Modern edge artificial intelligence (AI) chipsets and edge-intelligence-enabled smart sensors frameworks support real-time data processing and event detection at the signal source. Beyond measuring local conditions and transmitting corresponding signals, AI-enabled smart sensors provide the capability to interpret and analyze signals through local analytical operations. Its well-known semantics provide an abstraction layer for better comprehension and self-descriptive applications. This paper identifies and explains the advantages of semantics and ontology engineering in integrating edge AI-enabled smart sensors and their applications into federated automotive systems. Building on demonstrated work using AudioSet for event detection, the application presented as an AI mechanic at the edge demonstrates how smart sensor integration necessitates the re-evaluation of how automotive diagnostic trouble codes (DTCs) are generated and processed. While DTCs and their logic are intended to facilitate correct assembly and repair, they do not always clearly indicate the underlying issue. This paper also underlines the need to review ontologies and semantic models for smart sensor standard specifications in light of AI-enabled smart sensors.

The increasing importance of audio-based healthcare diagnostics, particularly in chronic respiratory problems, has prompted the development of novel approaches for disease classification using the full spectrum analysis of cough sounds. This paper presents an innovative study exploring the potential of employing supervised and semi-supervised learning methodologies for disease categorization based on cough audio samples. Specifically, this study focuses on scenarios characterized by a shortage of annotated data related to chronic diseases. The objectives of our study involved the utilization of standard machine learning algorithms for direct classification based on embeddings, as well as the integration of Graph Neural Networks (GNNs) on the KNN graph. Preliminary results indicate that GNN models consistently outperformed traditional classifiers. For instance, with just 1 % of the data, GAT and GCN achieved AUC PR values of 0.84 and 0.87, respectively, surpassing all traditional methods. The superiority mentioned above was sustained even when the data fraction was augmented to 3% and 5%. The usefulness of graph neural networks (GNNs) was further supported by comprehensive performance measures, wherein the graph convolutional network (GCN) exhibited exceptional preci-sion and PR (precision-recall). In summary, the AudioVecDiagnosis framework presents a promising opportunity for further investigation in audio-based healthcare diagnostics. It provides an optimum approach for situations with a limited availability of labeled data.

The average life expectancy of the world’s population is increasing and the healthcare systems sooner than later will be compromised by its reduced capacity and its highly economic cost; in addition, the age distribution of the population is leading towards the older spectrum. This trend will lead to immeasurable and unexpected economic problems and social changes. In order to face up this challenge and complex economic and social problem, it is necessary to rely on the appropriate digital tools and technological infrastructures for ensuring that the elderly are properly cared in their everyday living environments and they can live independently for longer. This article presents ACTIVAGE IoT Ecosystem Suite (AIoTES), a concrete reference architecture and its implementation process that addresses these issues and that was designed within the first European Large Scale Pilot, ACTIVAGE, a H2020 funded project by the European Commission with the objective of creating sustainable ecosystems for Active and Healthy Ageing (AHA) based on Internet of Things and big data technologies. AIoTES offers platform level semantic interoperability, with security and privacy, as well as Big Data and Ecosystem tools. AIoTES enables and promotes the creation, exchange and adoption of cross-platform services and applications for AHA. The number of existing AHA services and solutions are quite large, especially when state-of-the-art technology is introduced, however a concrete architecture such as AIoTES gains more importance and relevance by providing a vision for establishing a complete ecosystem, that looks for supporting a larger variety of AHA services, rather than claiming to be a unique solution for all the AHA domain problems. AIoTES has been successfully validated by testing all of its components, individually, integrated, and in real-world environments with 4345 direct users. Each validation is contextualized in 11 Deployment Sites (DS) with 13 Validation Scenarios covering the heterogeneity of the AHA-IoT needs. These results also show a clear path for improvement, as well as the importance for standardization efforts in the ever-evolving AHA-IoT domain.

The progress towards proving full deployable Internet of Things solutions for cross-domain data exchange is moving slowly and in todays IoT's challenges the need for integrating data across different software platforms and its use over heterogeneous technology remains open. The use of Cloud computing for data sharing as part of the IoT solution(s) design is provided on the basis that sharing data is enabled by the cloud. The emerging of Fog computing is generating that new scenarios are being re-defined, and in particular cases the need for combining both technologies enabling interoperability is explored/studied. This paper presents an approach of the Data Interplay for the Fog of Things. The need for a more structured way to exchange data seamlessly between edge applications and the cloud is required. The data interplay between Fog and Cloud infrastructures addresses the need for big volume generation and storage of data and the way on how to transfer the data from the edge to the cloud and viceversa.

Sensor technology and sensor networks have evolved so rapidly that they are now considered a core driver of the Internet of Things (IoT), however data analytics on IoT streams is still in its infancy. This paper introduces an approach to sensor data analytics by using the OpenIoT1middleware; real time event processing and clustering algorithms have been used for this purpose. The OpenIoT platform has been extended to support stream processing and thus we demonstrate its flexibility in enabling real time on-demand application domain analytics. We use mobile crowd-sensed data, provided in real time from wearable sensors, to analyse and infer air quality conditions. This experimental evaluation has been implemented using the design principles and methods for IoT data interoperability specified by the OpenIoT project. We describe an event and clustering analytics server that acts as an interface for novel analytical IoT services. The approach presented in this paper also demonstrates how sensor data acquired from mobile devices can be integrated within IoT platforms to enable analytics on data streams. It can be regarded as a valuable tool to understand complex phenomena, e.g., air pollution dynamics and its impact on human health.

This paper introduces the stack for service delivery models and interoperability in the Internet of Things. The main characteristics and functional layers of the IoT stack are described. The applicability of the IoT stack is described based on particular use cases and deployed pilots. The validation of the IoT stack in terms of functionality and adaptation at different IoT particular areas is based on the Virtual Development Kit (VDK) developed and implemented within the framework of the OpenIoT project-OpenIoT project is the awarded Internet of Things open-source rookie of the year by BlackDuck Software Co. (www.github.com/OpenIotOrg). The methods and standards that boosted OpenIoT-VDK implementation are described in this paper. An instance of the OpenIoT-VDK process is described as the practical use case demonstrating being an IoT platform with autonomic behavior. OpenIoT-VDK creates IoT instances, analyzes the IoT stack dependence, and resolves them following interoperability principles. The OpenIoT-VDK instance deploys IoT service delivery models facilitating the validation of use cases by using the OpenIoT platform. As proof of concept, a delivered IoT service using open data from OpenIoT local instantiation is described.

The management performance of cloud systems is measured by the capacity of the cloud for controlling virtual infrastructures and their capability to run parallel-computing applications and distributed-processing services independently. The challenge about how this management performance can be done more dynamically (self-organization) by means of distributed user data and application data demands is yet an area to explore. This paper introduces first a functional architecture design, following the principles for cloud-based service lifecycle control and service composition in cloud, and second an in-house approach enabling self-organization for cloud services controlling the installation of virtual machines by using event-driven management operations acting as a proof of concept implementation. From a management point of view in cloud, enabling control of virtual infrastructures as a response to performance protocols by means of event(s) data processing is fundamental. Likewise managing cloud services lifecycle by enabling scalable applications and using distributed information systems and linked data processing, guarantee the self-organizing feature for cloud systems. Finally multiple advantages arise when infrastructure performance and end user data are used in cloud service management as it is discussed in this paper.

Internet of Things (IoT) is an emerging area that not only requires development of infrastructure but also deployment of new services capable of supporting multiple, scalable (cloud-based) and interoperable (multi-domain) applications. In the race of designing the IoT as part of the Future Internet architecture, academic and ICT's (Information and Communication Technology) industry communities have realized that a common IoT problem to be tackled is the interoperability of the information. In this paper we review recent trends and challenges on interoperability, and discuss how semantic technologies, open service frameworks and information models can support data interoperability in the design of the Future Internet, taking the IoT and Cloud Computing as reference examples of application domains.

Modern distributed applications and communication services have become increasingly complex, composed of diverse heterogeneous sub-systems, and it is progressively more unrealistic that the users of these systems will be able to manage them in a holistic end-to-end manner. In particular, it is increasingly difficult to understand how such systems operate, the meaning of errors, and how they can be manipulated in a managed way, cognisant of the end-to-end nature of these systems. This paper describes an approach to semantically enrich monitoring information, events and faults, and management actions in such a way that they can be presented to a manager in manner that can be understood and leveraged. This work is based on one of the key scenarios FAME research project.

In the Future Internet, free exchange of information between enterprise applications and networking systems promotes the personalization of services and enables many different types of end-user applications and management operations optimizing the network performance. As result of this free information exchange, we need to facilitate the federation of information between these applications, harmonizing the differences between operation, management data and information models in heterogeneous networks, and application management systems. This paper concentrates on identifying the research challenges that we have to address in the areas of software engineering and network management supporting federation. In particular, we describe research challenges for federated management systems highlighting the flexibility these systems can offer in the Future Internet communications, when they are able to support value-added federated end-to-end services. As part of our approach, semantic techniques are cited to represent networking information governing technology and/or network protocols offering a wide diversity of end-to-end services as a result of this transparent information sharing process.