Computer Science ›› 2020, Vol. 47 ›› Issue (3): 11-18.doi: 10.11896/jsjkx.191100052

• Intelligent Software Engineering • Previous Articles     Next Articles

Taxonomy of Uncertainty Factors in Intelligence-oriented Cyber-physical Systems

YANG Wen-hua1,2,3,XU Chang3,4,YE Hai-bo1,ZHOU Yu1,HUANG Zhi-qiu1   

  1. (College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)1;
    (Key Laboratory of Safety-Critical Software (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, Nanjing 211106, China)2;
    (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China)3;
    (Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China)4
  • Received:2019-10-07 Online:2020-03-15 Published:2020-03-30
  • About author:YANG Wen-hua,born in 1990,Ph.D,lecturer,is member of China Computer Federation (CCF).His main research interests include self-adaptive software systems and intelligent software systems. XU Chang,born in 1977,Ph.D,professor,is senior member of China ComputerFederation (CCF).His main research interests include big data software engineering,and intelligent software testing and analysis.
  • Supported by:
    This work was supported by the National Key R&D Program of China (2017YFB1001801), National Natural Science Foundation of China (61802179, 61932021, 61972197), Open Fund of State Key Laboratory of Novel Software Technology (KFKT2018B02) and Qing Lan Project.

Abstract: Cyber-physical systems are increasingly presenting the characteristic of intelligence,while uncertainty is pervasive and intrinsic in them,e.g.,the sensors contain inevitable errors when the systems sense the environment through them.If the uncertainty is not properly handled,it will affect the correct running of the systems and bring a series of problems.Therefore,it is critical to study how to deal with uncertainty in cyber-physical systems.The premise of handling uncertainty is that we first need to understand and recognize it comprehensively.However,the existing work on the uncertainty of cyber-physical systems is still in its infancy.To address this issue,this paper studied the taxonomy of uncertainty in cyber-physical systems.Specifically,this paper classified the uncertainty based on the widely recognized 5C technology architecture in cyber-physical systems and introduced the possible uncertainties at each level of the technology architecture with illustrating examples in typical cyber-physical systems.Meanwhile,to help understand the current research status of uncertainty handling in the field of cyber-physical systems,this paper summarized the current research work and presented an outlook of future research directions for intelligence-oriented cyber-physical systems.

Key words: Cyber-physical systems, Intelligentize, Uncertainty, Taxonomy, 5C architecture, Uncertainty handling

CLC Number: 

  • TP311
[1]MITRA S,WONGPIROMSARN T,MURRAY R M.Verifying cyber-physical interactions in safety-critical systems[J].IEEE Security & Privacy,2013,11(4):28-37.
[2]ELBAUM S,ROSENBLUM D S.Known unknowns:testing in the presence of uncertainty[C]∥Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2014.New York,USA:ACM Press,2014.
[3]WANG X,HOVAKIMYAN N,SHA L.L1Simplex:fault-tolerant control of cyber-physical systems[C]∥2013 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).IEEE,2013:41-50.
[4]ZHANG M,SELIC B,ALI S,et al.Understanding uncertainty in cyber-physical systems:A conceptual model[M]∥Modelling Foundations and Applications.Cham:Springer International Publishing,2016:247-264.
[5]YANG W H,XU C,PAN M X,et al.Improving verification accuracy of CPS by modeling and calibrating interaction uncertainty[J].ACM Transactions on Internet Technology,2018,18(2):1-37.
[6]WOLF W.News briefs[J].Computer,2007,40(11):104-105.
[7]LEE E A.Cyber physical systems:design challenges[C]∥2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).IEEE,2008:363-369.
[8]BAHETI R,GILL H.Cyber-physical systems[J].The Impact of Control Technology,2011,12(1):161-166.
[9]LEE J,BAGHERI B,KAO H G.A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems[J].Manufacturing Letters,2015,3:18-23.
[10]LEE I,SOKOLSKY O.Medical cyber physical systems[C]∥ Proceedings of the 47th Design Automation Conference on - DAC ’10.New York,USA:ACM Press,2010:743-748.
[11]DEKA L,KHAN S M,CHOWDHURY M,et al.Transportation Cyber-Physical System and its importance for future mobility[M]∥Transportation Cyber-Physical Systems.Elsevier,2018:1-20.
[12]DO Q,MARTINI B,CHOO K K,et al.Cyber-physical systems information gathering:a smart home case study[J].Computer Networks,2018,138:1-12.
[13]BU L,XIONG W,LIANG C J M,et al.Systematically ensuring the confidence of real-time home automation IoT systems[J].ACM Transactions on Cyber-Physical Systems,2018,2(3):1-23.
[14]NICODANO G.The Economics of Uncertainty and Information by J.-J.Laffont[J].Giornale Degli Economisti E Annali Di Economia,1989,48(3/4):183-184.
[15]GILBOA I,POSTLEWAITE A W,SCHMEIDLER D.Probabili- ty and uncertainty in economic modeling[J].Journal of Economic Perspectives,2008,22(3):173-188.
[16]TAYLOR B N.Guidelines for evaluating and expressing the uncertainty of NIST measurement results[R].National Bureau of Standards,1994.
[17]TVERSKY A,KAHNEMAN D.Judgment under uncertainty:heuristics and biases[M]∥Uncertainty in Economics.Elsevier,1978:17-34.
[18]AUGHENBAUGH J M.Managing uncertainty in engineering design using imprecise probabilities and principles of information economics[D].Georgia Institute of Technology,2006.
[19]ESFAHANI N,KOUROSHFAR E,MALEK S.Taming uncertainty in self-adaptive software[C]∥Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering(SIGSOFT/FSE’11).New York,USA:ACM Press,2011:234-244.
[20]RAMIREZ A J,JENSEN A C,CHENG B H C.A taxonomy of uncertainty for dynamically adaptive systems[C]∥2012 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS).IEEE,2012:99-108.
[21]Networking and Information Technology Research and Development Program.High-Confidence Medical Devices:Cyber-Physical Systems for 21st Century Health Care [OL].http://www.nitrd.gov/About/MedDevice-FINAL1-web.pdf.
[22]Faulty sensor led to Boeing 737 Max crash [OL].https:// www.latimes.com/business/la-fi-ethiopian- airlines- crash- report-20190404-story.html.
[23]AIEN M,HAJEBRAHIMI A,FOTUHI-FIRUZABAD M.A comprehensive review on uncertainty modeling techniques in power system studies[J].Renewable and Sustainable Energy Reviews,2016,57:1077-1089.
[24]MUCCINI H,SHARAF M,WEYNS D.Self-adaptation for cy- ber-physical systems[C]∥Proceedings of the 11th International Workshop on Software Engineering for Adaptive and Self-Managing Systems(SEAMS’16).New York,USA:ACM Press,2016:75-81.
[25]BURES T,KNAUSS A,PATEL P,et al.Software engineering for smart cyber-physical systems[J].ACM SIGSOFT Software Engineering Notes,2017,42(2):19-24.
[26]CHENG B H C,SAWYER P,BENCOMO N,et al.A goal-based modeling approach to develop requirements of an adaptive system with environmental uncertainty[M]∥Model Driven Engineering Languages and Systems.Berlin:Springer,2009:468-483.
[27]AHMAD M,GNAHO C,BRUEL J M,et al.How to handle en- vironmental uncertainty in goal-based requirements engineering[C]∥Proceedings of the 40th International Conference on Software Engineering Companion Proceeedings(ICSE’18).New York,USA:ACM Press,2018:368-369.
[28]YANG W H,XU C,LIU Y P,et al.Verifying self-adaptive applications suffering uncertainty[C]∥Proceedings of the 29th ACM/IEEE international conference on Automated software engineering(ASE’14).New York,USA:ACM Press,2014:199-210.
[29]LIU B D.Uncertainty theory[M]∥Uncertainty Theory.Berlin: Springer,2010:1-79.
[30]ZHU Q Y,BASAR T.Robust and resilient control design for cyber-physical systems with an application to power systems[C]∥IEEE Conference on Decision and Control and European Control Conference.IEEE,2011:4066-4071.
[31]MORENO G A,CÁMARA J,GARLAN D,et al.Proactive self-adaptation under uncertainty:a probabilistic model checking approach[C]∥Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering(ESEC/FSE 2015).New York,USA:ACM Press,2015:1-12.
[32]CRENSHAW T L,GUNTER E,ROBINSON C L,et al.The simplex reference model:limiting fault-propagation due to unreliable components in cyber-physical system architectures[C]∥28th IEEE International Real-Time Systems Symposium (RTSS 2007).IEEE,2007.
[33]ALI S,YUE T.U-test:evolving,modelling and testing realistic uncertain behaviours of cyber-physical systems[C]∥2015 IEEE 8th International Conference on Software Testing,Verification and Validation (ICST).IEEE,2015:1-2.
[34]ZHANG M,ALI S,YUE T,et al.Uncertainty-wise evolution of test ready models[J].Information and Software Technology,2017,87:140-159.
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