Recent Developments in Embedded System  Modelling: An Extensive Examination

Himanshu Sharma

Himanshu Sharma Student, Birsa Agricultural University, Ranchi, Jharkhand.

Abstract

Embedded systems play a crucial role in various domains, from consumer electronics to critical infrastructure. As the complexity of these systems continues to increase, efficient modeling techniques become paramount for their design, analysis, and verification. This review article surveys recent advancements in embedded system modeling methodologies, tools, and emerging trends. It explores traditional approaches such as Finite State Machines and Petri Nets, alongside modern paradigms like Model-Based Development (MBD), formal methods, virtual prototyping, and the integration of machine learning and artificial intelligence. Additionally, it discusses challenges and future directions in embedded system modeling, highlighting the need for addressing system complexity, security concerns, and the growing influence of Cyber-Physical Systems (CPS) and the Internet of Things (IoT). This review aims to provide a comprehensive understanding of the current state-of-the-art in embedded system modeling and to guide future research in this dynamic field.

References

1. Deniziak S, Tomaszewski R. Co-synthesis of contentionfree energy-efficient NOC-based real time embedded
systems. Journal of Systems Architecture. 2019 Sep 1;98:92-101.
2. Mrabet F, Karamti W, Mahfoudhi A. Scheduling analysis and correction for dependent real-time tasks upon heterogeneous multiprocessor architectures. Computing. 2024 Mar;106(3):651-712.
3. Claasen TA. An industry perspective on current and future state of the art in system-on-chip (SoC) technology. Proceedings of the IEEE. 2006 Jun;94(6):1121-37.
4. Garousi V, Felderer M, Karapıçak ÇM, Yılmaz U. Testing embedded software: A survey of the literature.
Information and Software Technology. 2018 Dec 1;104:14-45.
5. Chen H, Zhu X, Guo H, Zhu J, Qin X, Wu J. Towards energy-efficient scheduling for real-time tasks under
uncertain cloud computing environment. Journal of Systems and Software. 2015 Jan 1;99:20-35.
6. Audsley, Neil, and Sanjoy Baruah. “Real-Time Systems: the past, the present, and the future.” (2013).
7. Molina RS, Gil-Costa V, Crespo ML, Ramponi G. High-level synthesis hardware design for fpga-based
accelerators: Models, methodologies, and frameworks. IEEE Access. 2022 Aug 23;10:90429-55.
8. Awari GK, Kumbhar VS, Tirpude RB. Automotive systems: principles and practice. CRC Press; 2021 Jan 26.
9. Barbosa JL. Ubiquitous computing: Applications and research opportunities. In2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) 2015 Dec 10 (pp. 1-8). IEEE.
10. Jeon D, Henry MB, Kim Y, Lee I, Zhang Z, Blaauw D, Sylvester D. An energy efficient full-frame feature extraction accelerator with shift-latch FIFO in 28 nm CMOS. IEEE Journal of Solid-State Circuits. 2014 Mar 11;49(5):1271-84.
11. Buttazzo GC. Hard real-time computing systems: predictable scheduling algorithms and applications. Springer Science & Business Media; 2011 Sep 10.
12. Soubervielle-Montalvo C, Perez-Cham OE, Puente C, Gonzalez-Galvan EJ, Olague G, Aguirre-Salado CA, Cuevas-Tello JC, Ontanon-Garcia LJ. Design of a lowpower embedded system based on a SoC-FPGA and the honeybee search algorithm for real-time video tracking. Sensors. 2022 Feb 8;22(3):1280.
13. Stankovic JA. Misconceptions about real-time computing: A serious problem for next-generation systems. Computer. 1988 Oct;21(10):10-9.14. Mitra T, editor. Reimagining ACM Transactions on Embedded Computing Systems (TECS). ACM Transactions on Embedded Computing Systems (TECS). 2021 Apr 23;20(3):1-3.
15. Simunic T, Benini L, Glynn P, De Micheli G. Dynamic power management for portable systems. InProceedings
of the 6th annual international conference on Mobile computing and networking 2000 Aug 1 (pp. 11-19).
16. Lee EA, Seshia SA. Introduction to embedded systems: A cyber-physical systems approach. MIT press; 2016 Dec 30.
17. González CA, Varmazyar M, Nejati S, Briand LC, Isasi Y. Enabling model testing of cyber-physical systems.
InProceedings of the 21th ACM/IEEE international conference on model driven engineering languages and systems 2018 Oct 14 (pp. 176-186).
18. Deshmukh JV, Sankaranarayanan S. Formal techniques for verification and testing of cyber-physical systems.
Design Automation of Cyber-Physical Systems. 2019:69-105.
19. Delgado-Santos P, Stragapede G, Tolosana R, Guest R, Deravi F, Vera-Rodriguez R. A survey of privacy
vulnerabilities of mobile device sensors. ACM Computing Surveys (CSUR). 2022 Sep 10;54(11s):1-30.
20. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015 May 28;521(7553):436-44.
21. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. InProceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining 2016 Aug 13 (pp. 785-794).