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DPHK: real-time distributed predicted data collecting based on activity pattern knowledge mined from trajectories in smart environments

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Abstract

In this paper, we have proposed and designed DPHK (data prediction based on HMM according to activity pattern knowledge mined from trajectories), a real-time distributed predicted data collection system to solve the congestion and data loss caused by too many connections to sink node in indoor smart environment scenarios (like Smart Home, Smart Wireless Healthcare and so on). DPHK predicts and sends predicted data at one time instead of sending the triggered data of these sensor nodes which people is going to pass in several times. Firstly, our system learns the knowledge of transition probability among sensor nodes from the historical binary motion data through data mining. Secondly, it stores the corresponding knowledge in each sensor node based on a special storage mechanism. Thirdly, each sensor node applies HMM (hidden Markov model) algorithm to predict the sensor node locations people will arrive at according to the receivedmessage. At last, these sensor nodes send their triggered data and the predicted data to the sink node. The significances of DPHK are as follows: (a) the procedure of DPHK is distributed; (b) it effectively reduces the connection between sensor nodes and sink node. The time complexities of the proposed algorithms are analyzed and the performance is evaluated by some designed experiments in a smart environment.

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Authors and Affiliations

  1. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University, Chongqing, 400044, China

    Chengliang Wang

  2. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Chengliang Wang & Yayun Peng

  3. Department of Computer Science, Georgia State University, Atlanta, GK, 30303, USA

    Debraj De & Wen-Zhan Song

Authors
  1. Chengliang Wang
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  2. Yayun Peng
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  3. Debraj De
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  4. Wen-Zhan Song
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Corresponding author

Correspondence to Chengliang Wang.

Additional information

Chengliang Wang is a doctor, professor and PhD visiting scholar to Georgia Institute of Technology, USA and senior member of China Computer Science Association, China and member of America Association of Computing Machinery, USA. His research interests include smart environemt theory and application based on Internet of things; the research and application of video monitoring based on image processing; research and development of intelligent computing on the complex system.

Yayun Peng is currently a master student of Department of Computer Science, ChongQing University, China. His current research interests are in the areas of smart environments, data mining, wireless sensor networks.

Debraj De is currently a postdoctoral research associate in Department of Computer Science, Missouri University of Science and Technology, USA. His current research interests are in the areas of smart environments, smart healthcare, machine learning, wearables and sensor networks.

Wen-zhan Song research mainly focuses on cyber–physical systems and computing for geophysical imaging, smart grid and smart health, where decentralized sensing, computing, communication and security play a critical role and need a transformative study. He is a recipient of NSF CAREER Award (2010), Outstanding Research Contribution Award (2012) by GSU Computer Science, Chancellor Research Excellence Award (2010) by WSU Vancouver.

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Wang, C., Peng, Y., De, D. et al. DPHK: real-time distributed predicted data collecting based on activity pattern knowledge mined from trajectories in smart environments. Front. Comput. Sci. 10, 1000–1011 (2016). https://doi.org/10.1007/s11704-015-4571-6

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  • DOI: https://doi.org/10.1007/s11704-015-4571-6

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