SPEED: An inhabitant activity prediction algorithm for smart homes

Muhammad Raisul Alam, Md. Mamun Ibne Reaz, M. A. Mohd Ali

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the on -off states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3% prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5.

Original languageEnglish
Article number6097066
Pages (from-to)985-990
Number of pages6
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Volume42
Issue number4
DOIs
Publication statusPublished - Jul 2012

Fingerprint

Domestic appliances

Keywords

  • Activity prediction
  • Markov model
  • prediction algorithm
  • prediction by partial matching (PPM)
  • smart home

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems
  • Software

Cite this

SPEED : An inhabitant activity prediction algorithm for smart homes. / Alam, Muhammad Raisul; Ibne Reaz, Md. Mamun; Mohd Ali, M. A.

In: IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, Vol. 42, No. 4, 6097066, 07.2012, p. 985-990.

Research output: Contribution to journalArticle

@article{c2df3445a86e480ab3ad778daacf0f24,
title = "SPEED: An inhabitant activity prediction algorithm for smart homes",
abstract = "This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the on -off states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3{\%} prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5.",
keywords = "Activity prediction, Markov model, prediction algorithm, prediction by partial matching (PPM), smart home",
author = "Alam, {Muhammad Raisul} and {Ibne Reaz}, {Md. Mamun} and {Mohd Ali}, {M. A.}",
year = "2012",
month = "7",
doi = "10.1109/TSMCA.2011.2173568",
language = "English",
volume = "42",
pages = "985--990",
journal = "IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.",
issn = "1083-4427",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - SPEED

T2 - An inhabitant activity prediction algorithm for smart homes

AU - Alam, Muhammad Raisul

AU - Ibne Reaz, Md. Mamun

AU - Mohd Ali, M. A.

PY - 2012/7

Y1 - 2012/7

N2 - This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the on -off states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3% prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5.

AB - This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the on -off states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3% prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5.

KW - Activity prediction

KW - Markov model

KW - prediction algorithm

KW - prediction by partial matching (PPM)

KW - smart home

UR - http://www.scopus.com/inward/record.url?scp=84862524025&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862524025&partnerID=8YFLogxK

U2 - 10.1109/TSMCA.2011.2173568

DO - 10.1109/TSMCA.2011.2173568

M3 - Article

AN - SCOPUS:84862524025

VL - 42

SP - 985

EP - 990

JO - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.

JF - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.

SN - 1083-4427

IS - 4

M1 - 6097066

ER -