Distance-based cluster head election for mobile sensing

Ruairí de Fréin, Liam Orfarrell

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Energy-efficient, fair, stochastic leader-selection algorithms are designed for mobile sensing scenarios which adapt the sensing strategy depending on the mobile sensing topology. Methods for electing a cluster head are crucially important when optimizing the trade-off between the number of peer-to-peer interactions between mobiles and client-server interactions with a cloud-hosted application server. The battery-life of mobile devices is a crucial constraint facing application developers who are looking to use the convergence of mobile computing and cloud computing to perform environmental sensing. We exploit the mobile network topology, specifically the location of mobiles with respect to the gateway device, to stochastically elect a cluster head so that (1) different energy saving policies can be implemented and (2) battery lifetime is increased given an energy efficiency policy, in a fair way. We demonstrate that the battery usage can be made more fair by reducing the difference in battery life-time of mobiles by up to 66%.

Original languageEnglish
Title of host publication2018 12th International Conference on Sensing Technology, ICST 2018
PublisherIEEE Computer Society
Pages196-201
Number of pages6
ISBN (Electronic)9781538651476
DOIs
Publication statusPublished - 2 Jul 2018
Event12th International Conference on Sensing Technology, ICST 2018 - Limerick, Ireland
Duration: 4 Dec 20186 Dec 2018

Publication series

NameProceedings of the International Conference on Sensing Technology, ICST
Volume2018-December
ISSN (Print)2156-8065
ISSN (Electronic)2156-8073

Conference

Conference12th International Conference on Sensing Technology, ICST 2018
Country/TerritoryIreland
CityLimerick
Period4/12/186/12/18

Keywords

  • Battery Life-time
  • Cloud Computing
  • Energy Efficiency
  • Leader-Election
  • Mobile Sensing

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