# Innovation Potentials and Pathways Merging AI, CPS, and IoT
## 2 State of the Art and Recent Advances  
### Production  
Cyber-Physical Systems (CPS) have emerged along the connected Industry 4.0 concept in production as recent technological advances. CPS is characterized by the integration of information and data technologies as well as communication (\"cyber\") with physical and real-world engineering processes and tasks, see e.g., [17; 18]. Recent challenges include security questions [19; 20; 21; 22; 23] as well as complexity handling, including the notion to reduce complexity within the models used to simulate CPS systems [24].  
The implications include many crucial industry and manufacturing as well as society infrastructure nodes and systems e.g., in the energy, health or communications sectors [25]. This also hints at the central role of interconnections and links between such sectors such as e.g., manufacturing and transportation, hitherto separated in the past.  
Furthermore, CPS are usually designed as a network of interacting elements with physical input and output as well as digital input (and possibly output). This is closely tied robotics and sensor network innovations with intelligence mechanisms and computational intelligence or artificial intelligence such as e.g., neural network applications. Further developments will enable improvements in-between computational and physical elements by means of intelligent mechanisms, increasing their adaptability, autonomy, efficiency, functionality, resilience, safety, and usability. This in turn will enlarge the application potential of CPS in several domains (besides traditional manufacturing e.g., traffic control, firefighting, exploration and safety, energy appliances, healthcare). Applications of CPS largely adhere to sensor-based communication-enabled autonomous systems in different fields. Many wireless sensor networks for example monitor some aspect of the environment and relay the processed information to a central node. Other examples of CPS include smart energy grids, autonomous automotive systems for cars and trucks, medical monitoring in health care, process control systems in manufacturing, distributed robotics, and automatic pilot avionics in planes and helicopters.  
A current challenge for innovation and development regarding CPS is the large difference in design thinking and practice between disciplines involved, e.g., software and mechanical engineering: A common language in terms of concepts and practice that is common to all involved disciplines is missing. Designing and deploying a CPS could for example be based on the 5C architecture (connection, conversion, cyber, cognition, configuration). In the connection domain, devices are designed to self-connect and self-sensing. Regarding conversion, data from self-connected devices and sensors are analyzing the status of critical issues with self-aware capabilities, machines can use the self-aware information to self-predict potential issues based on experience ore preset thresholds. In the cyber field, each active item is creating its own twin by using instrumented features and further characterize the machine health pattern--the established twin in the cyber space can perform self-compare for peer-to-peer performance for future enhancement. Regarding cognition, the outcomes of self-assessment and self-evaluation can be presented with an infographic meaning to show the content and context of potential issues. In the configuration level, an active item such as a machine or production system can be reconfigured based on risk criteria and priorities to achieve resilience and adaptability.