Research Papers

Resilience Quantification for Probabilistic Design of Cyber-Physical System Networks

[+] Author and Article Information
Yan Wang

Woodruff School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: yan.wang@me.gatech.edu

Manuscript received March 12, 2016; final manuscript received January 21, 2018; published online March 2, 2018. Assoc. Editor: Nii Attoh-Okine.

ASME J. Risk Uncertainty Part B 4(3), 031006 (Mar 02, 2018) (12 pages) Paper No: RISK-16-1072; doi: 10.1115/1.4039148 History: Received March 12, 2016; Revised January 21, 2018

Cyber-physical systems (CPS) are the physical systems of which individual components have functional identities in both physical and cyber spaces. Given the vastly diversified CPS components in dynamically evolving networks, designing an open and resilient architecture with flexibility and adaptability thus is important. To enable a resilience engineering approach for systems design, quantitative measures of resilience have been proposed by researchers. Yet, domain-dependent system performance metrics are required to quantify resilience. In this paper, generic system performance metrics for CPS are proposed, which are entropy, conditional entropy, and mutual information associated with the probabilities of successful prediction and communication. A new probabilistic design framework for CPS network architecture is also proposed for resilience engineering, where several information fusion rules can be applied for data processing at the nodes. Sensitivities of metrics with respect to the probabilistic measurements are studied. Fine-grained discrete-event simulation models of communication networks are used to demonstrate the applicability of the proposed metrics.

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Rajkumar, R. R. , Lee, I. , Sha, L. , and Stankovic, J. , 2010, “ Cyber-Physical Systems: The Next Computing Revolution,” ACM/IEEE 47th Design Automation Conference, Anaheim, CA, June 13–18, pp. 731–736.
Stankovic, J. A. , 2014, “ Research Directions for the Internet of Things,” IEEE Internet of Things J., 1(1), pp. 3–9.
Zhou, H. , Wan, J. , and Jia, H. , 2010, “ Resilience to Natural Hazards: A Geographic Perspective,” Nat. Hazards, 53(1), pp. 21–41. [CrossRef]
Francis, R. , and Bekera, B. , 2014, “ A Metric and Frameworks for Resilience Analysis of Engineered and Infrastructure Systems,” Reliab. Eng. Syst. Saf., 121, pp. 90–103. [CrossRef]
Youn, B. D. , Hu, C. , and Wang, P. , 2011, “ Resilience-Driven System Design of Complex Engineered Systems,” ASME J. Mech. Des., 133(10), p. 101011. [CrossRef]
Yodo, N. , and Wang, P. , 2016, “ Resilience Modeling and Quantification for Engineered Systems Using Bayesian Networks,” ASME J. Mech. Des., 138(3), p. 031404. [CrossRef]
Hu, Z. , and Mahadevan, S. , 2016, “ Resilience Assessment Based on Time-Dependent System Reliability Analysis,” ASME J. Mech. Des., 138(11), p. 111404. [CrossRef]
Bruneau, M. , and Reinhorn, A. , 2007, “ Exploring the Concept of Seismic Resilience for Acute Care Facilities,” Earthquake Spectra, 23(1), pp. 41–62. [CrossRef]
Cimellaro, G. P. , Reinhorn, A. M. , and Bruneau, M. , 2010, “ Framework for Analytical Quantification of Disaster Resilience,” Eng. Struct., 32(11), pp. 3639–3649. [CrossRef]
Ouyang, M. , Dueñas-Osorio, L. , and Min, X. , 2012, “ A Three-Stage Resilience Analysis Framework for Urban Infrastructure Systems,” Struct. Saf., 36–37, pp. 23–31. [CrossRef]
Ayyub, B. M. , 2015, “ Practical Resilience Metrics for Planning, Design, and Decision Making,” ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., 1(3), p. 04015008. [CrossRef]
Holling, C. S. , 1961, “ Principles of Insect Predation,” Annu. Rev. Entomol., 6(1), pp. 163–182. [CrossRef]
Rosenzweig, M. L. , and MacArthur, R. H. , 1963, “ Graphical Representation and Stability Conditions of Predator-Prey Interactions,” Am. Nat., 97(895), pp. 209–223. [CrossRef]
Lewontin, R. C. , 1969, “ The Meaning of Stability,” Diversity and Stability of Ecological Systems, Brookhaven Symposia in Biology, Brookhaven, NY, May 26–28, pp. 13–24.
Holling, C. S. , 1973, “ Resilience and Stability of Ecological Systems,” Annu. Rev. Ecol. Syst., 4(1), pp. 1–23. [CrossRef]
Folke, C. , 2006, “ Resilience: The Emergence of a Perspective for Social–Ecological Systems Analyses,” Global Environ. Change, 16(3), pp. 253–267. [CrossRef]
Scheffer, M. , and Carpenter, S. R. , 2003, “ Catastrophic Regime Shifts in Ecosystems: Linking Theory to Observation,” Trends Ecol. Evol., 18(12), pp. 648–656. [CrossRef]
Arrow, K. , Bolin, B. , Costanza, R. , Dasgupta, P. , Folke, C. , Holling, C. S. , Jansson, B.-O. , Levin, S. , Mäler, K.-G. , Perrings, C. , and Pimentel, D. , 1995, “ Economic Growth, Carrying Capacity, and the Environment,” Science, 268(5210), pp. 520–521. [CrossRef] [PubMed]
Christopherson, S. , Michie, J. , and Tyler, P. , 2010, “ Regional Resilience: Theoretical and Empirical Perspectives,” Cambridge J. Reg. Econ. Soc., 3(1), pp. 3–10. [CrossRef]
Martin, R. , 2012, “ Regional Economic Resilience, Hysteresis and Recessionary Shocks,” J. Econ. Geogr., 12(1), pp. 1–32. [CrossRef]
Foster, K. A. , 2007, “ A Case Study Approach to Understanding Regional Resilience,” Institute of Urban and Regional Development, University of California, Berkeley, CA, Report No. 2007-08.
Hill, E. , Wial, H. , and Wolman, H. , 2008, “ Exploring Regional Economic Resilience,” Institute of Urban and Regional Development, University of California, Berkeley, CA, Report No. 2008-04.
Schiefer, H. F. , 1933, “ The Compressometer an Instrument for Evaluating the Thickness, Compressibility and Compressional Resilience of Textiles and Similar Materials,” Text. Res. J., 3(10), pp. 505–513. [CrossRef]
Mark, H. , 1946, “ Some Remarks About Resilience of Textile Materials,” Text. Res. J., 16(8), pp. 361–368. [CrossRef]
Hoffman, R. M. , 1948, “ A Generalized Concept of Resilience,” Text. Res. J., 18(3), pp. 141–148. [CrossRef]
Fielding, J. H. , 1937, “ Impact Resilience in Testing Channel Black,” Rubber Chem. Technol., 10(4), pp. 807–819. [CrossRef]
Turner, L. B. , Haworth, J. P. , Smith, W. C. , and Zapp, R. L. , 1943, “ Carbon Black in Butyl Rubber,” Ind. Eng. Chem., 35(9), pp. 958–963. [CrossRef]
Dillon, J. H. , Prettyman, I. B. , and Hall, G. L. , 1944, “ Hysteretic and Elastic Properties of Rubberlike Materials Under Dynamic Shear Stresses,” J. Appl. Phys., 15(4), pp. 309–323. [CrossRef]
Liu, J. W. , Shih, W. K. , Lin, K. J. , Bettati, R. , and Chung, J. Y. , 1994, “ Imprecise Computations,” Proc. IEEE, 82(1), pp. 83–94. [CrossRef]
Hegde, R. , and Shanbhag, N. R. , 1999, “ Energy-Efficient Signal Processing Via Algorithmic Noise-Tolerance,” International Symposium on Low Power Electronics and Design, San Diego, CA, Aug. 16–17, pp. 30–35.
Cho, H. , Leem, L. , and Mitra, S. , 2012, “ ERSA: Error Resilient System Architecture for Probabilistic Applications,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 31(4), pp. 546–558. [CrossRef]
Chippa, V. K. , Mohapatra, D. , Raghunathan, A. , Roy, K. , and Chakradhar, S. T. , 2010, “ Scalable Effort Hardware Design: Exploiting Algorithmic Resilience for Energy Efficiency,” 47th ACM/IEEE Design Automation Conference (DAC'10), Anaheim, CA, June 13–18, pp. 555–560.
Verma, N. , Lee, K. H. , Jang, K. J. , and Shoeb, A. , 2012, “ Enabling System-Level Platform Resilience Through Embedded Data-Driven Inference Capabilities in Electronic Devices,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, Mar. 25–30, pp. 5285–5288.
Wang, Z. , Schapire, R. E. , and Verma, N. , 2015, “ Error Adaptive Classifier Boosting (EACB): Leveraging Data-Driven Training Towards Hardware Resilience for Signal Inference,” IEEE Trans. Circuits Syst. I: Regular Papers, 62(4), pp. 1136–1145. [CrossRef]
Abdallah, R. , and Shanbhag, N. R. , 2013, “ Error-Resilient Systems Via Statistical Signal Processing,” IEEE Workshop on Signal Processing Systems (SiPS), Taipei City, Taiwan, Oct. 16–18, pp. 312–317.
Schneider , F. B. , ed., 1999, Trust in Cyberspace, National Academies Press, Washington, DC.
Lin, H. S. , and Goodman, S. E. , eds., 2007, Toward a Safer and More Secure Cyberspace, National Academies Press, Washington, DC.
Sterbenz, J. P. , Hutchison, D. , Çetinkaya, E. K. , Jabbar, A. , Rohrer, J. P. , Schöller, M. , and Smith, P. , 2010, “ Resilience and Survivability in Communication Networks: Strategies, Principles, and Survey of Disciplines,” Comput. Networks, 54(8), pp. 1245–1265. [CrossRef]
Hollnagel , E., Woods , D. D. , and Leveson , N. , eds., 2007, Resilience Engineering: Concepts and Precepts, Ashgate Publishing, Burlington, VT.
Madni, A. M. , and Jackson, S. , 2009, “ Towards a Conceptual Framework for Resilience Engineering,” IEEE Syst. J., 3(2), pp. 181–191. [CrossRef]
Hollnagel, E. , and Fujita, Y. , 2013, “ The Fukushima Disaster—Systematic Failures as the Lack of Resilience,” Nucl. Eng. Technol., 45(1), pp. 13–20. [CrossRef]
Khabbaz, M. J. , Assi, C. M. , and Fawaz, W. F. , 2012, “ Disruption-Tolerant Networking: A Comprehensive Survey on Recent Developments and Persisting Challenges,” IEEE Commun. Surv. Tutorials, 14(2), pp. 607–640. [CrossRef]
Miu, A. , Balakrishnan, H. , and Koksal, C. E. , 2005, “ Improving Loss Resilience With Multi-Radio Diversity in Wireless Networks,” 11th Annual International Conference on Mobile Computing and Networking (MobiCom), Cologne, Germany, Aug. 28–Sept. 2, pp. 16–30.
Lei, J. J. , and Kwon, G. I. , 2010, “ Reliable Data Transmission Based on Erasure-Resilient Code in Wireless Sensor Networks,” TIIS Trans. Internet Inf. Syst., 4(1), pp. 62–77.
Huang, Y. , Gao, Y. , Nahrstedt, K. , and He, W. , 2009, “ Optimizing File Retrieval in Delay-Tolerant Content Distribution Community,” 29th IEEE International Conference on Distributed Computing Systems (ICDCS'09), Montreal, QC, Canada, June 22–26, pp. 308–316.
Cohen, R. , Erez, K. , Ben-Avraham, D. , and Havlin, S. , 2000, “ Resilience of the Internet to Random Breakdowns,” Phys. Rev. Lett., 85(21), p. 4626. [CrossRef] [PubMed]
Çetinkaya, E. K. , Broyles, D. , Dandekar, A. , Srinivasan, S. , and Sterbenz, J. P. , 2013, “ Modelling Communication Network Challenges for Future Internet Resilience, Survivability, and Disruption Tolerance: A Simulation-Based Approach,” Telecommun. Syst., 52(2), pp. 751–766.
Rohrer, J. P. , Jabbar, A. , and Sterbenz, J. P. , 2014, “ Path Diversification for Future Internet End-to-End Resilience and Survivability,” Telecommun. Syst., 56(1), pp. 49–67. [CrossRef]
Paul, G. , Sreenivasan, S. , and Stanley, H. E. , 2005, “ Resilience of Complex Networks to Random Breakdown,” Phys. Rev. E, 72(5), p. 056130. [CrossRef]
Sun, F. , and Shayman, M. A. , 2007, “ On Pairwise Connectivity of Wireless Multihop Networks,” Int. J. Secur. Networks, 2(1–2), pp. 37–49. [CrossRef]
Shirazi, F. , Diaz, C. , and Wright, J. , 2015, “ Towards Measuring Resilience in Anonymous Communication Networks,” 14th ACM Workshop on Privacy in the Electronic Society (WPES), Denver, CO, Oct. 12, pp. 95–99.
Pradhan, S. , Dubey, A. , Levendovszky, T. , Kumar, P. S. , Emfinger, W. A. , Balasubramanian, D. , Otte, W. , and Karsai, G. , 2016, “ Achieving Resilience in Distributed Software Systems Via Self-Reconfiguration,” J. Syst. Software, 122, pp. 344–363. [CrossRef]
Sheffi, Y. , 2005, The Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage, MIT Press, Cambridge, MA.
Hohenstein, N. O. , Feisel, E. , Hartmann, E. , and Giunipero, L. , 2015, “ Research on the Phenomenon of Supply Chain Resilience: A Systematic Review and Paths for Further Investigation,” Int. J. Phys. Distrib. Logist. Manage., 45(1/2), pp. 90–117. [CrossRef]
Tukamuhabwa, B. R. , Stevenson, M. , Busby, J. , and Zorzini, M. , 2015, “ Supply Chain Resilience: Definition, Review and Theoretical Foundations for Further Study,” Int. J. Prod. Res., 53(18), pp. 5592–5623. [CrossRef]
Ivanov, D. , Mason, S. J. , and Hartl, R. , 2016, “ Supply Chain Dynamics, Control and Disruption Management,” Int. J. Prod. Res., 54(1), pp. 1–7. [CrossRef]
Chopra, S. , Reinhardt, G. , and Mohan, U. , 2007, “ The Importance of Decoupling Recurrent and Disruption Risks in a Supply Chain,” Nav. Res. Logist., 54(5), pp. 544–555. [CrossRef]
Snyder, L. V. , and Daskin, M. S. , 2005, “ Reliability Models for Facility Location: The Expected Failure Cost Case,” Transp. Sci., 39(3), pp. 400–416. [CrossRef]
Li, X. , and Ouyang, Y. , 2010, “ A Continuum Approximation Approach to Reliable Facility Location Design Under Correlated Probabilistic Disruptions,” Transp. Res. Part B: Methodol., 44(4), pp. 535–548. [CrossRef]
Yang, Y. , and Xu, X. , 2015, “ Post-Disaster Grain Supply Chain Resilience With Government Aid,” Transp. Res. Part E: Logist. Transp. Rev., 76, pp. 139–159. [CrossRef]
Seok, H. , Kim, K. , and Nof, S. Y. , 2016, “ Intelligent Contingent Multi-Sourcing Model for Resilient Supply Networks,” Expert Syst. Appl., 51, pp. 107–119. [CrossRef]
Spiegler, V. L. , Naim, M. M. , and Wikner, J. , 2012, “ A Control Engineering Approach to the Assessment of Supply Chain Resilience,” Int. J. Prod. Res., 50(21), pp. 6162–6187. [CrossRef]
Hu, Y. , Li, J. , and Holloway, L. E. , 2013, “ Resilient Control for Serial Manufacturing Networks With Advance Notice of Disruptions,” IEEE Trans. Syst., Man, Cybern. Syst., 43(1), pp. 98–114. [CrossRef]
Hishamuddin, H. , Sarker, R. A. , and Essam, D. , 2013, “ A Recovery Model for a Two-Echelon Serial Supply Chain With Consideration of Transportation Disruption,” Comput. Ind. Eng., 64(2), pp. 552–561. [CrossRef]
Mari, S. I. , Lee, Y. H. , Memon, M. S. , Park, Y. S. , and Kim, M. , 2015, “ Adaptivity of Complex Network Topologies for Designing Resilient Supply Chain Networks,” Int. J. Ind. Eng., 22(1), pp. 102–116.
Wang, Y. , 2016, “ System Resilience Quantification for Probabilistic Design of Internet-of-Things Architecture,” ASME Paper No. DETC2016-59426.
USC/ISI, Xerox PARC, LBNL, and UCB, 2017, “ Network Simulator ns-2,” accessed Feb. 5, 2018, http://www.isi.edu/nsnam/ns/


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Fig. 1

System performance curve used by Francis and Bekera [4]

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Fig. 3

The entropies and prediction probabilities of the simulated system in Fig. 2(b), where the maximum number of disconnected edges is 76: (a) the average conditional entropy calculated from Eq. (10) and the average entropy calculated from prediction probability in Eq. (8) and (b) the minimum and maximum values of prediction probabilities among ten nodes

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Fig. 2

Performance measure in Eq. (12) for a simulated CPS network with 10 nodes: (a) the maximum number of disconnected edges is 39 and (b) the maximum number of disconnected edges is 76

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Fig. 4

Performance measure of a simulated CPS network with 30 nodes: (a) the maximum number of disconnected edges is 49 and (b) the maximum number of disconnected edges is 834

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Fig. 5

The entropies and prediction probabilities of the simulated system in Fig. 4(b), where the maximum number of disconnected edges is 834: (a) the average conditional entropy and the average entropy and (b) the minimum and maximum values of prediction probabilities among 30 nodes

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Fig. 10

Two ring models simulated in ns-2 for demonstration

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Fig. 11

Comparison of metrics for the two simulated ring models

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Fig. 6

Simulation results based on the Bayesian fusion rule for a system of 30 nodes with a maximum of 826 disrupted connections: (a) average mutual information performance measure and (b) average conditional entropy and entropy

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Fig. 7

Sensitivity of conditional entropy with respect to prediction probability

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Fig. 8

Sensitivity analysis based on the best-case fusion rule by increasing all reliance probabilities by 25% (+25%), reducing all by 25% (−25%), increasing only those large probabilities that are greater than 0.5 by 25% (Large + 25%), reducing only these large probabilities (Large −25%), increasing only those small probabilities that are less than 0.5 by 25% (Small + 25%), and reducing only those small probabilities (Small −25%): (a) average conditional entropies and entropies and (b) average mutual information

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Fig. 9

Sensitivity of a simulated system based on the Bayesian rule




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