CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
research
Efficient consensus algorithm for the accurate faulty node tracking with faster convergence rate in a distributed sensor network
Authors
Rajkin Hossain
Muhidul Islam Khan
Publication date
Publisher
© 2016 Springer Netherlands
Doi
Cite
Abstract
This article was published in the Eurasip Journal on Wireless Communications and Networking [©2016 Published by Springer International Publishing.] and the definite version is available at: http://dx.doi.org/10.1186/s13638-016-0698-x . The article website is at:http://jwcn.eurasipjournals.springeropen.com/articles/10.1186/s13638-016-0698-xOne of the challenging issues in a distributed computing system is to reach on a decision with the presence of so many faulty nodes. These faulty nodes may update the wrong information, provide misleading results and may be nodes with the depleted battery power. Consensus algorithms help to reach on a decision even with the faulty nodes. Every correct node decides some values by a consensus algorithm. If all correct nodes propose the same value, then all the nodes decide on that. Every correct node must agree on the same value. Faulty nodes do not reach on the decision that correct nodes agreed on. Binary consensus algorithm and average consensus algorithm are the most widely used consensus algorithm in a distributed system. We apply binary consensus and average consensus algorithm in a distributed sensor network with the presence of some faulty nodes. We evaluate these algorithms for better convergence rate and error rate. © 2016, The Author(s).Publishe
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
BRAC University Institutional Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:localhost:10361/6645
Last time updated on 08/02/2017