The last few years have witnessed an explosion of research activity around the so-called Cyber-Physical Systems (CPS), conceived as architectures, protocols, standards, platforms, services and applications with a high level of integration and interaction of software and physical components. This broad definition embraces a myriad of technologies from different disciplines (such as aimed at bridging the gap between the physical and the digital worlds, moving at different temporal and spatial scales and with very diverse, yet complementary, capabilities in regard to ubiquity, interactivity, cognition, self-configurability, dynamicity, usability and adaptability. Several sectors have lately exploited the enormous benefits foreseen for CPS, from Energy (smart grids, energy efficiency for buildings) to Industry 4.0 (smart robotics, mechatronics) through Health (body nets, robot surgery), Operations Research (firefighting, disaster missions) or Transport (collision avoidance, driving efficiency), among many others. It is not in vain that the market potential predicted for CPS is huge, with billions of dollars worth revenues expected for sectors implementing these systems. As to mention, forecasts for the intelligent sensor market, a core part of the CPS technology spectrum, are foreseen to be a $10.5 billion industry in 2020 (source: Deloitte).

From a technical perspective, CPS can be regarded as a rich substrate, where many different technologies collide with no clear means for their implementation, integration, and coordination. The integration itself poses a very challenging scenario in what refers to the communication between components and functionalities coming from radically different disciplines (e.g., computer science and mechanical engineering), with differing programming languages, constraints and requirements. Technologies such as Virtual Reality, Internet of Things, Ultra-Reliable Low-Latency communications, Mechatronics, Embedded Systems or Data Analytics lie at the core of CPS, and are called to revolutionize all the aforementioned sectors, by enabling smart CPS capable of capturing information from their interaction with the physical environment and mining it towards an increased level of intelligence (e.g., better decision making or increased self-* capabilities).

However, several technological challenges stem from the particularly complex characteristics of CPS, far beyond the aforementioned integration of portfolios of diverse technologies. To begin with, overly involved are those barriers related to the inference of knowledge from the captured data, which can be implemented partly or fully at the sensors or, more usually, remotely queried upstream from the Cloud subject to the latency requirements of the application at hand. All in all, CPS span a plethora of paradigms in regards to its deployment, communication, organization, resource allocation, management, data collection, fusion, aggregation, analysis and human interaction, closely related to research trends such as Fog Computing, Tactile Internet, Data Science, Optimization and other critical enablers. Computational Intelligence (in particular, Machine Learning and Soft Computing techniques) are promising enablers of the intelligence and self-learning capability required for the CPS to succeed in tackling the above challenges.

Authors are invited to submit their original works focusing on how CPS can benefit from its synergy with Soft Computing techniques and Machine Learning models, with an emphasis on evidences of the practicability of the reported findings. Topics of interest for this special issue include, but are not limited to, the following:

- Novel Soft Computing techniques and their application to problems related to CPS, such as distributed predictive modelling, hybrid optimization techniques, online learning over data streams, concept drift adaptation, automated model construction, large-scale deployment of Soft Computing techniques, collaborative reasoning and weakly/semi-supervised learning, among others
- Data analytics and scalable/parallel/distributed computing algorithms for CPS
- Artificial Intelligence (AI) as a service (AIaaS) for CPS
- Energy efficiency paradigms for CPS tackled via Soft Computing and Machine Learning
- Distributed computing, data fusion and aggregation over large-scale CPS
- Predictive and clustering models for CPS self-configuration, self-resilience and self-autonomy
- Optimization algorithms for optimal sensor actuation- Autonomic computing, inference of human patterns, analysis, monitoring, and situation alertness in CPS
- Federated learning, collaborative machine learning and distributed AI for large scale CPS
- Soft Computing techniques to enable ultra-reliable, low-latency applications in CPS scenarios.