Density Classification in Asynchronous Random Networks with Faulty Nodes

Author

Gogolev, Alexander ; Marcenaro, Lucio

Author_Institution

Inst. of Networked & Embedded Syst., Univ. of Klagenfurt, Klagenfurt, Austria

fYear

2014

fDate

12-14 Feb. 2014

Firstpage

256

Lastpage

261

Abstract

This paper investigates distributed consensus for density classification in asynchronous random networks with faulty nodes. We compare four different models of faulty behavior under randomized topology. Using computer simulations, we show that (a) faulty nodes´ impact depends on their location and (b) faulty nodes with persistent failures inhibit consensus stronger than commonly-used Byzantine faulty nodes with random failures. We also show that (c) randomization by Byzantine faulty nodes can be strongly beneficial for binary consensus and (d) topology randomization can increase robustness towards faulty node behavior.

Keywords

distributed algorithms; fault tolerant computing; graph theory; network theory (graphs); randomised algorithms; Byzantine faulty nodes; Watts-Strogatz graph; asynchronous random networks; binary consensus; centralized decision making; computer simulations; density classification; distributed consensus algorithms; faulty node behavior; persistent failures; random failures; topology randomization; Clustering algorithms; Convergence; Network topology; Nickel; Robustness; Topology; Vectors; Byzantine failure; binary consensus; density classification; distributed consensus; majority sorting; randomized consensus; self-organization;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel, Distributed and Network-Based Processing (PDP), 2014 22nd Euromicro International Conference on

Conference_Location

Torino

ISSN

1066-6192

Type

conf

DOI

10.1109/PDP.2014.62

Filename

6787284