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Integrating centralized and peer-to-peer architectures to support interest management in massively multiplayer on-line games

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2015年第13期27卷 3362-3382页

作者： Carlini, Emanuele Ricci, Laura Coppola, Massimo ISTI CNR Ist A Faedo Pisa Italy Univ Pisa Dept Comp Sci Pisa Italy

A fundamental problem for the development of peer-to-peer (P2P) distributed virtual environments, like massively multiplayer on-line games, is the definition of an overlay supporting interest management, that is, determining all the entities of the virtual world that are relevant for a given player. To this end, this paper proposes a gossip-based approach that considers the coverage of the area of interest of peers as the guiding principle for the definition of the P2P overlay and its maintenance. The resulting overlay provides a support for a best-effort resolution of interest management, mostly supported through communications on the P2P overlay, with minimal intervention of a centralized entity. The paper presents a set of extensive simulations based on realistic mobility traces. The experimental results show the effectiveness of gossiping for the construction and maintenance of a best-effort overlay for interest management. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： peer-to-peer MMOG gossip algorithms interest management

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Compress sensing algorithm for estimation of signals in sensor networks

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WIRELESS NETWORKS 2020年第8期26卷 5681-5688页

作者： Martinez, Juan Mejia, Jose Mederos, Boris Ochoa, Alberto Cruz-Mejia, Oliverio Marmolejo-Saucedo, Jose Antonio Univ Autonoma Ciudad Juarez Ciudad Juarez Chihuahua Mexico Univ Autonoma Estado Mexico Toluca Mexico Univ Panamer Fac Ingn Augusto Rodin 498 Mexico City 03920 DF Mexico

In this research, we present a data recovery scheme for wireless sensor networks. In some sensor networks, each node must be able to recover the complete information of the network, which leads to the problem of the high cost of energy in communication and storage of information. We proposed a modified gossip algorithm for acquire distributed measurements and communicate the information across all nodes of the network using compressive sampling and gossip algorithms to compact the data to be stored and transmitted through a network. The experimental results on synthetic data show that the proposed method reconstruct better the signal and in less iterations than with a similar method using a thresholding algorithm.

关键词： Sensor networks Compressive sampling gossip algorithms

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Distributed Lossy Averaging

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IEEE TRANSACTIONS ON INFORMATION THEORY 2010年第7期56卷 3422-3437页

作者： Su, Han-I El Gamal, Abbas Stanford Univ Dept Elect Engn Stanford CA 94305 USA

In this paper, an information theoretic formulation of the distributed averaging problem previously studied in computer science and control is presented. We assume a network with m nodes each observing a white Gaussian noise (WGN) source. The nodes communicate and perform local processing with the goal of computing the average of the sources to within a prescribed mean squared error distortion. The network rate distortion function R*(D) for a two-node network with correlated Gaussian sources is established. A general cutset lower bound on R*(D) is established and shown to be achievable to within a factor of 2 via a centralized protocol over a star network. A lower bound on the network rate distortion function for distributed weighted-sum protocols, which is larger in order than the cutset bound by a factor of log m, is established. An upper bound on the network rate distortion function for gossip-base weighted-sum protocols, which is only log log m larger in order than the lower bound for a complete graph network, is established. The results suggest that using distributed protocols results in a factor of log m increase in order relative to centralized protocols.

关键词： Consensus distributed averaging gossip algorithms interactive communication lossy source coding

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Toward Resource-Optimal Consensus Over the Wireless Medium

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2013年第2期7卷 284-295页

作者： Nokleby, Matthew Bajwa, Waheed U. Calderbank, Robert Aazhang, Behnaam Duke Univ Dept Elect & Comp Engn Durham NC 27705 USA Duke Univ Dept Comp Sci Durham NC 27705 USA Rutgers State Univ Dept Elect & Comp Engn Piscataway NJ 08854 USA Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

We carry out a comprehensive study of the resource cost of averaging consensus in wireless networks. Most previous approaches suppose a graphical network, which abstracts away crucial features of the wireless medium, and measure resource consumption only in terms of the total number of transmissions required to achieve consensus. Under a path-loss model, we study the resource requirements of consensus with respect to three wireless-appropriate metrics: total transmit energy, elapsed time, and time-bandwidth product. First, we characterize the performance of several popular gossip algorithms, showing that they may be order-optimal with respect to transmit energy but are strictly suboptimal with respect to elapsed time and time-bandwidth product. Further, we propose a new consensus scheme, termed hierarchical averaging, and show that it is nearly order-optimal with respect to all three metrics. Finally, we examine the effects of quantization, showing that hierarchical averaging provides a nearly order-optimal tradeoff between resource consumption and quantization error.

关键词： Average consensus distributed algorithms gossip algorithms wireless sensor networks

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Network-Based Consensus Averaging With General Noisy Channels

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第1期59卷 373-385页

作者： Rajagopal, Ram Wainwright, Martin J. Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA

This paper focuses on the consensus averaging problem on graphs under general imperfect communications. We study a particular class of distributed consensus algorithms based on damped updates, and using the ordinary differential equation method, we prove that the updates converge almost surely to the consensus average for various models of perturbation of data exchanged between nodes. The convergence is not asymptotic in the size of the network. Our analysis applies to various types of stochastic disturbances to the updates, including errors in update calculations, dithered quantization and imperfect data exchange among nodes. Under a suitable stability condition, we prove that the error between estimated and true averages is asymptotically Gaussian, and we show how the asymptotic covariance is specified by the graph Laplacian. For additive perturbations, we show how the scaling of the asymptotic MSE is controlled by the spectral gap of the Laplacian.

关键词： Consensus protocols distributed averaging gossip algorithms graph Laplacian message-passing sensor networks stochastic approximation

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Randomized Matrix Weighted Consensus

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第4期11卷 3536-3549页

作者： Le-Phan, Nhat-Minh Trinh, Minh Hoang Nguyen, Phuoc Doan Hanoi Univ Sci & Technol Sch Elect & Elect Engn Dept Automat Engn Hanoi 11615 Vietnam Viettel Aerosp Inst Nav Guidance & Control Technol Ctr Hanoi 155530 Vietnam FPT Univ AI Dept Quy Nhon Campus Binh Dinh 55117 Vietnam

In this paper, randomized gossip-type matrix weighted consensus algorithms are proposed for both leaderless and leader-follower topologies. First, we introduce the notion of expected matrix weighted network, which captures the multi-dimensional interactions between any two agents in a probabilistic sense. Under some mild assumptions on the distribution of the expected matrix weights and the upper bound of the updating step size, the proposed asynchronous pairwise update algorithms drive the network to achieve a consensus in expectation. An upper bound of the $\epsilon$-convergence time of the algorithm is then derived. Furthermore, the proposed algorithms are applied to the bearing-based network localization and formation control problems. The theoretical results are supported by several numerical examples.

关键词： Consensus algorithm Heuristic algorithms Location awareness Topology Laplace equations Protocols Clocks Matrix weighted consensus multi-agent systems gossip algorithms randomized algorithms network localization formation control

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Convergence of Consensus Models With Stochastic Disturbances

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IEEE TRANSACTIONS ON INFORMATION THEORY 2010年第8期57卷 4101-4113页

作者： Aysal, Tuncer Can Barner, Kenneth E. Cornell Univ Sch Elect & Comp Engn Commun Res Signal Proc Grp Ithaca NY 14853 USA Univ Delaware Dept Elect & Comp Engn Signal Proc & Commun Grp Newark DE 19716 USA

We consider consensus algorithms in their most general setting and provide conditions under which such algorithms are guaranteed to converge, almost surely, to a consensus. Let {A(t), B(t)} is an element of R(NxN) be (possibly) stochastic, nonstationary matrices and {x(t), m(t)} is an element of R(Nx1) be state and perturbation vectors, respectively. For any consensus algorithm of the form x(t + 1) = A(t)x(t) + B(t)m(t), we provide conditions under which consensus is achieved almost surely, i.e., Pr{lim(t ->infinity) x(t) = c1} = 1 for some c is an element of R. Moreover, we show that this general result subsumes recently reported results for specific consensus algorithms classes, including sum-preserving, nonsum-preserving, quantized, and noisy gossip algorithms. Also provided are the epsilon-converging time for any such converging iterative algorithm, i.e., the earliest time at which the vector x(t) is epsilon close to consensus, and sufficient conditions for convergence in expectation to the average of the initial node measurements. Finally, mean square error bounds of any consensus algorithm of the form discussed above are presented.

关键词： Convergence in expectation convergence of random sequences convergence to consensus distributed average consensus gossip algorithms mean square error noisy gossip algorithms nonsum-preserving gossip algorithms quantized gossip algorithms sum-preserving gossip algorithms

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gossiped and Quantized Online Multi-Kernel Learning

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IEEE SIGNAL PROCESSING LETTERS 2023年 30卷 468-472页

作者： Ortega, Tomas Jafarkhani, Hamid Univ Calif Irvine Ctr Pervas Commun & Comp Irvine CA 92697 USA Univ Calif Irvine EECS Dept Irvine CA 92697 USA

In instances of online kernel learning where little prior information is available and centralized learning is unfeasible, past research has shown that distributed and online multi-kernel learning provides sub-linear regret as long as every pair of nodes in the network can communicate (i.e., the communications network is a complete graph). In addition, to manage the communication load, which is often a performance bottleneck, communications between nodes can be quantized. This letter expands on these results to non-fully connected graphs, which is often the case in wireless sensor networks. To address this challenge, we propose a gossip algorithm and provide a proof that it achieves sub-linear regret. Experiments with real datasets confirm our findings.

关键词： Kernel Signal processing algorithms Radio frequency Quantization (signal) Mathematical models Task analysis Hilbert space Federated learning gossip algorithms kernel-based learning quantization sensor networks

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Fast distributed algorithms for computing separable functions

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IEEE TRANSACTIONS ON INFORMATION THEORY 2008年第7期54卷 2997-3007页

作者： Mosk-Aoyama, Damon Shah, Devavrat Stanford Univ Dept Elect Engn & Comp Sci Stanford CA 94305 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA

The problem of computing functions of values at the nodes in a network in a fully distributed manner, where nodes do not have unique identities and make decisions based only on local information, has applications in sensor, peer-to-peer, and ad hoc networks. The task of computing separable functions, which can be written as linear combinations of functions of individual variables, is studied in this context. Known iterative algorithms for averaging can be used to compute the normalized values of such functions, but these algorithms do not extend, in general, to the computation of the actual values of separable functions. The main contribution of this paper is the design of a distributed randomized algorithm for computing separable functions. The running time of the algorithm is shown to depend on the running time of a minimum computation algorithm used as a subroutine. Using a randomized gossip mechanism for minimum computation as the subroutine yields a complete fully distributed algorithm for computing separable functions. For a class of graphs with small spectral gap, such as grid graphs, the time used by the algorithm to compute averages is of a smaller order than the time required by a known iterative averaging scheme.

关键词： data aggregation distributed algorithms gossip algorithms randomized algorithms

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Detection of Insider Attacks in Distributed Projected Subgradient algorithms

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2021年第4期7卷 1099-1111页

作者： Wu, Sissi Xiaoxiao Li, Gangqiang Zhang, Shengli Lin, Xiaohui Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Peng Cheng Lab Dept Math & Theory Shenzhen 518060 Peoples R China Huanghuai Univ Coll Informat Engn Zhumadian 463000 Peoples R China

The gossip-based distributed projected subgradient algorithms (DPS) are widely used to solve decentralized optimization problems in various multi-agent applications, while they are generally vulnerable to data injection attacks by internal malicious agents as each agent locally estimates its descent direction without an authorized supervision. In this work, we explore the application of artificial intelligence (AI) technologies to detect internal attacks. We show that a general neural network is particularly suitable for detecting and localizing the malicious agents, as they can effectively explore nonlinear relationship underlying the collected data. Moreover, we propose to adopt one of the state-of-the-art approaches in decentralized federated learning, i.e., a gossip-based collaborative learning protocol, to facilitate training the neural network models by gossip exchanges. This advanced approach is expected to make our model more robust to challenges with insufficient training data, or mismatched test data. In our simulations, a least-squared problem is considered to verify the feasibility and effectiveness of AI-based methods. Simulation results demonstrate that the proposed AI-based methods are beneficial to improve performance of detecting and localizing malicious agents over score-based methods, and the peer-to-peer neural network model is indeed robust to target issues.

关键词： gossip algorithms distributed projected subgradient (DPS) artificial intelligence (AI) technology internal attacks malicious agents

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