Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture.

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The extension of the Cloud to the Edge of the network through Fog Computing can have a significant impact on the reliability and latencies of deployed applications. Recent papers have suggested a shift from VM and Container based deployments to a shared environment among applications to better utilize resources. Unfortunately, the existing deployment and optimization methods pay little attention to developing and identifying complete models to such systems which may cause large inaccuracies between simulated and physical runtime parameters. Existing models do not account for application interdependence or the locality of application resources which causes extra communication and processing delays. This paper addresses these issues by carrying out experiments in both cloud and edge systems with various scales and applications. It analyses the outcomes to derive a new reference model with data driven parameter formulations and representations to help understand the effect of migration on these systems. As a result, we can have a more complete characterization of the fog environment. This, together with optimization methods can instruct application deployment and migration and improve the overall system reliability, delay and constraint violations. An Industry 4.0 based case study with different scenarios was used to analyze and validate the effectiveness of the proposed model. Tests were deployed on physical and virtual environments with different scales. The advantages of the model based optimization methods were validated in real physical environments. Based on these tests, we have found that our model is 92% accurate on load and delay predictions for application deployments in both cloud and edge. AbstractThe extension of the Cloud to the Edge of the network through Fog Computing can have a significant impact on the reliability and latencies of deployed applications. Recent papers have suggested a shift from VM and Container based deployments to a shared environment among applications to better utilize resources. Unfortunately, the existing deployment and optimization methods pay little attention to developing and identifying complete models to such systems which may cause large inaccuracies between simulated and physical runtime parameters. Existing models do not account for application interdependence or the locality of application resources which causes extra communication and processing delays. This paper addresses these issues by carrying out experiments in both cloud and edge systems with various scales and applications. It analyses the outcomes to derive a new reference model with data driven parameter formulations and representations to help understand the effect of migration on these systems. As a result, we can have a more complete characterization of the fog environment. This, together with optimization methods can instruct application deployment and migration and improve the overall system reliability, delay and constraint violations. An Industry 4.0 based case study with different scenarios was used...

Background: Person-Centered Care (PCC) is a promising approach towards improved quality of care and cost containment within health systems. It has been evaluated in Sweden and England. This feasibility study examines initial PCC implementation in a rehabilitation hospital for children in Poland. Methods: The WE-CARE Roadmap of enablers was used to guide implementation of PCC for patients with moderate scoliosis. A multi-disciplinary team of professionals were trained in the PCC approach and the hospital Information Technology (IT) system was modified to enhance PCC data capture. Semi-structured interviews were conducted with the nine health care professionals involved in the pilot study and three patients/parents receiving care. Transcribed data were analyzed via content analysis. Results: 51 patients and their families were treated via a PCC approach. High proportions of new PCC data fields were completed by the professionals. The professionals were able to implement the three core PCC routines and perceived benefits using the PCC approach. Patients and their families also perceived improved quality care. The WE-CARE framework enablers facilitated PCC implementation in this setting. Conclusions: This feasibility pilot study indicates that the Gothenburg PCC approach can be successfully transferred to a rehabilitation hospital in Poland with favorable perceptions of implementation by both professionals and patients/their families.