Information aggregation for collaborative sensing in mobile Cloud computing

Radhika Loomba, Lei Shi, Brendan Jennings, Roy Friedman, John Kennedy, Joe Butler

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

4 Citations (Scopus)

Abstract

The proliferation of smart mobile devices, having multiple sensing capabilities and significant computing power, enables their inclusion into mobile sensing systems. Sensor-driven mobile applications are drastically altering various sectors like healthcare, social networks and environmental monitoring. However, using a large number of mobile devices has an impact on the viability of techniques involved in sensing systems. Moreover, continuous sensing affects the battery performance of the mobile device. This motivates a need for energy efficient sensor-data collection with a minimum number of mobile devices. This paper presents an algorithm that makes a trade-off between the energy consumption and the number of involved mobile devices in the sensing environment, subject to satisfying the sensing needs of multiple applications. We compare our algorithm with an energy-efficient solution for sensor allocation. Results show that our algorithm suffers from less than 6% battery loss difference while reducing the number of involved mobile devices by more than 30%.

Original languageEnglish
Title of host publicationProceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014
PublisherIEEE
Pages149-158
Number of pages10
ISBN (Print)9781479925049
DOIs
Publication statusPublished - 2014
Event2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014 - Oxford, United Kingdom
Duration: 07 Apr 201410 Apr 2014

Publication series

NameProceedings - 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014

Conference

Conference2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014
Country/TerritoryUnited Kingdom
CityOxford
Period07/04/201410/04/2014

Keywords

  • Frequent pattern mining
  • Info aggregation
  • Mobile application
  • Mobile cloud
  • Mobile sensing
  • Optimization

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