This new blog post and the upcoming ones are focused on revisiting some of the hot technological topics addressed in the last two years where more than 10,000 of frequent readers are encouraged us to go on. Let us, begin revisiting Cyber-Physical Systems (CPS) and Internet of Things (IoT). Last decades have demonstrated complementarities because both paradigms aim at integrating digital capabilities, including connectivity with physical devices and systems. Moreover, CPS and IoT include interacting logical, physical, and human components by integrating logic and physics. However, there are some differences. IoT makes more emphasis on connecting “things” towards connecting “everything” whereas CPS put more attention on integrating computation, networking and physical systems. CPS and IoT are cross-cutting human-in-the-loop technologies covering a variety of all domains .
CPS are recognized as a top-priority in research and development. Although approaches in software engineering (SE) and control engineering (CE) exist that individually meet these demands, their synergy to address the challenges of smart CPS in a holistic manner remains an open challenge towards a Cyber-Physical System of Systems, the new paradigm.
Among hot topics in CPS and IoT such as sensors and actuators , hardware designs and development platforms, architectures and computational frameworks , modeling , control  and optimization , and potential applications, let’s focus architectures and computational frameworks.
CPS and IoT are becoming large-scale pervasive systems, which combine various data sources to control real-world ecosystems such as intelligent traffic control, smart production systems, smart buildings, urban water management, precision agriculture, among others. IoT and CPS have to control emergent behavior, be scalable and fault tolerant. In this post, some impacts on the basic science panorama, potential applications and key challenging areas are analyzed focusing on computational architectures and frameworks.
What is the current trend? What is happening in relation to architectures and computational frameworks for IoT and CPS?
The research on CPS and IoT architectures and computational framework has already produced significant impact, which may be analyzed taking into account different layers: 1) application layer; 2) network layer; and 3) perception layer. The network layer is the most important layer in IoT architecture, because various devices (e.g., hub, switching, gateway, cloud computing perform, etc.), and various communication technologies are integrated in this layer. In parallel, educational needs are growing in two learning paths: communications standards and computing platforms .
Special attention has received cloud, fog and edge computing, even being not completely new topic it deserves tailored post in the upcoming weeks . Nowadays, the integration between cloud computing and the IoT and CPS allows resource-constrained IoT devices to offload data and complex computation onto the cloud, taking advantage of its computational and storage capacity. However, the centralized nature of a cloud can lead to a considerable topological distance between cloud computing re- sources/services and the vast majority of end (user) devices, limiting the deployment of some cloud-based solutions. On the contrary, Fog computing is an evolution of early proposals with the objective to afford the needs of the IoT and CPS, therefore cloud and fog computing will still remain essential for IoT and CPS in the upcoming years.
In order to adopt Cyber-Physical System of Systems (CPSoS) paradigm, new approaches on the integration in several digital functionalities in a cloud-based platform have been yielded. New research results will enable real time multiple devices interaction, data analytic and global reconfiguration to increase the management and optimization capabilities for increasing the quality of facility services, safety, energy efficiency and productivity.
Smart manufacturing and emerging cyber-production systems are the natural recipient to adopt CPSoS paradigm and related technologies . Industry 4.0 is evolving towards more automated data exchange beyond manufacturing technologies by unifying technologies of the third industrial revolution (automation processes and new production technologies) with new technologies such as storage, processing and massive data transmission. Solving this challenge will have implications of great impact on robotics, computer science, mathematics and engineering, but also in health and social sciences, as they will change the way we relate to the factory of the future .
Research and development challenges of digital transformation in the next decades require new paradigms combining control engineering, information software and telecommunication engineering . Nowadays the top-10 topics targeted world-wide are: signal processing and estimation, computational models, architectures and design approaches, interfacing the computational and physical world, dependability, synergy between monitoring and adaptation techniques, distributed algorithms, monitoring and control, handling emergent behavior and uncertainty, modeling and simulation, scalability and evolvability.
Software platforms with well-defined and appropriate levels of abstractions and architecture are essential for the development of reliable, scalable, and evolvable CPS in various application domains. Methods and theories for high-level decision-making based on information collected from different sources at different spatial and temporal scales are necessary for system-wide reliability, efficiency, security, robustness, and autonomy of CPS. Just to finish this blog post, and beyond purely industry-related, a very interesting analysis of how electrical vehicles are increasingly CPS , in a very interesting and recommended article of Robert N. Charette in IEEE Spectrum, therefore… Les systèmes cyber-physiques sommes mort, vive les systèmes cyber-physiques!
In future posts, we will be continue writing about technology and business trends for enterprises. The objective of this blog is to provide a personal vision of how digital transformation trends will be impacting in our daily activities, businesses and lifestyle.
 C. Greer, M. Burns, D. Wollman, and E. Griffor, “Cyber-physical systems and internet of things,” 2019. https://www.nist.gov/publications/cyber-physical-systems-and-internet-things (accessed Sep. 27, 2022).
 F. Castaño, G. Beruvides, A. Villalonga, and R. E. Haber, “Self-tuning method for increased obstacle detection reliability based on internet of things LiDAR sensor models,” Sensors (Switzerland), vol. 18, no. 5, 2018, doi: https://doi.org/10.3390/s18051508.
 A. Villalonga, G. Beruvides, F. Castano, and R. E. Haber, “Cloud-Based Industrial Cyber-Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line,” IEEE Trans Industr Inform, vol. 16, no. 9, 2020, doi: https://doi.org/10.1109/TII.2020.2971057.
 A. Villalonga et al., “A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins,” Annu Rev Control, vol. 51, pp. 357–373, Jan. 2021, doi: https://doi.org/10.1016/J.ARCONTROL.2021.04.008.
 A. Artuñedo, R. M. del Toro, and R. E. Haber, “Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks,” Sensors, vol. 17, no. 5, p. 953, Apr. 2017, doi: https://doi.org/10.3390/s17050953.
 R. H. Guerra, R. Quiza, A. Villalonga, J. Arenas, and F. Castaño, “Digital Twin-Based Optimization for Ultraprecision Motion Systems With Backlash and Friction,” IEEE Access, vol. 7, pp. 93462–93472, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2928141.
 J. Perez, “Learn In-Demand IoT Concepts From New IEEE Academy The training covers communications standards and computing platforms,” 2022. https://spectrum.ieee.org/ieee-iot-academy (accessed Sep. 27, 2022).
 M. Bursell, “Trust, the Cloud, and the Edge,” 2022, doi: https://doi.org/10.1002/9781119695158.ch10.
 R. Harrison, D. A. Vera, and B. Ahmad, “A Connective Framework to Support the Lifecycle of Cyber–Physical Production Systems,” Proceedings of the IEEE, vol. 109, no. 4, pp. 568–581, 2021, doi: https://doi.org/10.1109/JPROC.2020.3046525.
 B. Vogel-Heuser, E. Trunzer, D. Hujo, and M. Sollfrank, “(Re)deployment of Smart Algorithms in Cyber–Physical Production Systems Using DSL4hDNCS,” Proceedings of the IEEE, vol. 109, no. 4, pp. 542–555, 2021, doi: https://doi.org/10.1109/JPROC.2021.3050860.
 A. Sánchez Boza, R. H. Guerra, and A. Gajate, “Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach,” Eng Appl Artif Intell, vol. 24, no. 2, pp. 209–219, Mar. 2011, doi: https://doi.org/10.1016/J.ENGAPPAI.2010.10.005.
 R. N. Charette, “As Electric Car Makers Ante Up Billions, Software Is Ace in the Hole Coders set to cash in,” 2021. https://spectrum.ieee.org/electric-cars#toggle-gdpr (accessed Sep. 27, 2022).
Director Centre of Automation and Robotics, Madrid, Spain