A sliding window method for online tracking of spatiotemporal event patterns

Junqi Zhang, Shanwen Zhu, Di Zang, Mengchu Zhou

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

1 Scopus citations

Abstract

Online Tracking of Spatiotemporal Event Patterns (OTSEP) is important in the fields of smart home and Internet of Things (IoT), but difficult to be resolved due to various noises. On account of the strong learning capability in noisy environments, Learning Automaton (LA) has been adopted in the existing literature to notify users once a pattern disappears, and suppress the notification to avoid the distraction from noise if a pattern exists. However, the LA-based models require continuous and identical responses from the environment to jump to another action, which lowers their learning speed especially when the noise level is high. This paper proposes a sliding window method, with which the learning speed is stable in different environments. Experimental results show that the learning accuracy and speed are greatly improved over the existing methods in dynamic and noisy environments.

Original languageEnglish (US)
Title of host publicationInternet and Distributed Computing Systems - 9th International Conference, IDCS 2016, Proceedings
PublisherSpringer Verlag
Pages513-524
Number of pages12
Volume9864 LNCS
ISBN (Print)9783319459394
DOIs
StatePublished - Jan 1 2016
Event9th International Conference on Internet and Distributed Computing Systems, IDCS 2016 - Wuhan, China
Duration: Sep 28 2016Sep 30 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9864 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Internet and Distributed Computing Systems, IDCS 2016
CountryChina
CityWuhan
Period9/28/169/30/16

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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