A Digital Twin Method for Automated Behavior Analysis of Large-Scale Distributed IoT Systems

The behavior of a large-scale distributed IoT system is often hard to verify and validate. The reasons include: 1) the specification is often unclear, ambiguous and incomplete resulting in misunderstandings and undesired behavior. 2) It is almost impossible for a human to reason about the correctness of a system consisting of thousands of components. 3) It is very hard to observe all related components when trying to solve a problem because the system is geographically distributed over large areas. A digital twin capturing the system operational behavior will be of great help to assist a human in detecting behavioral anomalies and reasoning about root-causes. This paper proposes a method to develop digital twins for automated behavior analysis of large-scale distributed IoT systems. We present a real-life use-case of a smart office lighting system for which the method was successfully applied. The developed digital twin was used for anomaly detection and reasoning in a semi-automated root-cause analysis (RCA) approach.

[1]  He Zhang,et al.  Digital Twin in Industry: State-of-the-Art , 2019, IEEE Transactions on Industrial Informatics.

[2]  Edward H. Glaessgen,et al.  The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles , 2012 .

[3]  J. Christopher Westland,et al.  The cost of errors in software development: evidence from industry , 2002, J. Syst. Softw..

[4]  Linus Atorf,et al.  Closed-Loop Systems Engineering (CLOSE): Integrating Experimentable Digital Twins with the Model-Driven Engineering Process. , 2018, 2018 IEEE International Systems Engineering Symposium (ISSE).

[5]  Rainer Drath,et al.  AutomationML - the glue for seamless automation engineering , 2008, 2008 IEEE International Conference on Emerging Technologies and Factory Automation.

[6]  Marc Priggemeyer,et al.  Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0 , 2018, IEEE Transactions on Industrial Informatics.

[7]  Jozef Hooman,et al.  Pain-mitigation Techniques for Model-based Engineering using Domain-specific Languages , 2018, MODELSWARD.

[8]  Meng Zhang,et al.  Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing , 2017, IEEE Access.

[9]  Eric J. Tuegel,et al.  The Airframe Digital Twin: Some Challenges to Realization , 2012 .

[10]  Jacques Verriet,et al.  A Domain Model-Centric Approach for the Development of Large-Scale Office Lighting Systems , 2018, CSDM.

[11]  Jacques Verriet,et al.  Virtual Prototyping of Large-scale IoT Control Systems using Domain-specific Languages , 2019, MODELSWARD.

[12]  F. G. Rimini,et al.  Digital twin applications for the JET divertor , 2017 .

[13]  Antonella Molinaro,et al.  Multi-source data retrieval in IoT via named data networking , 2014, ICN '14.

[14]  Giancarlo Fortino,et al.  Modeling and Simulating Internet-of-Things Systems: A Hybrid Agent-Oriented Approach , 2017, Computing in Science & Engineering.

[15]  S. Michael Spottswood,et al.  Reengineering Aircraft Structural Life Prediction Using a Digital Twin , 2011 .

[16]  Thanassis Tiropanis,et al.  Analytics for the Internet of Things , 2018, ACM Comput. Surv..

[17]  Lida Xu,et al.  Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things , 2013, IEEE Transactions on Industrial Informatics.

[18]  M. Shamim Hossain,et al.  Narrowband Internet of Things: Simulation and Modeling , 2018, IEEE Internet of Things Journal.

[19]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[20]  Carlos Eduardo Pereira,et al.  Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange , 2016 .

[21]  Samira Moussaoui,et al.  A BLE-based data collection system for IoT , 2015, 2015 First International Conference on New Technologies of Information and Communication (NTIC).