There is great potential in leveraging Artificial Intelligence (AI) systems to optimize complex infrastructures, automate difficult tasks, or support autonomy and coordination between networked devices. However, advances in state-of-theart AI often neglect features and/or requirements that businesses care deeply about, namely traceability and explainability. A majority of available research has not explored much the deployment of semi-physical architectures combining fuzzy rule-based systems with more opaque models to improve explainability, being this specially true for the management of microservices in cloud and cloud-edge environments. This contribution builds on previous work that proposes a middle ground of mixed AI architectures that combine the performance of black-box AI models with "a more explainable overall architecture" by implementing a microservice scaling system for distributed cloud environments using a cascade approach. This work demonstrates and evaluates an application case of such an approach departing from a Service Level Agreement compliance, in a case of microservice scaling decision over cloud (and cloud-like) infrastructures.

Over the years, RDF streaming has been explored in research and practice from many angles, resulting in a wide range of RDF stream definitions. This variety presents a major challenge in discussing and integrating streaming systems due to a lack of a common language. This work attempts to address this critical research gap by systematizing RDF stream types present in the literature in a novel taxonomy. The proposed RDF Stream Taxonomy (RDF-STaX) is embodied in an OWL 2 DL ontology that follows the FAIR principles, making it readily applicable in practice. Extensive documentation and additional resources are provided to foster the adoption of the ontology. Three use cases for the ontology are presented with accompanying competency questions, demonstrating the usefulness of the resource. Additionally, this work introduces a novel nanopublications dataset, which serves as a collaborative, living state-of-the-art review of RDF streaming. The results of a multifaceted evaluation of the resource are presented, testing its logical validity, use case coverage, and adherence to the community’s best practices, while also comparing it to other works. RDF-STaX is expected to help drive innovation in RDF streaming by fostering scientific discussion, cooperation, and tool interoperability.

As the role of knowledge-based systems in IoT keeps growing, ensuring resource efficiency of RDF stores becomes critical. However, up until now benchmarks of RDF stores were most often conducted with only one dataset, and the differences between the datasets were not explored in detail. In this paper, our objective is to close this research gap by experimentally evaluating the load times of eight diverse RDF datasets from the RiverBench benchmark suite. In the experiments, we use five different RDF store implementations and several resource-constrained hardware platforms. To analyze the results, we introduce the notion of relative loading speed (RLS), allowing us to observe that the loading speed can differ between datasets by as much as a factor of 9.01. This serves as clear evidence that “not all RDF is created equal” and stresses the importance of using multiple benchmark datasets in evaluations. We outline the possible reasons for this drastic difference, which should be further investigated in future work. To this end, we published the data, code, and the results of our experiments.

There is great potential in leveraging Artificial Intelligence (AI) systems to optimize complex infrastructures, automate difficult tasks, or support autonomy and coordination between networked devices. However, advances in state-of-the-art AI often neglect features and/or requirements that businesses care deeply about, namely traceability and explainability. While majority of research concerning Explainable AI remains focused on weight modelling and timid gray-box approaches, the state-of-the-art has not explored much the deployment of semi-physical architectures combining fuzzy rule-based systems with more opaque models to improve explainability. This contribution aims to explore and make the case for a middle ground of mixed AI architectures that combine the performance of black-box AI models with a more explainable overall architecture, enabling operators to use them, while still retaining the core aspects of explainability, when compared to full black-box AI systems. This work contextualizes a potential application of such approach to the problem of Service Level Agreement compliance, in a case of microservice allocation decision over cloud (and cloud-like) infrastructures.

This paper presents the design of the ASSIST-IoT's smart orchestrator. This solution aims at controlling the lifecycle of next-generation IoT enablers in distributed, multi-cluster environments, easing the communication of such enablers and increasing the overall network security. The presented high-level architecture is based on three main design concepts: cloud-nativeness, encapsulation, and automation. A preliminary realization of such architecture is described, along with some graphical interfaces specifically developed to manage it. Its practical use has served for validating its expected performance, allowing the instantiation and communication of enablers in Kubernetes-based distributed ecosystems, and has shown some potential areas of improvement: better support to broader number of use cases, optimization of installation procedures considering a unique packaging format, and enabling the integration of DevOps tools for both automated system and enablers' upgrading.

Modern programming languages are very complex, diverse, and non-uniform in their structure, code composition, and syntax. Therefore, it is a difficult task for computer science students to retrieve relevant code snippets from large code repositories, according to their programming course requirements. To solve this problem, an AI-based approach is proposed, for students to better understand and learn code semantics, with solutions for real-world coding exercises. First, a large number of solutions are collected from a course titled “Algorithms and Data Structures” and preprocessed, by removing unnecessary elements. Second, the solution code is converted into a sequence of words and tokenized. Third, the sequence of tokens is used to train and validate the model, through a word embedding layer. Finally, the model is used for the relevant code retrieval and classification task, for the students. In this study, a bidirectional long short-term memory neural network (BiLSTM) is used as the core deep neural network model. For the experiment, approximately 120,000 real-world solutions from three datasets are used. The trained model achieved an average precision, recall, F1 score, and accuracy of 94.35

Next Generation Internet of Things (NGIoT) addresses the deployment of complex, novel IoT ecosystems. These ecosystems are related to different technologies and initiatives, such as 5G/6G, AI, cybersecurity, and data science. The interaction with these disciplines requires addressing complex challenges related with the implementation of flexible solutions that mix heterogeneous software and hardware, while providing high levels of customisability and manageability, creating the need for a blueprint reference architecture (RA) independent of particular existing vertical markets (e.g., energy, automotive, or smart cities). Different initiatives have partially dealt with the requirements of the architecture. However, the first complete, consolidated NGIoT RA, covering the hardware and software building blocks, and needed for the advent of NGIoT, has been designed in the ASSIST-IoT project. The ASSIST-IoT RA delivers a layered and modular design that divides the edge-cloud continuum into independent functions and cross-cutting capabilities. This contribution discusses practical aspects of implementation of the proposed architecture within the context of real-world applications. In particular, it is shown how use of cloud-native concepts (microservices and applications, containerisation, and orchestration) applied to the edge-cloud continuum IoT systems results in bringing the ASSIST-IoT concepts to reality. The description of how the design elements can be implemented in practice is presented in the context of an ecosystem, where independent software packages are deployed and run at the selected points in the hardware environment. Both implementation aspects and functionality of selected groups of virtual artefacts (micro-applications called enablers) are described, along with the hardware and software contexts in which they run.

Abundance of vastly heterogeneous, high-volume/high-velocity data producers/consumers, predominantly caused by proliferation of IoT-based solutions, results in an urgent need for efficient semantic interoperability solutions. Hence, the need to solve the problems of domain understanding, domain formal representation, and expression of mappings between different data models arise. In this contribution, we present an alignment format called IPSM-AF, for persisting correspondences between ontologies that can be used for semantic translation. Specifically, alignments represented using the IPSM-AF can be efficiently parsed and consumed by the IPSM tool, which performs the actual translation (possibly on streaming data). The proposed format is compliant with the Alignment API format, level 2.

With the ongoing, gradual shift of large-scale distributed systems towards the edge-cloud continuum, the need arises for software solutions that are universal, scalable, practical, and grounded in well-established technologies. Simultaneously, semantic technologies, especially in the streaming context, are becoming increasingly important for enabling interoperability in edge-cloud systems. However, in recent years, the field of semantic data streaming has been stagnant, and there are no available solutions that would fit those requirements. To fill this gap, in this contribution, a novel end-to-end RDF streaming approach is proposed (named JELLY). The method is simple to implement, yet very elastic, and designed to fit a wide variety of use cases. Its practical performance is evaluated in a series of experiments, including end-to-end throughput and latency measurements. It is shown that JELLY achieves vastly superior performance to the currently available approaches. The presented method makes significant progress towards enabling high-performance semantic data processing in numerous applications, including future edge-cloud systems. Moreover, this study opens up the possibility of applying and evaluating the method in real-life scenarios, which will be the focus of further research.

The aim of this contribution is twofold. First, to summarize the state-of-the-art in the area of Tactile Internet. Second, to outline, based on pilots of the ASSIST-IoT project, an architecture needed to realize Tactile Internet in Internet of Things ecosystems.

New requirements, posed by the Next Generation IoT, demand design of novel reference architectures, providing foundation for implementation of Internet of Things (IoT) ecosystems. Building on cloud-native concepts (e.g. microservices, virtualisation, and containerization), a flexible architecture that answers requirements present in recent IoT deployments is introduced. A general description of components of the architecture (grouped in horizontal planes and vertical capabilities) is provided, together with formal definition of architectural views. Moreover, ground is laid for upcoming validation in real-world-anchored scenarios. Functional, node, deployment and data views are presented, each of them addressing concerns of different stakeholder groups, typically involved in an IoT deployments.

Interoperability, on the semantic level, deals with shared understanding of data, between IoT artifacts. Positioned on top of the syntactic layer, semantic interoperability facilitates solutions to problems that arise after the data is in a common format, with syntax understood by all participants. Provisioning of compatibility between not directly compatible data structures, representations, conventions and standards, falls strictly under the responsibility of semantic methods.This chapter introduces the INTER-IoT perspective on data semantics and summarizes it’s achievements in the field of semantic interoperability. Being a generic and far-reaching solution, the syntactic compatibility challenge is also mentioned, as it is a necessary precondition to comprehensive data interoperability suite.

The Internet of Things (IoT) is a jeopardized ecosystem in which heterogeneity is intrinsic at all levels, from physical devices to communication protocols till high-level application semantics. The absence of IoT standards increases the complexity of integration and interoperability among heterogeneous platforms. This generates a strong demand for proper methodologies in order to fully support the development of heterogeneous, yet interoperable, IoT systems. To fill this gap, in this chapter the INTER-METH engineering methodology is presented. Developed in the context of the European H2020 INTER-IoT project, INTER-METH supports the integration of heterogeneous IoT platforms from the analysis to the maintenance phase. Its abstract and instantiated process schema are described, with particular focus on the analysis and design phases that are fundamental drivers of the whole integration process. Relevant interoperability design patterns, the building blocks of the design phase, will be discussed. The chapter also presents the INTER-CASE tool associated to the methodology which is useful to guide integrator designers in properly following the INTER-METH workflow. Finally, the chapter shows the proposed methodology and its tool in action, with the practical integration of BodyCloud and UniversAAL platforms adopted in the INTER-Health pilot of the INTER-IoT project.

RDF is growing in popularity for enterprise data harmonization software (e.g. Virtuoso, or Corporate Memory). Interoperability techniques, e.g. query rewriting, and language transformation rely on the expressivity and flexibility of RDF. As a meta-model capable of expressing information with the use of a variety of vocabularies or ontologies, RDF enables a range of solutions for the problem of data interoperability. Exploiting RDF for interoperability surfaces the need for transformations between homologous RDF structures. This paper examines two independently developed technologies that enable mapping of RDF graphs: Shape Expressions Mapping Language (ShExMap) and IPSM (Inter Platform Semantic Mediator). We investigate the formal languages used by both, examining their theoretical foundation, peculiarities, and design. We consider two practical examples of transformations and compare effectively equivalent constructs that can be expressed in either approach. We also describe both technologies in broader context of practical applicability.

CasAware is an Ambient Assisted Living platform, developed within an Italian research project, with the aim to improve the level of comfort and well-being of inhabitants of a house, while optimizing the energy consumption. A key feature, for successful realization of such a platform, is its capability to interoperate with other IoT platforms, which can augment CasAware with additional services. Indeed, this capability facilitates smooth communication between CasAware devices and external devices connected to other IoT platforms, thus allowing efficient exchange of messages among them. However, such integration is hindered by the heterogeneity of data models used in different platforms, which is also related to lack of common standards. In order to realize integration needed for CasAware, this paper presents an approach which exploits results of the INTER-IoT project. Specifically, the INTER-IoT methodology and a set of software tools for achieving IoT interoperability are applied. In the presented study, it is shown how the INTER-IoT based approach can facilitate interoperability between CasAware and two other platforms, which use completely different data models.

CasAware is an Ambient-Assisted Living platform, which aims at improving level of comfort and well-being of inhabitants, while optimizing energy consumption. A key feature for a successful realization of such a platform is its integration with other available/deployed IoT solutions. Indeed, this integration has to facilitate smooth communication between the CasAware platform and devices, and other IoT devices, in particular to enable the exchange of data sets among them. In this paper, we introduce an approach, followed in CasAware, to realize such integration. Specifically, the proposed solution exploits the guidelines of the INTER-IoT project, which proposed a framework for inter-platform communication. So far, various existing IoT platforms have been plugged into this framework, originating from multiple application fields, thus demonstrating the advantages of such integration, capable of disregarding the specific application context. The idea behind the herein presented study is that an INTER-IoT-based approach can guarantee enhancement of interoperability between CasAware and other platforms, thus promoting a unified view of the data, from client’s perspective, within the complete IoT ecosystem.

While Internet of Things (IoT) systems/applications/ platforms/devices materialize with increasing speed, software engineering "reflection" does not follow "fast enough". The situation is particularly "unbalanced" when one considers integration of independently developed IoT artifacts. To address this problem, we attempt at cataloging software design patterns that materialize in the context of interoperability of/within IoT ecosystems. The aim of this contribution is to briefly describe most common patterns (based on results of the INTER-IoT project), including analysis of common issues, and elaboration of a need for the creation of new (or extending existing) patterns in order to achieve solutions applicable for IoT artifact integration.

With the rise of Linked Data, triplestores, natural language processing and other semantic technologies, popularity and applicability of ontologies grows as well. One of more common operations on ontologies, regardless of their application, is an alignment - a matching between entities from different ontologies. So far, many alignment formats and languages have been proposed, some of them for general use and some particular to a concrete implementation of an alignment software. In this paper we present an ontology alignment format developed within the INTER-IoT project, and compatible with the Alignment API. Named after our semantic translation software - Inter Platform Semantic Mediator (IPSM) - the IPSM Alignment Format is universal and can be used to express both simple andf complex alignments. It is applied in practice, and used in semantic translations within INTER-IoT.

There are multiple ways, in which large-scale IoT ecosystems can materialize. First, they can be instantiated as a result of a specific project. For instance, when a city authority decides to deploy an IoT-based smart city solution. Second, they can be result of "organic growth". For instance, a port authority deploys smart lighting system, then it implements smart gateways, and in the next stage it decides to combine these two, and a smart container positioning system, into a complete port IoT ecosystem. In the context of interoperability, one of the interesting open issues is: how to manage identities of artifacts and entities that constitute the IoT ecosystem. Identity management seems to be relatively easy in the first case - identifier management can be a part of the IoT deployment. However, as soon as multiple artifacts are to be combined, the problem of identification is no longer easy to solve. This is particularly the case when (i) multiple artifacts originate from different vendors (that use their own identity management approaches), and (ii) when the interoperability solution is based on semantic technologies. In this context, the aim of this contribution is to introduce an approach to identifier management, as well as potential ID interoperability architectures that have been developed within the scope of the INTER-IoT project.