J. Sens. Actuator Netw. 2013, 2(4), 675-699; doi:10.3390/jsan2040675
iMASKO: A Genetic Algorithm Based Optimization Framework for Wireless Sensor Networks
Heterogeneous Systems Design Group, Lyon Institute of Nanotechnology UMR CNRS 5270, University of Lyon, Ecole Centrale de Lyon, Ecully 69134, France
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Received: 29 August 2013 / Revised: 15 September 2013 / Accepted: 17 September 2013 / Published: 9 October 2013
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. View Full-TextKeywords:
WSNs; optimization; MATLAB; genetic algorithm; performance metrics; simulation; evaluation; weighted sum; multi-objective; multi-scenario
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Zhu, N.; O'Connor, I. iMASKO: A Genetic Algorithm Based Optimization Framework for Wireless Sensor Networks. J. Sens. Actuator Netw. 2013, 2, 675-699.
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