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
Evolutionary approach on connectivity-based sensor network localization
Authors
GKH Pang
D Qiao
Publication date
1 January 2014
Publisher
'Elsevier BV'
Doi
Cite
Abstract
The sensor network localization based on connectivity can be modeled as a non-convex optimization problem. It can be argued that the actual problem should be represented as an optimization problem with both convex and non-convex constraints. A two-objective evolutionary algorithm is proposed which utilizes the result of all convex constraints to provide a starting point on the location of the unknown nodes and then searches for a solution to satisfy all the convex and non-convex constraints of the problem. The final solution can reach the most suitable configuration of the unknown nodes because all the information on the constraints (convex and non-convex) related to connectivity have been used. Compared with current models that only consider the nodes that have connections, this method considers not only the connection constraints, but also the disconnection constraints. As a MOEA (Multi-Objective Evolution Algorithm), PAES (Pareto Archived Evolution Strategy) is used to solve the problem. Simulation results have shown that better solution can be obtained through the use of this method when compared with those produced by other methods. © 2014 Elsevier B.V.postprin
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
HKU Scholars Hub
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:hub.hku.hk:10722/202819
Last time updated on 01/06/2016