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A Scheduler for Smart Homes with Probabilistic User Preferences

  • Van Nguyen
  • William Yeoh
  • Tran Cao Son
  • Vladik KreinovichEmail author
  • Tiep Le
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11873)

Abstract

Scheduling appliances is a challenging and interesting problem aimed at reducing energy consumption at a residential level. Previous work on appliance scheduling for smart homes assumes that user preferences have no uncertainty. In this paper, we study two approaches to address this problem when user preferences are uncertain. More specifically, we assume that user preferences in turning on or off a device are represented by Normal distributions. The first approach uses sample average approximation, a mathematical model, in computing a schedule. The second one relies on the fact that a scheduling problem could be viewed as a constraint satisfaction problem and uses depth-first search to identify a solution. We also conduct an experimental evaluation of the two approaches to investigate the scalability of each approach in different problem variants. We conclude by discussing computational challenges of our approaches and some possible directions for future work.

Keywords

Smart Home Scheduling Probabilistic user preference 

Notes

Acknowledgment

This research is partially supported by NSF grants 1242122, 1345232, 1619273, 1623190, 1757207, 1812618, and 1812619. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, or the U.S. government. We would also like to thank Long Tran-Thanh for initial discussions that influenced the direction of this research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Van Nguyen
    • 1
  • William Yeoh
    • 2
  • Tran Cao Son
    • 1
  • Vladik Kreinovich
    • 3
    Email author
  • Tiep Le
    • 1
    • 4
  1. 1.New Mexico State UniversityLas CrucesUSA
  2. 2.Washington University in St. LouisSt. LouisUSA
  3. 3.University of Texas at El PasoEl PasoUSA
  4. 4.Viome, Inc.Santa ClaraUSA

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