A Mixed Integer Programming Based Recursive Variance Reduction Method for Reliability Evaluation of Linear Sensor Systems

Vishnu Vijayaraghavan, Kiavash Kianfar, Yu Ding, Hamid Parsaei

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

Abstract

Linear models have been successfully used to establish the connections between sensor measurements and source variables in sensor networks. Sensor failures are a leading concern during the estimation of these source variables that cannot be measured directly. The reliability of a sensor system is a probabilistic evaluation of the ability of a system to withstand sensor failures. Finding the exact reliability of a linear sensor system is proven to be a #P problem. Consequently, for most practical systems, it is highly unlikely to obtain exact solutions to this problem within a reasonable timeframe. A viable alternative is to estimate the reliability using the crude Monte Carlo method. However, this method is known to be inefficient for highly reliable systems. An improved Monte Carlo approach called the Recursive Variance Reduction (RVR) method is commonly used in the literature to obtain better reliable estimates. However, the accuracy of this method banks heavily on the approach used in finding minimal cut sets of the linear sensor system. In this paper, we introduce two enhanced RVR methods in which mixed integer programming algorithms are deployed to find minimal cut sets that significantly improve the accuracy of the overall RVR technique. A case study over a wide range of test instances is conducted to establish the efficiency of the proposed methods.

Original languageEnglish
Title of host publication2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
PublisherIEEE Computer Society
Pages836-842
Number of pages7
Volume2018-August
ISBN (Electronic)9781538635933
DOIs
Publication statusPublished - 4 Dec 2018
Event14th IEEE International Conference on Automation Science and Engineering, CASE 2018 - Munich, Germany
Duration: 20 Aug 201824 Aug 2018

Other

Other14th IEEE International Conference on Automation Science and Engineering, CASE 2018
CountryGermany
CityMunich
Period20/8/1824/8/18

Fingerprint

Integer programming
Sensors
Sensor networks
Monte Carlo methods

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Vijayaraghavan, V., Kianfar, K., Ding, Y., & Parsaei, H. (2018). A Mixed Integer Programming Based Recursive Variance Reduction Method for Reliability Evaluation of Linear Sensor Systems. In 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018 (Vol. 2018-August, pp. 836-842). [8560604] IEEE Computer Society. https://doi.org/10.1109/COASE.2018.8560604

A Mixed Integer Programming Based Recursive Variance Reduction Method for Reliability Evaluation of Linear Sensor Systems. / Vijayaraghavan, Vishnu; Kianfar, Kiavash; Ding, Yu; Parsaei, Hamid.

2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. Vol. 2018-August IEEE Computer Society, 2018. p. 836-842 8560604.

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

Vijayaraghavan, V, Kianfar, K, Ding, Y & Parsaei, H 2018, A Mixed Integer Programming Based Recursive Variance Reduction Method for Reliability Evaluation of Linear Sensor Systems. in 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. vol. 2018-August, 8560604, IEEE Computer Society, pp. 836-842, 14th IEEE International Conference on Automation Science and Engineering, CASE 2018, Munich, Germany, 20/8/18. https://doi.org/10.1109/COASE.2018.8560604
Vijayaraghavan V, Kianfar K, Ding Y, Parsaei H. A Mixed Integer Programming Based Recursive Variance Reduction Method for Reliability Evaluation of Linear Sensor Systems. In 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. Vol. 2018-August. IEEE Computer Society. 2018. p. 836-842. 8560604 https://doi.org/10.1109/COASE.2018.8560604
Vijayaraghavan, Vishnu ; Kianfar, Kiavash ; Ding, Yu ; Parsaei, Hamid. / A Mixed Integer Programming Based Recursive Variance Reduction Method for Reliability Evaluation of Linear Sensor Systems. 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. Vol. 2018-August IEEE Computer Society, 2018. pp. 836-842
@inproceedings{4898ef48c2eb4b2b88c948abb9a13d8f,
title = "A Mixed Integer Programming Based Recursive Variance Reduction Method for Reliability Evaluation of Linear Sensor Systems",
abstract = "Linear models have been successfully used to establish the connections between sensor measurements and source variables in sensor networks. Sensor failures are a leading concern during the estimation of these source variables that cannot be measured directly. The reliability of a sensor system is a probabilistic evaluation of the ability of a system to withstand sensor failures. Finding the exact reliability of a linear sensor system is proven to be a #P problem. Consequently, for most practical systems, it is highly unlikely to obtain exact solutions to this problem within a reasonable timeframe. A viable alternative is to estimate the reliability using the crude Monte Carlo method. However, this method is known to be inefficient for highly reliable systems. An improved Monte Carlo approach called the Recursive Variance Reduction (RVR) method is commonly used in the literature to obtain better reliable estimates. However, the accuracy of this method banks heavily on the approach used in finding minimal cut sets of the linear sensor system. In this paper, we introduce two enhanced RVR methods in which mixed integer programming algorithms are deployed to find minimal cut sets that significantly improve the accuracy of the overall RVR technique. A case study over a wide range of test instances is conducted to establish the efficiency of the proposed methods.",
author = "Vishnu Vijayaraghavan and Kiavash Kianfar and Yu Ding and Hamid Parsaei",
year = "2018",
month = "12",
day = "4",
doi = "10.1109/COASE.2018.8560604",
language = "English",
volume = "2018-August",
pages = "836--842",
booktitle = "2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - A Mixed Integer Programming Based Recursive Variance Reduction Method for Reliability Evaluation of Linear Sensor Systems

AU - Vijayaraghavan, Vishnu

AU - Kianfar, Kiavash

AU - Ding, Yu

AU - Parsaei, Hamid

PY - 2018/12/4

Y1 - 2018/12/4

N2 - Linear models have been successfully used to establish the connections between sensor measurements and source variables in sensor networks. Sensor failures are a leading concern during the estimation of these source variables that cannot be measured directly. The reliability of a sensor system is a probabilistic evaluation of the ability of a system to withstand sensor failures. Finding the exact reliability of a linear sensor system is proven to be a #P problem. Consequently, for most practical systems, it is highly unlikely to obtain exact solutions to this problem within a reasonable timeframe. A viable alternative is to estimate the reliability using the crude Monte Carlo method. However, this method is known to be inefficient for highly reliable systems. An improved Monte Carlo approach called the Recursive Variance Reduction (RVR) method is commonly used in the literature to obtain better reliable estimates. However, the accuracy of this method banks heavily on the approach used in finding minimal cut sets of the linear sensor system. In this paper, we introduce two enhanced RVR methods in which mixed integer programming algorithms are deployed to find minimal cut sets that significantly improve the accuracy of the overall RVR technique. A case study over a wide range of test instances is conducted to establish the efficiency of the proposed methods.

AB - Linear models have been successfully used to establish the connections between sensor measurements and source variables in sensor networks. Sensor failures are a leading concern during the estimation of these source variables that cannot be measured directly. The reliability of a sensor system is a probabilistic evaluation of the ability of a system to withstand sensor failures. Finding the exact reliability of a linear sensor system is proven to be a #P problem. Consequently, for most practical systems, it is highly unlikely to obtain exact solutions to this problem within a reasonable timeframe. A viable alternative is to estimate the reliability using the crude Monte Carlo method. However, this method is known to be inefficient for highly reliable systems. An improved Monte Carlo approach called the Recursive Variance Reduction (RVR) method is commonly used in the literature to obtain better reliable estimates. However, the accuracy of this method banks heavily on the approach used in finding minimal cut sets of the linear sensor system. In this paper, we introduce two enhanced RVR methods in which mixed integer programming algorithms are deployed to find minimal cut sets that significantly improve the accuracy of the overall RVR technique. A case study over a wide range of test instances is conducted to establish the efficiency of the proposed methods.

UR - http://www.scopus.com/inward/record.url?scp=85059984347&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059984347&partnerID=8YFLogxK

U2 - 10.1109/COASE.2018.8560604

DO - 10.1109/COASE.2018.8560604

M3 - Conference contribution

AN - SCOPUS:85059984347

VL - 2018-August

SP - 836

EP - 842

BT - 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018

PB - IEEE Computer Society

ER -