses the entire dataset to train a classifier and requires a significant amount of communication among the distributed sites. Our second algorithm requires very little communication but uses a subsample of the dataset to train the final classifier. Both of our algorithms improve upon existing practical distributed boosting algorithms. Further, both are competitive with AdaBoost when it is run with the entire dataset.

关键词： Boosting Training data Machine learning algorithms Training Partitioning algorithms distributed algorithms Standards

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