Use of self-adaptive methodology in wireless sensor networks for reducing energy consumption

Syed S. Rizvi, Aasia Riasat

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

2 Scopus citations

Abstract

A sensor network is made up of numerous small independent sensor nodes with sensing, processing and communicating capabilities. The sensor nodes have limited battery and a minimal amount of on-board computing power. A self-adaptive methodology that utilizes the source and path redundancy techniques to efficiently reduce the required energy consumption is proposed. The proposed methodology presents a self-adaptive strategy to optimize the number of active sensor nodes and assign equal time slots to each sensor nodes for sensing and communication with the BS. The self-adaptive property enables the proposed methodology to perform a global search for optimizing the position of active sensor nodes with respect to the location of the base station (BS). The global search performed by the proposed methodology is carried out in a complete top down manner. The proposed self-adaptive methodology, therefore, not only reduces the energy consumption of wireless sensor nodes but also effectively maximizes the lifetime of active sensor nodes. Simulation results demonstrate that the proposed methodology significantly minimizes the energy consumption and consequently increases the life time of active sensor nodes.

Original languageEnglish (US)
Title of host publication2007 International Conference on Information and Emerging Technologies, ICIET
Pages71-77
Number of pages7
DOIs
StatePublished - Dec 1 2007
Event2007 International Conference on Information and Emerging Technologies, ICIET - Karachi, Pakistan
Duration: Jul 6 2007Jul 7 2007

Publication series

Name2007 International Conference on Information and Emerging Technologies, ICIET

Other

Other2007 International Conference on Information and Emerging Technologies, ICIET
Country/TerritoryPakistan
CityKarachi
Period7/6/077/7/07

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

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