DCTP: Data collecting based on trajectory prediction in Smart Environment

Chengliang Wang; Debraj De; Yayun Peng; Wen Zhan Song

doi:10.1109/SMARTCOMP-W.2014.7046673

DCTP: Data collecting based on trajectory prediction in Smart Environment

Chengliang Wang
, Debraj De
, Yayun Peng
, Wen Zhan Song

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper, we have proposed and designed a real-time distributed predicted data collection system-DCTP (Data Collection based on Trajectory Prediction according to Knowledge mined from trajectories) 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). DCTP predicts and sends predicted data of the sensor nodes which people is going to pass at one time instead of sending the triggered data in several times. Firstly, our system applies data mining to get the knowledge of transition probability among sensor nodes from the historical binary motion data. Secondly, each sensor node stores the corresponding knowledge based on a special storage mechanism. Thirdly, each triggered sensor node predicts the next destinations people will arrive at according to the received message using HMM algorithm. At last, the sensor node sends its triggered data and the predicted data to the sink node. The significances of DCTP are as follows: (a) the procedure of DCTP 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.

Original language	English
Title of host publication	Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	93-98
Number of pages	6
ISBN (Electronic)	9781479964475
DOIs	https://doi.org/10.1109/SMARTCOMP-W.2014.7046673
State	Published - Feb 20 2015
Externally published	Yes
Event	2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014 - Hong Kong, China Duration: Nov 5 2014 → …

Publication series

Name	Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014

Conference

Conference	2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014
Country/Territory	China
City	Hong Kong
Period	11/5/14 → …

Keywords

Hidden Markov Model
Human Trajectory Prediction
Smart Environments
Wireless Sensor Networks
binary motion sensor

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10.1109/SMARTCOMP-W.2014.7046673

Cite this

Wang, C., De, D., Peng, Y., & Song, W. Z. (2015). DCTP: Data collecting based on trajectory prediction in Smart Environment. In Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014 (pp. 93-98). Article 7046673 (Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMARTCOMP-W.2014.7046673

Wang, Chengliang ; De, Debraj ; Peng, Yayun et al. / DCTP : Data collecting based on trajectory prediction in Smart Environment. Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 93-98 (Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014).

@inproceedings{81da40c6ccb94078ace1bd42b26dde89,

title = "DCTP: Data collecting based on trajectory prediction in Smart Environment",

abstract = "In this paper, we have proposed and designed a real-time distributed predicted data collection system-DCTP (Data Collection based on Trajectory Prediction according to Knowledge mined from trajectories) 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). DCTP predicts and sends predicted data of the sensor nodes which people is going to pass at one time instead of sending the triggered data in several times. Firstly, our system applies data mining to get the knowledge of transition probability among sensor nodes from the historical binary motion data. Secondly, each sensor node stores the corresponding knowledge based on a special storage mechanism. Thirdly, each triggered sensor node predicts the next destinations people will arrive at according to the received message using HMM algorithm. At last, the sensor node sends its triggered data and the predicted data to the sink node. The significances of DCTP are as follows: (a) the procedure of DCTP 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.",

keywords = "Hidden Markov Model, Human Trajectory Prediction, Smart Environments, Wireless Sensor Networks, binary motion sensor",

author = "Chengliang Wang and Debraj De and Yayun Peng and Song, \{Wen Zhan\}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014 ; Conference date: 05-11-2014",

year = "2015",

month = feb,

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doi = "10.1109/SMARTCOMP-W.2014.7046673",

language = "English",

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Wang, C, De, D, Peng, Y & Song, WZ 2015, DCTP: Data collecting based on trajectory prediction in Smart Environment. in Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014., 7046673, Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014, Institute of Electrical and Electronics Engineers Inc., pp. 93-98, 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014, Hong Kong, China, 11/5/14. https://doi.org/10.1109/SMARTCOMP-W.2014.7046673

DCTP: Data collecting based on trajectory prediction in Smart Environment. / Wang, Chengliang; De, Debraj; Peng, Yayun et al.
Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014. Institute of Electrical and Electronics Engineers Inc., 2015. p. 93-98 7046673 (Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wang, Chengliang

AU - De, Debraj

AU - Peng, Yayun

AU - Song, Wen Zhan

PY - 2015/2/20

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N2 - In this paper, we have proposed and designed a real-time distributed predicted data collection system-DCTP (Data Collection based on Trajectory Prediction according to Knowledge mined from trajectories) 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). DCTP predicts and sends predicted data of the sensor nodes which people is going to pass at one time instead of sending the triggered data in several times. Firstly, our system applies data mining to get the knowledge of transition probability among sensor nodes from the historical binary motion data. Secondly, each sensor node stores the corresponding knowledge based on a special storage mechanism. Thirdly, each triggered sensor node predicts the next destinations people will arrive at according to the received message using HMM algorithm. At last, the sensor node sends its triggered data and the predicted data to the sink node. The significances of DCTP are as follows: (a) the procedure of DCTP 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.

AB - In this paper, we have proposed and designed a real-time distributed predicted data collection system-DCTP (Data Collection based on Trajectory Prediction according to Knowledge mined from trajectories) 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). DCTP predicts and sends predicted data of the sensor nodes which people is going to pass at one time instead of sending the triggered data in several times. Firstly, our system applies data mining to get the knowledge of transition probability among sensor nodes from the historical binary motion data. Secondly, each sensor node stores the corresponding knowledge based on a special storage mechanism. Thirdly, each triggered sensor node predicts the next destinations people will arrive at according to the received message using HMM algorithm. At last, the sensor node sends its triggered data and the predicted data to the sink node. The significances of DCTP are as follows: (a) the procedure of DCTP 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.

KW - Hidden Markov Model

KW - Human Trajectory Prediction

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KW - Wireless Sensor Networks

KW - binary motion sensor

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U2 - 10.1109/SMARTCOMP-W.2014.7046673

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M3 - Conference contribution

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T3 - Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014

SP - 93

EP - 98

BT - Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 5 November 2014

ER -

Wang C, De D, Peng Y, Song WZ. DCTP: Data collecting based on trajectory prediction in Smart Environment. In Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014. Institute of Electrical and Electronics Engineers Inc. 2015. p. 93-98. 7046673. (Proceedings of 2014 International Conference on Smart Computing Workshops, SMARTCOMP Workshops 2014). doi: 10.1109/SMARTCOMP-W.2014.7046673

DCTP: Data collecting based on trajectory prediction in Smart Environment

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