On directional bias for network coverage

Smith, Graeme, Sanders, J. W. and Li, Qin (2014). On directional bias for network coverage. In: Linqiang Pan, Gheorghe Păun, Mario J. Pérez-Jiménez and Tao Song, Bio-Inspired Computing - Theories and Applications, Bic-Ta 2014. Bio-Inspired Computing - Theories and Applications, Wuhan, China, (384-388). 16-19 October 2014. doi:10.1007/978-3-662-45049-9_62

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Smith, Graeme
Sanders, J. W.
Li, Qin
Title of paper On directional bias for network coverage
Conference name Bio-Inspired Computing - Theories and Applications
Conference location Wuhan, China
Conference dates 16-19 October 2014
Proceedings title Bio-Inspired Computing - Theories and Applications, Bic-Ta 2014   Check publisher's open access policy
Series Communications in Computer and Information Science
Place of Publication Berlin & Heidelberg, Germany
Publisher Springer Berlin Heidelberg
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1007/978-3-662-45049-9_62
ISBN 9783662450482
ISSN 1865-0929
Editor Linqiang Pan
Gheorghe Păun
Mario J. Pérez-Jiménez
Tao Song
Volume 472
Start page 384
End page 388
Total pages 5
Collection year 2015
Language eng
Formatted Abstract/Summary
Random walks have been proposed as a simple method of efficiently searching, or disseminating information throughout, communication and sensor networks. In nature, animals (such as ants) tend to follow correlated random walks, i.e., random walks that are biased towards their current heading. In this paper, we investigate whether or not complementing random walks with directional bias can decrease the expected discovery and coverage times in networks. To do so, we use a macro-level model of a directionally biased random walk based on Markov chains. By focussing on regular, connected networks, the model allows us to efficiently calculate expected coverage times for different network sizes and biases. Our analysis shows that directional bias can significantly reduce the coverage time, but only when the bias is below a certain value which is dependent on the network size.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Google Scholar Search Google Scholar
Created: Mon, 02 Mar 2015, 09:58:03 EST by Graeme Smith on behalf of School of Information Technol and Elec Engineering