Distributed genetic algorithm for energy-efficient resource management in sensor networks

Qinru Qiu, Qing Wu, Daniel Burns, Douglas Holzhauer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work We consider energy-efficient resource management in an environment monitoring and hazard detection sensor network. Our goal is to allocate different detection methods to different sensor nodes in the way such that the required detection probability can be achieved while the network lifetime is maximized. The optimization algorithm is designed based on the Island multi-deme genetic algorithm (GA). The experimental results show that our algorithm increases the network lifetime by approximately 14.4% in average compared with the heuristic approaches. We also investigate the effect of the configuration parameters on the searching quality of the proposed distributed G A. A regression model is derived empirically that estimates the runtime of the distributed G A given the configuration parameters such as the subpopulation size, parallelism, and migration rate. Once the model has been fit to a group of data, it can be utilized to find the efficient configurations of the proposed algorithm.

Original languageEnglish (US)
Title of host publicationGECCO 2006 - Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Pages1425-1426
Number of pages2
ISBN (Print)1595931864, 9781595931863
DOIs
StatePublished - 2006
Externally publishedYes
Event8th Annual Genetic and Evolutionary Computation Conference 2006 - Seattle, WA, United States
Duration: Jul 8 2006Jul 12 2006

Publication series

NameGECCO 2006 - Genetic and Evolutionary Computation Conference
Volume2

Other

Other8th Annual Genetic and Evolutionary Computation Conference 2006
CountryUnited States
CitySeattle, WA
Period7/8/067/12/06

Keywords

  • Distributed Genetic Algorithm
  • Energy Aware Design
  • Resource Management
  • Sensor Network

ASJC Scopus subject areas

  • Engineering(all)

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