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Article
How to High-Efficiently Acquire Activity Pattern in Smart Environment
Proceedings of the 2016 IEEE International Conferences on Big Data and Cloud Computing, Social Computing and Networking, and Sustainable Computing and Communications (2016, Atlanta, GA)
  • Chengliang Wang
  • Fei Ma
  • Yunpeng Wang
  • Debraj De
  • Sajal K. Das, Missouri University of Science and Technology
Abstract

The application of Smart Environment plays an important role in the development of advanced science and technology and therefore more and more attention. And activity recognition is the basis of Smart Environment, which reflects the intelligence of Smart Environment. However, there are two difficult and important problems which limiting the popularization of Smart Environment existing: high costs and difficulties in obtaining activity pattern. In order to overcome these problems and obtain activity pattern more effectively and efficiently, a framework for activity pattern transfer is proposed in this paper. There are two parts of activity pattern transfer: (i) Trajectory transfer, establishing the relationship on trajectories of template environment and new environment. (ii) Trigger duration transfer, transferring the trigger duration from template environment to new environment. There are four core algorithms of activity recognition based on transfer learning after pretreatment: candidate path set generation algorithm (CTSG), similarity computing algorithm (SC), trajectory mapping algorithm (TM) and trigger duration transfer algorithm (TDT). A lot of experiments had been done in the end to verify the efficiency of activity pattern transfer in simulation environment. And the experiments present the methods good time consuming performance and effectiveness.

Meeting Name
2016 IEEE International Conferences on Big Data and Cloud Computing, BDCloud 2016, Social Computing and Networking, SocialCom 2016 and Sustainable Computing and Communications, SustainCom 2016 (2016: Oct. 8-10, Atlanta, GA)
Department(s)
Computer Science
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
  • Cloud computing,
  • Conformal mapping,
  • Distributed computer systems,
  • Pattern recognition,
  • Sustainable development,
  • Trajectories,
  • Activity patterns,
  • Activity recognition,
  • Similarity computing,
  • Simulation environment,
  • Smart environment,
  • Trajectory transfer,
  • Transfer learning,
  • Trigger duration,
  • Big data,
  • Activity trajectory
International Standard Book Number (ISBN)
978-1-5090-3936-4
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
10-1-2016
Disciplines
Citation Information
Chengliang Wang, Fei Ma, Yunpeng Wang, Debraj De, et al.. "How to High-Efficiently Acquire Activity Pattern in Smart Environment" Proceedings of the 2016 IEEE International Conferences on Big Data and Cloud Computing, Social Computing and Networking, and Sustainable Computing and Communications (2016, Atlanta, GA) (2016) p. 501 - 508
Available at: http://works.bepress.com/sajal-das/68/