.png){kind=icon}
返回
A local average broadcast gossip algorithm for fast global consensus over graphs
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jpdc.2017.05.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Motivated by applications to wireless sensor, peer-to-peer, and social networks, the canonical average consensus problem is considered in random and regular graphs in this paper. A local average information exchange (LAIE) algorithm is developed to compute the global consensus of the initial measurements of the nodes at every node in the network. In the proposed algorithm, each node interacts with all of its neighboring nodes in each round of the diffusion process to compute and exchange the local average value, such that all nodes can asymptotically reach a global consensus in a distributed manner very quickly. This is in contrast to the conventional random gossip scheme, where each node only interacts with one of its neighboring nodes, leading to very long convergence time. Results show that in a random graph with n nodes, the convergence time of the LAIE algorithm is bounded below by Omega ((n-1)log n/Delta),(1) where the parameter Delta denotes the largest degree of the graphs. When a network has n nodes represented by d-regular topology graphs (d > 2), where each node has the same number of neighbors d, the convergence time of the LAIE algorithm is bounded below by Theta (n(d+1)log n(2+d+2 root d-1)(d-2 root d-1)). This shows that the proposed algorithms can achieve quicker convergence to the global consensus than other schemes based on the classic random gossip algorithm. Finally, we assess and compare the communication cost of the local average algorithm to achieve consensus through numerical results. (C) 2017 Elsevier Inc. All rights reserved.
Keyword:
Broadcasting
Consensus
Local average
d-regular graph
Random graph
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
3.8K
被引数:
4.8K
机构
引用论文
Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms.png)
NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏Consensus and cooperation in networked multi-agent systems网络化多智能体系统的共识与合作PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏Coordinated collective motion of groups of autonomous mobile robots: Analysis of Vicsek's model
PREAI
分享收藏分享收藏
查看更多内容