K. Bellman
N. Dutt
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Abstract
In this article, we make the case for the new class of Self-aware Cyber-physical Systems. By bringing together the two established fields of cyber-physical systems and self-aware computing, we aim at creating systems with strongly increased yet managed autonomy, which is a main requirement for many emerging and future applications and technologies. Self-aware cyber-physical systems are situated in a physical environment and constrained in their resources, and they understand their own state and environment and, based on that understanding, are able to make decisions autonomously at runtime in a self-explanatory way. In an attempt to lay out a research agenda, we bring up and elaborate on five key challenges for future self-aware cyber-physical systems: (i) How can we build resource-sensitive yet self-aware systems? (ii) How to acknowledge situatedness and subjectivity? (iii) What are effective infrastructures for implementing self-awareness processes? (iv) How can we verify self-aware cyber-physical systems and, in particular, which guarantees can we give? (v) What novel development processes will be required to engineer self-aware cyber-physical systems? We review each of these challenges in some detail and emphasize that addressing all of them requires the system to make a comprehensive assessment of the situation and a continual introspection of its own state to sensibly balance diverse requirements, constraints, short-term and long-term objectives. Throughout, we draw on three examples of cyber-physical systems that may benefit from self-awareness: a multi-processor system-on-chip, a Mars rover, and an implanted insulin pump. These three very different systems nevertheless have similar characteristics: limited resources, complex unforeseeable environmental dynamics, high expectations on their reliability, and substantial levels of risk associated with malfunctioning. Using these examples, we discuss the potential role of self-awareness in both highly complex and rather more simple systems, and as a main conclusion we highlight the need for research on above listed topics.
- E. A. Lee. 2007. Computing Foundations and Practice for Cyber Physical Systems: A Preliminary Report. Technical Report. Technical Report No. UCB/EECS-2007-72.Google Scholar
- Jean-Michel Bergé, Oz Levia, and Jacques (editors) Rouillard. 1995. High-level System Modeling: Specification Languages. Springer.Google Scholar
- H. Kopetz. 1997. Real-Time Systems: Design Principles for Distributed Embedded Applications. Springer.Google ScholarDigital Library
- Peter R. Lewis, Marco Platzner, Bernhard Rinner, Jim Torresen, and Xin Yao (Eds.). 2016. Self-aware Computing Systems: An Engineering Approach. Springer.Google Scholar
- Samuel Kounev, Jeffrey O. Kephart, Aleksandar Milenkowski, and Xiaoyun Zhu (Eds.). 2017. Self-aware Computing Systems. Springer.Google Scholar
- Steve Chien and Kiri L. Wagstaff. 2017. Robotic space exploration agents. Sci. Robot. 2, 7 (2017).Google Scholar
- Anna Nowogrodzki. 2016. How does Mars rover Curiosity’s new AI system work? Retrieved August 2016 from http://www.astronomy.com/news/2016/08/how-does-mars-rover-curiositys-new-ai-system-work.Google Scholar
- W. Burleson, S. S. Clark, B. Ransford, and K. Fu. 2012. Design challenges for secure implantable medical devices. In Proceedings of the Design Automation Conference (DAC’12). 12--17.Google Scholar
- Benjamin Ransford, Daniel B. Kramer, Denis Foo Kune, Julio Auto de Medeiros, Chen Yan, Wenyuan Xu, Thomas Crawford, and Kevin Fu. Cybersecurity and medical devices: A practical guide for cardiac electrophysiologists. Pacing Clin. Electrophysiol. 40, 8, 913--917.Google Scholar
- D. Halperin, T. S. Heydt-Benjamin, K. Fu, T. Kohno, and W. H. Maisel. 2008. Security and privacy for implantable medical devices. IEEE Perv. Comput. 7, 1 (Jan. 2008), 30--39.Google ScholarDigital Library
- Sarbari Gupta. 2012. Implantable medical devices -- Cyber risks and mitigation approaches. In Proceedings of the NIST Cyber Physical Systems Workshop.Google Scholar
- Christopher Landauer and Kirstie L. Bellman. 2000. Reflective infrastructure for autonomous systems. In Proceedings of the 15th European Meeting on Cybernetics and Systems Research (EMCSR’00). 671--676.Google Scholar
- M. T. Cox. 2005. Metacognition in computation: A selected research review. Art. Int. 169, 2 (2005), 104--141.Google ScholarDigital Library
- Peter R. Lewis. 2017. Self-aware computing systems: From psychology to engineering. In Proceedings of the 2017 Design, Automation 8 Test in Europe Conference 8 Exhibition (DATE’17). 1044--1049.Google ScholarCross Ref
- Peter R. Lewis, Arjun Chandra, Funmilade Faniyi, Kyrre Glette, Tao Chen, Rami Bahsoon, Jim Torresen, and Xin Yao. 2015. Architectural aspects of self-aware and self-expressive computing systems. IEEE Comput. 48, 8 (2015), 62--70.Google ScholarDigital Library
- J. S. Preden, K. Tammemäe, A. Jantsch, M. Leier, A. Riid, and E. Calis. 2015. The benefits of self-awareness and attention in fog and mist computing. Computer 48, 7 (2015), 37--45.Google ScholarDigital Library
- Peter Lewis, Kirstie Bellman, Chris Landauer, Lukas Esterle, Kyrre Glette, Ada Diaconescu, and Holger Giese. 2017. Towards a framework for the levels and aspects of self-aware computing systems. In Self-Aware Computing Systems, Samuel Kounev, Jeffrey O. Kephart, Aleksandar Milenkoski, and Xiaoyun Zhu (Eds.). Springer, 51--85.Google Scholar
- H. Hoffmann et al. 2013. A generalized software framework for accurate and efficient management of performance goals. In Proceedings of the International Conference on Embedded Software. 1--10.Google Scholar
- IBM Corporation. 2006. An Architectural Blueprint for Autonomic Computing. IBM White Paper.Google Scholar
- Samuel Kounev, Peter Lewis, Kirstie Bellman, Nelly Bencomo, Javier Camara, Ada Diaconescu, Lukas Esterle, Kurt Geihs, Holger Giese, Sebastian Götz, Paola Inverardi, Jeffrey Kephart, and Andrea Zisman. 2017. The notion of self-aware computing. In Self-Aware Computing Systems, Samuel Kounev, Jeffrey O. Kephart, Aleksandar Milenkoski, and Xiaoyun Zhu (Eds.). Springer, 3--16.Google Scholar
- L. D. Paulson. 2003. DARPA creating self-aware computing. IEEE Comput/ 36, 3 (Mar. 2003), 24.Google Scholar
- European Commission. 2013. Self-Awareness in Autonomic Systems.Google Scholar
- Jeremy Pitt (Ed.). 2014. The Computer After Me. Imperial College Press/World Scientific Book.Google Scholar
- Peter R. Lewis, Arjun Chandra, Shaun Parsons, Edward Robinson, Kyrre Glette, Rami Bahsoon, Jim Torresen, and Xin Yao. 2011. A survey of self-awareness and its application in computing systems. In Proceedings of the International Conference on Self-Adaptive and Self-Organizing Systems Workshops (SASOW’11). IEEE Computer Society, 102--107.Google ScholarDigital Library
- J. Schaumeier, J. Jeremy Pitt, and G. Cabri. 2012. A tripartite analytic framework for characterising awareness and self-awareness in autonomic systems research. In Proceedings of the 6th IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops (SASOW’12). IEEE Computer Society, 157--162.Google Scholar
- Axel Jantsch, Nikil Dutt, and Amir M. Rahmani. 2017. Self-awareness in systems on chip—A survey. IEEE Des. Test 34, 6 (2017), 8--26.Google ScholarCross Ref
- Tao Chen, Rami Bahsoon, and Xin Yao. 2018. A survey and taxonomy of self-aware and self-adaptive cloud autoscaling systems. Comput. Surv. 51, 06 (2018).Google Scholar
- Lukas Esterle and Radu Grosu. 2016. Cyber-physical systems: Challenge of the 21st century. Elektrotech. Informationstech. 133, 7 (01 Nov 2016), 299--303.Google Scholar
- Aamir Akbar and Peter R. Lewis. 2018. Self-adaptive and self-aware mobile-cloud hybrid robotics. In Proceedings of the 4th International Workshop on Mobile Cloud Computing systems, Management, and Security (MCSMS’18). To appear.Google Scholar
- Andreas Agne, Markus Happe, Achim Lösch, Christian Plessl, and Marco Platzner. 2014. Self-awareness as a model for designing and operating heterogeneous multicores. ACM Trans. Reconfig. Technol. Syst. 7, 2 (Jul. 2014), 13:1--13:18.Google ScholarDigital Library
- Ariane Keller, Daniel Borkmann, Stephan Neuhaus, and Markus Happe. 2014. Self-awareness in computer networks. Int. J. Reconfig. Comput. 2014, Article 10 (Jan. 2014), 1 pages.Google ScholarDigital Library
- Bernhard Rinner, Lukas Esterle, Jennifer Simonjan, Georg Nebehay, Roman Pflugfelder, Peter R. Lewis, and Gustavo Fernandez Dominguez. 2015. Self-aware and self-expressive camera networks. IEEE Comput. 48, 7 (2015), 21--28.Google ScholarDigital Library
- M. Götzinger, N. TaheriNejad, H. A. Kholerdi, A. Jantsch, E. Willegger, T. Glatzl, A. M. Rahmani, T. Sauter, and P. Liljeberg. 2019. Model-free monitoring with confidence. Int. J. Comput. Integr. Manufact. 32, 4–5 (2019), 466--481. DOI:http://dx.doi.org/10.1080/0951192X.2019.1605201 arXiv:https://doi.org/10.1080/0951192X.2019.1605201Google ScholarCross Ref
- Maciej Kurek, Tobias Becker, Ce Guo, Stewart Denholm, Andreea-Ingrid Funie, Mark Salmon, Tim Todman, and Wayne Luk. 2016. Self-aware hardware acceleration of financial applications on a heterogeneous cluster. In Self-aware Computing Systems: An Engineering Approach. Springer.Google Scholar
- M. Götzinger, A. Anzanpour, I. Azimi, N. TaheriNejad, A. Jantsch, A. Rahmani, and P. Liljeberg. 2019. Confidence-enhanced early warning score based on fuzzy logic. ACM/Springer Mobile Networks and Applications (August 2019), 1--18. DOI:http://dx.doi.org/10.1007/s11036-019-01324-5Google Scholar
- K. Nymoen, A. Chandra, and J. Torresen. 2016. Self-awareness in active music systems. In Self-aware Computing Systems: An Engineering Approach. Springer.Google Scholar
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS’12), Vol. 1. Curran Associates, 1097--1105.Google ScholarDigital Library
- K. Neshatpour, F. Behnia, H. Homayoun, and A. Sasan. 2018. ICNN: An iterative implementation of convolutional neural networks to enable energy and computational complexity aware dynamic approximation. In Proceedings of the 2018 Design, Automation Test in Europe Conference Exhibition (DATE’18). 551--556.Google Scholar
- Farnaz Forooghifar, Amir Aminifar, and David Atienza Alonso. 2018. Self-aware wearable systems in epileptic seizure detection. In 21st Euromicro Conference on Digital System Design (DSD'18). IEEE, 426--432.Google ScholarCross Ref
- H. A. Kholerdi, N. TaheriNejad, and A. Jantsch. 2018. Enhancement of classification of small data sets using self-awareness; an iris flower case-study. In Proceedings of the 2018 IEEE International Symposium on Circuits and Systems (ISCAS’18). 1--5.Google Scholar
- N. TaheriNejad and A. Jantsch. 2019. Improved machine learning using confidence. In Proceedings of the 2019 IEEE 32nd Canadian Conference on Electrical and Computer Engineering (CCECE’19). 1--5.Google Scholar
- N. TaheriNejad, A. Jantsch, and D. Pollreisz. 2016. Comprehensive observation and its role in self-awareness; an emotion recognition system example. In Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS’16).Google Scholar
- N. TaheriNejad, M. A. Shami, and S. M. P. D. 2017. Self-aware sensing and attention-based data collection in multi-processor system-on-chips. In Proceedings of the 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS’17). 81--84.Google ScholarCross Ref
- Arman Anzanpour, Iman Azimi, Maximilian Götzinger, Amir M. Rahmani, Nima TaheriNejad, Pasi Liljeberg, Axel Jantsch, and Nikil Dutt. 2017. Self-awareness in remote health monitoring systems using wearable electronics. In Proceedings of the Design and Test Europe Conference (DATE’17).Google ScholarCross Ref
- Sparsh Mittal. 2016. A survey of techniques for approximate computing. Comput. Surv. 48, 4 (Mar. 2016).Google ScholarDigital Library
- Qiang Xu, Todd Mytkowicz, and Nam Sung Kim. 2016. Approximate computing: A survey. IEEE Des. Test 33, 1 (2016), 8--22.Google ScholarCross Ref
- Armin Alaghi and John P. Hayes. 2013. Survey of stochastic computing. ACM Trans. Embed. Comput. Syst. 12, 2 (May 2013).Google ScholarDigital Library
- E. P. Kim and N. R. Shanbhag. 2014. Energy-efficient accelerator architecture for stereo image matching using approximate computing and statistical error compensation. In Proceedings of the IEEE Global Conference Signal and Information Processing (GlobalSIP’14).Google Scholar
- Fei Qiao, Ni Zhou, Yuanchang Chen, and Huazhong Yang. 2015. Approximate computing in chrominance cache for image/video processing. In Proceedings of the IEEE International Conference on Multimedia Big Data. 180--183.Google ScholarDigital Library
- Zidong Du, Robert Fasthuber, Tianshi Chen, Paolo Ienne, Ling Li, Xiaobing Feng, Yunji Chen, and Olivier Temam. 2015. ShiDianNao: Shifting vision processing closer to the sensor. In Proceedings of IEEE/ACM International Symposium on Computer Architecture.Google ScholarDigital Library
- Jiachao Deng, Yuntan Fang, Zidong Du, Ying Wang, and Huawei L. 2015. Retraining-based timing error mitigation for hardware neural networks. In Proceedings of the Conference on Design, Automation and Test in Europe. 593--596.Google Scholar
- N. Taherinejad, L. Lampe, and S. Mirabbasi. 2014. Adaptive impedance matching for vehicular power line communication systems. In Proceedings of the 18th IEEE International Symposium on Power Line Communications and Its Applications. 214--219.Google Scholar
- Z. Sheng, A. Kenarsari-Anhari, N. Taherinejad, and V. C. M. Leung. 2016. A multichannel medium access control protocol for vehicular power line communication systems. IEEE Trans. Vehic. Technol. 65, 2 (Feb. 2016), 542--554.Google ScholarCross Ref
- N. Taherinejad, L. Lampe, and S. Mirabbasi. 2017. An adaptive impedance-matching system for vehicular power line communication. IEEE Trans. Vehic. Technol. 66, 2 (Feb. 2017), 927--940.Google ScholarCross Ref
- E. Shamsa, A. Kanduri, N. Taherinejad, A. Proebstl, S. Chakraborty, A. M. Rahmani, and P. Liljeberg. 2020. User-centric resource management for embedded multi-core processors. In Proceedings of 33rd IEEE Internation Conference on VLSI Design and Embedded Systems (VLSID’20). 1--6.Google Scholar
- Tao Chen, Funmilade Faniyi, Rami Bahsoon, Peter R. Lewis, Xin Yao, Leandro L. Minku, and Lukas Esterle. 2014. The handbook of engineering self-aware and self-expressive systems. CoRR abs/1409.1793 (2014).Google Scholar
- Betty H. C. Cheng, Rogério de Lemos, Holger Giese, Paola Inverardi, Jeff Magee, Jesper Andersson, Basil Becker, Nelly Bencomo, Yuriy Brun, Bojan Cukic, Giovanna Di Marzo Serugendo, Schahram Dustdar, Anthony Finkelstein, Cristina Gacek, Kurt Geihs, Vincenzo Grassi, Gabor Karsai, Holger M. Kienle, Jeff Kramer, Marin Litoiu, Sam Malek, Raffaela Mirandola, Hausi Müller, Sooyong Park, Mary Shaw, Matthias Tichy, Massimo Tivoli, Danny Weyns, and Jon Whittle. 2009. Software engineering for self-adaptive systems: A research roadmap. In Software Engineering for Self-Adaptive Systems, Betty H. C. Cheng, Rogério de Lemos, Holger Giese, Paola Inverardi, and Jeff Magee (Eds.). Lecture Notes in Computer Science (LNCS), Vol. 5525. Springer, 1--26.Google Scholar
- Shelley Duval and Robert A. Wicklund. 1972. A Theory of Objective Self Awareness. Academic Press.Google Scholar
- Lukas Esterle, Peter R. Lewis, Richie McBride, and Xin Yao. 2017. The future of camera networks: Staying smart in a chaotic world. In Proceedings of the 11th International Conference on Distributed Smart Cameras (ICDSC’17). ACM, New York, NY, 163--168.Google ScholarDigital Library
- Maximilian Götzinger, Arman Azanpour, Iman Azimi, Nima Taherinejad, and Amir M Rahmani. 2017. Enhancing the self-aware early warning score system through fuzzified data reliability assessment. In Proceedings of the International Conference on Wireless Mobile Communication and Healthcare. Springer.Google ScholarCross Ref
- Anthony Stein, Sven Tomforde, Ada Diaconescu, Jörg Hähner, and Christian Müller-Schloer. 2018. A concept for proactive knowledge construction in self-learning autonomous systems. In Proceedings of the 2018 IEEE 3rd International Workshops on Foundations and Applications of Self⋆ Systems (FAS⋆W’18).Google ScholarCross Ref
- George Orwell. 1949. Nineteen Eighty-Four. A Novel.Secker 8 Warburg, London.Google Scholar
- Peter R. Lewis, Lukas Esterle, Arjun Chandra, Bernhard Rinner, and Xin Yao. 2013. Learning to be different: Heterogeneity and efficiency in distributed smart camera networks. In Proceedings of the 7th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO’13). IEEE Press, 209--218.Google ScholarDigital Library
- Peter R. Lewis, Harry Goldingay, and Vivek Nallur. 2014. It’s good to be different: Diversity, heterogeneity and dynamics in collective systems. In Proceedings of the 8th IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops (SASO’14). IEEE Computer Society Press, 84--89.Google ScholarDigital Library
- Yong Liu and Xin Yao. 1999. Ensemble learning via negative correlation. Neur. Netw. 12, 10 (1999), 1399--1404.Google ScholarDigital Library
- Gavin Brown, Jeremy Wyatt, Rachel Harris, and Xin Yao. 2005. Diversity creation methods: A survey and categorisation. Inf. Fus. 6, 1 (2005), 5--20.Google ScholarCross Ref
- Georg Nebehay, Walter Chibamu, Peter R. Lewis, Arjun Chandra, Roman Pflugfelder, and Xin Yao. 2013. Can diversity amongst learners improve online object tracking? In Multiple Classifier Systems. Springer, Berlin, 212--223.Google Scholar
- Harry Goldingay and Peter R. Lewis. 2014. A taxonomy of heterogeneity and dynamics in particle swarm optimisation. In Parallel Problem Solving from Nature--PPSN XIII,Lecture Notes in Computer Science, Vol. 8672. Springer, 171--180.Google Scholar
- Edwin Hutchins. 1995. Cognition in the Wild. MIT Press.Google Scholar
- Pattie Maes. 1987. Concepts and experiments in computational reflection. In Proceedings of the Conference Proceedings on Object-oriented Programming Systems, Languages and Applications (OOPSLA’87). ACM, New York, NY, 147--155.Google ScholarDigital Library
- C. Landauer and K. L. Bellman. 1999. Generic programming, partial evaluation, and a new programming paradigm. In Software Process Improvement,Gene McGuire (ed.). Idea Group Publishing, 108--154.Google Scholar
- C. Landauer. 2017. Mitigating the inevitable failure of knowledge representation. In Proceedings of the 2017 IEEE International Conference on Autonomic Computing (ICAC’17). 239--246.Google ScholarCross Ref
- C. Landauer and K. L. Bellman. 2017. Integration by negotiated behavior restrictions. In Proceedings of the 2017 IEEE 2nd International Workshops on Foundations and Applications of Self⋆ Systems (FAS⋆W’17). 117--121.Google Scholar
- David G. Ullman. 2017. OO-OO-OO!” the sound of a broken OODA loop. CrossTalk-The Journal of Defense Software Engineering (2007), 22--25.Google Scholar
- Yuriy Brun, Giovanna Di Marzo Serugendo, Cristina Gacek, Holger Giese, Holger Kienle, Marin Litoiu, Hausi Müller, Mauro Pezzè, and Mary Shaw. 2009. Engineering Self-Adaptive Systems through Feedback Loops. Springer, Berlin, 48--70.Google ScholarDigital Library
- Danny Weyns, Sam Malek, and Jesper Andersson. 2010. On decentralized self-adaptation: Lessons from the trenches and challenges for the future. In Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems (SEAMS’10). ACM, New York, NY, 84--93.Google ScholarDigital Library
- Yuriy Brun, Ron Desmarais, Kurt Geihs, Marin Litoiu, Antonia Lopes, Mary Shaw, and Michael Smit. 2013. A Design Space for Self-Adaptive Systems. Springer, Berlin, 33--50.Google Scholar
- Christopher Landauer and Kirstie L. Bellman. 2003. Managing self-modeling systems. In Proceedings of the 3rd International Workshop on Self-Adaptive Software, R. Laddaga and H. Shrobe (Eds.).Google Scholar
- Marta Kwiatkowska, Gethin Norman, and David Parker. 2007. Stochastic Model Checking. Springer, Berlin, 220--270.Google Scholar
- Scott D. Stoller, Ezio Bartocci, Justin Seyster, Radu Grosu, Klaus Havelund, Scott A. Smolka, and Erez Zadok. 2012. Runtime verification with state estimation. In Runtime Verification, Sarfraz Khurshid and Koushik Sen (Eds.). Springer, Berlin, 193--207.Google Scholar
- Ezio Bartocci, Radu Grosu, Atul Karmarkar, Scott A. Smolka, Scott D. Stoller, Erez Zadok, and Justin Seyster. 2013. Adaptive runtime verification. In Runtime Verification, Shaz Qadeer and Serdar Tasiran (Eds.). Springer, Berlin, 168--182.Google Scholar
- Denise Ratasich, Faiq Khalid, Florian Geissler, Radu Grosu, Muhammad Shafique, and Ezio Bartocci. 2019. A Roadmap toward the resilient internet of things for cyber-physical systems. In IEEE Access, vol. 7. 13260--13283. DOI:10.1109/ACCESS.2019.2891969Google ScholarCross Ref
- Lukas Esterle, Kirstie L. Bellman, Steffen Becker, Anne Koziolek, Christopher Landauer, and Peter Lewis. 2017. Assessing Self-awareness. Springer International Publishing, Cham, 465--481.Google Scholar
- Nikil Dutt, Fadi J. Kurdahi, Rolf Ernst, and Andreas Herkersdorf. 2016. Conquering MPSoC complexity with principles of a self-aware information processing factory. In Proceedings of the 11th IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis. ACM, 37.Google ScholarDigital Library
- Kirstie L. Bellman. 2018. What reasonable guarantees can we make for a SISSY system. In Proceedings of the 2018 IEEE 3rd International Workshops on Foundations and Applications of Self⋆ Systems (FAS⋆W’18).Google ScholarCross Ref
- US Food and Drug Administration. 2017. Cybersecurity Vulnerabilities Identified in St. Jude Medical’s Implantable Cardiac Devices and Merlin@home Transmitter: FDA Safety Communication. Retrieved December 5, 2019 from https://www.fda.gov/medical-devices/safety-communications/cybersecurity-vulnerabilities-identified-st-jude-medicals-implantable-cardiac-devices-and-merlinhome accessed: 2019-12-05.Google Scholar
- US Food and Drug Administration. 2019. Cybersecurity Vulnerabilities Affecting Medtronic Implantable Cardiac Devices, Programmers, and Home Monitors: FDA Safety Communication. Retrieved December 5, 2019 from https://www.fda.gov/medical-devices/safety-communications/cybersecurity-vulnerabilities-affecting-medtronic-implantable-cardiac-devices-programmers-and-home.Google Scholar
Index Terms
- Self-aware Cyber-Physical Systems
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