• Submit to JSAN Login Register
• MDPI
• Journals A-Z
• For Authors
• For Editors
• For Librarians
• About
• Open Access Policy

• Abstract
• Full-Text PDF [1177 KB]
• Full-Text XML
• Article Versions Notes

• Article Statistics
• Google Scholar
• Order Reprints

• Cite this article
• Export to BibTeX
• Export to EndNote

• [+] on DOAJ
• Zhu, N
• O'Connor, I
• [+] on Google Scholar
• Zhu, N
• O'Connor, I
• [+] on PubMed
• Zhu, N
• O'Connor, I

•  CiteULike
•  Facebook
•  Mendeley
•  Twitter
• More services

Open AccessThis article is

• freely available
• re-usable

J. Sens. Actuator Netw. 2013, 2(4), 675-699; doi:10.3390/jsan2040675

Article

iMASKO: A Genetic Algorithm Based Optimization Framework for Wireless Sensor Networks

Nanhao Zhu *  and Ian O'Connor

Heterogeneous Systems Design Group, Lyon Institute of Nanotechnology UMR CNRS 5270, University of Lyon, Ecole Centrale de Lyon, Ecully 69134, France

* Author to whom correspondence should be addressed.

Received: 29 August 2013; in revised form: 15 September 2013 / Accepted: 17 September 2013 / Published: 9 October 2013

Download PDF Full-Text [1177 KB, uploaded 9 October 2013 10:04 CEST]

Abstract: In this paper we present the design and implementation of a generic GA-based optimization framework iMASKO (iNL@MATLAB Genetic Algorithm-based Sensor NetworK Optimizer) to optimize the performance metrics of wireless sensor networks. Due to the global search property of genetic algorithms, the framework is able to automatically and quickly fine tune hundreds of possible solutions for the given task to find the best suitable tradeoff. We test and evaluate the framework by using it to explore a SystemC-based simulation process to tune the configuration of the unslotted CSMA/CA algorithm of IEEE 802.15.4, aiming to discover the most available tradeoff solutions for the required performance metrics. In particular, in the test cases different sensor node platforms are under investigation. A weighted sum based cost function is used to measure the optimization effectiveness and capability of the framework. In the meantime, another experiment is performed to test the framework’s optimization characteristic in multi-scenario and multi-objectives conditions.

Keywords: WSNs; optimization; MATLAB; genetic algorithm; performance metrics; simulation; evaluation; weighted sum; multi-objective; multi-scenario

Article Statistics

Cite This Article