Traffic Pattern Based Data Recovery Scheme for Cyber-Physical Systems

Naushin NOWER; Yasuo TAN; Azman Osman LIM

doi:10.1587/transfun.E97.A.1926

Traffic Pattern Based Data Recovery Scheme for Cyber-Physical Systems

Naushin NOWER, Yasuo TAN, Azman Osman LIM

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Feedback data loss can severely degrade overall system performance. In addition, it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging applications that include different data traffic patterns. These data traffic patterns have wide varieties of diversities. To recover various traffic patterns we need to know the nature of their underlying property. In this paper, we propose a data recovery framework for different traffic patterns of CPS, which comprises data pre-processing step. In the proposed framework, we designed a Data Pattern Analyzer to classify the different patterns and built a model based on the pattern as a data pre-processing step. Inside the framework, we propose a data recovery scheme, called Efficient Temporal and Spatial Data Recovery (ETSDR) algorithm to recover the incomplete feedback for CPS to maintain real time control. In this algorithm, we utilize the temporal model based on the traffic pattern and consider the spatial correlation of the nearest neighbor sensors. Numerical results reveal that the proposed ETSDR outperforms both the weighted prediction (WP) and the exponentially weighted moving average (EWMA) algorithms regardless of the increment percentage of missing data in terms of the root mean square error, the mean absolute error, and the integral of absolute error.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E97-A No.9 pp.1926-1936

Publication Date: 2014/09/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E97.A.1926

Type of Manuscript: PAPER

Category: Systems and Control

Authors

Naushin NOWER
  Japan Advanced Institute of Science and Technology (JAIST)
Yasuo TAN
  Japan Advanced Institute of Science and Technology (JAIST)
Azman Osman LIM
  Japan Advanced Institute of Science and Technology (JAIST)

Keyword

data pattern analyzer, stochastic traffic pattern, data recovery, cyber-physical systems

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Naushin NOWER, Yasuo TAN, Azman Osman LIM, "Traffic Pattern Based Data Recovery Scheme for Cyber-Physical Systems" in IEICE TRANSACTIONS on Fundamentals, vol. E97-A, no. 9, pp. 1926-1936, September 2014, doi: 10.1587/transfun.E97.A.1926.
Abstract: Feedback data loss can severely degrade overall system performance. In addition, it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging applications that include different data traffic patterns. These data traffic patterns have wide varieties of diversities. To recover various traffic patterns we need to know the nature of their underlying property. In this paper, we propose a data recovery framework for different traffic patterns of CPS, which comprises data pre-processing step. In the proposed framework, we designed a Data Pattern Analyzer to classify the different patterns and built a model based on the pattern as a data pre-processing step. Inside the framework, we propose a data recovery scheme, called Efficient Temporal and Spatial Data Recovery (ETSDR) algorithm to recover the incomplete feedback for CPS to maintain real time control. In this algorithm, we utilize the temporal model based on the traffic pattern and consider the spatial correlation of the nearest neighbor sensors. Numerical results reveal that the proposed ETSDR outperforms both the weighted prediction (WP) and the exponentially weighted moving average (EWMA) algorithms regardless of the increment percentage of missing data in terms of the root mean square error, the mean absolute error, and the integral of absolute error.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E97.A.1926/_p

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@ARTICLE{e97-a_9_1926,
author={Naushin NOWER, Yasuo TAN, Azman Osman LIM, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Traffic Pattern Based Data Recovery Scheme for Cyber-Physical Systems},
year={2014},
volume={E97-A},
number={9},
pages={1926-1936},
abstract={Feedback data loss can severely degrade overall system performance. In addition, it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging applications that include different data traffic patterns. These data traffic patterns have wide varieties of diversities. To recover various traffic patterns we need to know the nature of their underlying property. In this paper, we propose a data recovery framework for different traffic patterns of CPS, which comprises data pre-processing step. In the proposed framework, we designed a Data Pattern Analyzer to classify the different patterns and built a model based on the pattern as a data pre-processing step. Inside the framework, we propose a data recovery scheme, called Efficient Temporal and Spatial Data Recovery (ETSDR) algorithm to recover the incomplete feedback for CPS to maintain real time control. In this algorithm, we utilize the temporal model based on the traffic pattern and consider the spatial correlation of the nearest neighbor sensors. Numerical results reveal that the proposed ETSDR outperforms both the weighted prediction (WP) and the exponentially weighted moving average (EWMA) algorithms regardless of the increment percentage of missing data in terms of the root mean square error, the mean absolute error, and the integral of absolute error.},
keywords={},
doi={10.1587/transfun.E97.A.1926},
ISSN={1745-1337},
month={September},}

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TY - JOUR
TI - Traffic Pattern Based Data Recovery Scheme for Cyber-Physical Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1926
EP - 1936
AU - Naushin NOWER
AU - Yasuo TAN
AU - Azman Osman LIM
PY - 2014
DO - 10.1587/transfun.E97.A.1926
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E97-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2014
AB - Feedback data loss can severely degrade overall system performance. In addition, it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging applications that include different data traffic patterns. These data traffic patterns have wide varieties of diversities. To recover various traffic patterns we need to know the nature of their underlying property. In this paper, we propose a data recovery framework for different traffic patterns of CPS, which comprises data pre-processing step. In the proposed framework, we designed a Data Pattern Analyzer to classify the different patterns and built a model based on the pattern as a data pre-processing step. Inside the framework, we propose a data recovery scheme, called Efficient Temporal and Spatial Data Recovery (ETSDR) algorithm to recover the incomplete feedback for CPS to maintain real time control. In this algorithm, we utilize the temporal model based on the traffic pattern and consider the spatial correlation of the nearest neighbor sensors. Numerical results reveal that the proposed ETSDR outperforms both the weighted prediction (WP) and the exponentially weighted moving average (EWMA) algorithms regardless of the increment percentage of missing data in terms of the root mean square error, the mean absolute error, and the integral of absolute error.
ER -