Parallel computing approaches to sensor network design using the value paradigm

One previous paradigm was based on sensor positioning selection to allow key variable observability. Later, the problem was posed as a minimum cost problem subject to sensor network performance (precision and gross error robustness first and accuracy as a replacement later). In a previous paper ( Ng...

Full description

Saved in:
Bibliographic Details
Published in:Computers & chemical engineering Vol. 35; no. 6; pp. 1119 - 1134
Main Authors: Nguyen, DuyQuang, Bagajewicz, Miguel J.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 09.06.2011
Elsevier
Subjects:
Acceptability
Applied sciences
Chemical engineering
Computation
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Exact sciences and technology
Instrumentation design
Minimum cost
Networks
Parallel computing
Parallel processing
Robustness
Sensor network design
Sensors
Software
Parallel computing
Instrumentation design
Sensor network design
Positioning
Parallel algorithm
Economic value
Measurement sensor
Distributed computing
Information value
Robustness
Economic information
Sensor array
Observability
ISSN:0098-1354, 1873-4375
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:One previous paradigm was based on sensor positioning selection to allow key variable observability. Later, the problem was posed as a minimum cost problem subject to sensor network performance (precision and gross error robustness first and accuracy as a replacement later). In a previous paper ( Nguyen and Bagajewicz, submitted for publication), we presented a new paradigm in sensor network design, one that maximizes value, defined as economic value of information minus its cost. We presented two methods to solve the problem and we concluded that their performance was acceptable for small and medium size problems, but not for large problems. In this paper we use parallel computing approaches for large problems.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2010.07.014