Pawel Szmeja

Semantic interoperability in the Internet of Things: An overview from the INTER-IoT perspective

Abstract The Internet of Things (IoT) idea, explored across the globe, brings about an important issue: how to achieve interoperability among multiple existing (and constantly created) IoT platforms. In this context, in January 2016, the European Commission has funded seven projects that are to deal with various aspects of interoperability in the Internet of Things. Among them, the INTER-IoT project is aiming at the design and implementation of, and experimentation with, an open cross-layer framework and associated methodology to provide voluntary interoperability among heterogeneous IoT platforms. While the project considers interoperability across all layers of the software stack, we are particularly interested in answering the question: how ontologies and semantic data processing can be harnessed to facilitate interoperability across the IoT landscape. Henceforth, we have engaged in a “fact finding mission” to establish what is currently at our disposal when semantic interoperability is concerned. Since the INTER-IoT project is initially driven by two use cases originating from (i) ( e/m ) Health and (ii) transportation and logistics , these two application domains were used to provide context for our search. The paper summarizes our findings and provides foundation for developing methods and tools for supporting semantic interoperability in the INTER-IoT project (and beyond).

Semantic Technologies for the IoT - An Inter-IoT Perspective

The Inter-IoT project is aiming at the design and implementation of, and experimentation with, an open cross-layer framework and associated methodology, to provide voluntary interoperability among heterogeneous Internet of Things (IoT) platforms. The project is initially driven by uses cases from two domains: (e/m) Health and transportation and logistics in a port environment. While the Inter-IoT will provide interoperability across the software stack, here, we focus our attention on the semantic interoperability. In this context, we present a concise overview of existing IoT-related semantic approaches, which might either be directly applicable to, or serve as a source of inspiration for, the Inter-IoT applications.

Tools for Ontology Matching—Practical Considerations from INTER-IoT Perspective

There exists a large body of scientific literature devoted to ontology matching, aligning, mapping translating and merging. With it, comes a long list (90+) of tools that support various aspects of these operations. We have approached such tools from the perspective of the INTER-IoT project, in which one of the goals is to facilitate semantic interoperability of Internet of Things platforms. Thus, we had to answer a question: what is actually available when one needs to align/merge ontologies. Here, we summarize our findings.

Applying Saaty's Multicriterial Decision Making Approach in Grid Resource Management

In this paper we consider combining ontologically represented information with Saaty’s Analytic Hierarchy Process (AHP) to facilitate decision support for Grid users. The context for the proposal is provided by the Agents in Grid project (AiG), which aims at development of an agent-based infrastructure for efficient resource management in the Grid. In the AiG project, agents form teams, managers of which negotiate with clients and workers terms of potential collaboration. Here, we focus on the scenario, in which the user is searching for resources to execute a task, while the resources and the expert domain knowledge are organized in an ontology. Taking into account the complex nature of resource description and domain knowledge, multicriterial assessment of how accurate is the user description of her needs, and how it can be extended/refined, plays a crucial role. For instance, it should help the user to choose optimal algorithm and/or resource to solve her problem. Furthermore, ontologically described contract proposals, that are the results of autonomous negotiations, require multicriterial assessment. The AHP method is based on pairwise comparisons of criteria, and relies on the judgment of a panel of experts. In the context of the AiG project, we show on the example of the AHP method, how multicriteria group decision making can be used to support user in resource selection and assessment of contract proposals. DOI: http://dx.doi.org/10.5755/j01.itc.43.1.4587

Using Ontologies to Manage Resources in Grid Computing: Practical Aspects

This chapter shows how semantic technologies can enable the development of an agent-based intelligent middleware for the Grid (the Agents in Grid system). Resource providing agents are organized in teams and negotiate contracts with agents representing users. All information is ontologically demarcated and semantically processed. In particular, user preferences are turned into ontology class expressions and are applied directly in contract negotiations. The chapter provides a detailed overview of how ontologies are used in the AiG system. Key issues in the development of the AiG ontology are discussed, and the implementation of the ontology-focused parts of the system are described.

Towards Semantic Interoperability Between Internet of Things Platforms

Lack of interoperability between Internet of Things (IoT) platforms, systems, and applications is one of serious problems that prevents its even more rapid adoption. As long as individual IoT platforms cannot meaningfully communicate with each other, the vision of a global IoT ecosystem will not be realized. While the problem of IoT interoperability can be considered at different levels, starting from hardware interoperability at the bottom of the stack, we are interested in the semantic interoperability, which can be seen as the meta-level for all interoperability considerations. In other words, the aim of this chapter is to consider how multiple IoT platforms can “understand” each other and have meaningful “conversation”.

Reengineering and Extending the Agents in Grid Ontology

Ontology engineering, despite considerable progress, is still relatively new and dynamically evolving discipline. As a result, the universal standards for creating and/or editing an ontology, have not been established. This leads to problems with reusing and updating existing ontologies. It also makes writing an ontology from scratch seem like a good idea. The aim of this paper is two-fold. First, to discuss key issues encountered during re-engineering of an existing ontology. Second, to show how the good practices of ontology development were applied to model the area of computational linear algebra. Here, special attention is paid to the application of this ontology in the user support system.

Towards Common Vocabulary for IoT Ecosystems—preliminary Considerations

The INTER-IoT project aims at delivering a comprehensive solution to the problem of interoperability of Internet of Things platforms. Henceforth, semantic interoperability also has to be addressed. This should involve a hierarchy of ontologies, starting from an upper ontology, through core and domain ontologies. As a starting point, we have analyzed ontological models of the concepts of thing, device, observation and deployment, as occurring in the IoT domain. We have chosen five popular ontologies: SSN, SAREF, oneM2M Base Ontology, IoT-Lite, and OpenIoT, as candidates for a central INTER-IoT ontology.

Dimensions of Semantic Similarity

Semantic similarity is a broad term used to describe many tools, models and methods applied in knowledge bases, semantic graphs, text disambiguation, ontology matching and more. Because of such broad scope it is, in a “general” case, difficult to properly capture and formalize. So far, many models and algorithms have been proposed that, albeit often very different in design and implementation, produce a single score (a number) each. These scores come under the single term of semantic similarity. Whether one is comparing documents, ontologies, entities, or terms, existing methods often propose a universal score—a single number that “captures all aspects of similarity”. In opposition to this approach, we claim that there are many ways, in which semantic entities can be similar. We propose a division of knowledge (and, consequently, similarity) into categories (dimensions) of semantic relationships. Each dimension represents a different “type” of similarity and its implementation is guided by an interpretation of the meaning (semantics) of that similarity score in a particular dimension. Our proposal allows to add extra information to the similarity score, and to highlight differences and similarities between results of existing methods.

From relational databases to an ontology — Practical considerations

One of interesting problems, arising with deployment of large-scale systems, is integration of its nodes (systems / devices). In this work, we discus how to apply semantic technologies, as a mechanism to support node integration and facilitate interoperability within the developed ecosystem. We focus on pragmatic aspects of the proposed solution, discussed from the perspective of the Dependable Embedded Wireless Infrastructure (DEWI) EU project. In this context, a brief analysis of typical integration problems and reasons to apply solution based on semantic technologies is presented. Moreover, the integration procedure is outlined. Here, the key aspect that is discussed in considerable detail, is conversion from the DEWI nodes (based on a traditional relational database approach) towards universal cooperative nodes, which use semantic technologies.