The possibility of continuous monitoring of health conditions represents a crucial aspect for the improvement of living conditions, the prevention of potential pathologies and prompt response in critical situations. In particular, in intensive care or emergency situations, the evaluation of illness degree of a clinical risk level can be considered a predictive task in situations where streams of vital signs data are gathered by medical devices and the Internet of Medical Things (IoMT) sensors. In this framework, Early Warning Score (EWS) systems generating an aggregate score based on the measurement of a set of vital signs, such as National Early Warning Score 2 (NEWS2) and Modified Early Warning Score (MEWS), may provide a helpful decision support for the estimation of health state and triggers for critical care intervention. In the present work, we address a preliminary analysis in order to investigate the most suitable Machine Learning (ML) technique for the prediction of clinical risk classes of a continuously monitored patient in a particular condition where a limited number of vital parameters is available. This analysis is then intended to be preparatory for the final goal of designing an edge device connected to one or more wearable medical devices via IoMT, which adaptively exploits the best ML model to predict a reliable EWS.

Artificial Intelligence (AI) in Cyber-Physical Systems allows machine learning inference on acquired data with ever greater accuracy, thanks to models trained with massive amounts of information generated by Internet of Things devices. Edge Intelligence is increasingly adopted to execute inference on data at the border of local networks, exploiting models trained in the Cloud. However, the training tasks on Edge nodes are not supported yet with flexible dynamic migration between Edge and Cloud. This paper proposes a Cloud-Edge AI microservice architecture, based on Osmotic Computing principles. Notable features include: (i) containerized architecture enabling training and inference on the Edge, Cloud, or both, exploiting computational resources opportunistically to reach the best prediction accuracy; and (ii) microservice encapsulation of each architectural module, allowing a direct mapping with Commercial-Off-The-Shelf (COTS) components. Grounding on the proposed architecture: (i) a prototype has been realized with commodity hardware leveraging open-source software technologies; and (ii) it has been then used in a small-scale intelligent manufacturing case study, carrying out experiments. The obtained results validate the feasibility and key benefits of the approach.

Machine Learning (ML) is increasingly exploited in a wide range of application areas to analyze data streams from large-scale sensor networks, train predictive models and perform inference. The Cloud-Edge Intelligence (CEI) computing paradigm integrates cloud infrastructures for resource-intensive ML tasks with devices at the border of a local network for distributed data preprocessing, small-scale model training and prediction tasks. This can achieve a tunable trade-off of ML accuracy with improved data privacy, response latency, and bandwidth usage. Prevalent CEI architectures are based on microservices encapsulated in containers, but serverless computing is emerging as an alternative model. It is based on stateless event-driven functions to facilitate development and provisioning of application components, increase infrastructure elasticity and reduce management effort. This paper proposes a novel CEI framework for sensor-based applications, exploiting serverless computing for data management and ML tasks. Small-scale model training occurs at the edge with local data for quick prediction response, while large-scale models are trained in the cloud with the full sensor network data and then they are fed back to edge nodes for a progressive accuracy improvement. A fully functional prototype has been built by leveraging open source software tools, selected devices for field sensing and edge computing, and a commercial cloud platform. Experiments validate the feasibility and sustainability of the proposal, compared to an existing container-oriented microservice architecture.

Emerging distributed intelligence paradigms for the Internet of Things (IoT) call for flexible and dynamic reconfiguration of elementary services, resources and devices. In order to achieve such capability, this paper faces complex interoperability and autonomous decision problems by proposing a thorough framework based on the integration of the Semantic Web of Things (SWoT) and Social Internet of Things (SIoT) paradigms. SWoT enables low-power knowledge representation and autonomous reasoning at the edge of the network through carefully optimized inference services and engines. This layer provides service/resource management and discovery primitives for a decentralized collaborative social protocol in the IoT, based on the Linked Data Notifications(LDN) over Linked Data Platform on Constrained Application Protocol (LDP-CoAP). The creation and evolution of friend and follower relationships between pairs of devices is regulated by means of novel dynamic models assessing trust as a usefulness reputation score. The close SWoT-SIoT integration overcomes the functional limitations of existing proposals, which focus on either social device or semantic resource management only. A smart mobility case study on Plug-in Electric Vehicles (PEVs) illustrates the benefits of the proposal in pervasive collaborative scenarios, while experiments show the computational sustainability of the dynamic relationship management approach.