The Internet of Things (IoT) has promised a future where everything gets connected. Unfortunately, building a single global ecosystem of Things that communicate with each other seamlessly is virtually impossible today. The reason is that the IoT is essentially a collection of isolated “Intranets of Things”, also referred to as “vertical silos”, which cannot easily and efficiently interact with each other. Smart cities are perhaps the most striking examples of this problem since they comprise a wide range of stakeholders and service providers who must work together, including urban planners, financial organisations, public and private service providers, telecommunication providers, industries, citizens, and so forth. Within this context, the contribution of this paper is threefold: (i) discuss business and technological implications as well as challenges of creating successful open innovation ecosystems, (ii) present the technological building blocks underlying an IoT ecosystem developed in the framework of the EU Horizon 2020 programme, (iii) present a smart city pilot (Heat Wave Mitigation in Métropole de Lyon) for which the proposed ecosystem significantly contributes to improving interoperability between a number of system components, and reducing regulatory barriers for joint service co-creation practices.

The Semantic Web emerged with the vision of eased integration of heterogeneous, distributed data on the Web. The approach fundamentally relies on the linkage between and reuse of previously published vocabularies to facilitate semantic interoperability. In recent years, the Semantic Web has been perceived as a potential enabling technology to overcome interoperability issues in the Internet of Things (IoT), especially for service discovery and composition. Despite the importance of making vocabulary terms discoverable and selecting the most suitable ones in forthcoming IoT applications, no state-of-the-art survey of tools achieving such recommendation tasks exists to date. This survey covers this gap by specifying an extensive evaluation framework and assessing linked vocabulary recommendation tools. Furthermore, we discuss challenges and opportunities of vocabulary recommendation and related tools in the context of emerging IoT ecosystems. Overall, 40 recommendation tools for linked vocabularies were evaluated, both empirically and experimentally. Some of the key findings include that (i) many tools neglect to thoroughly address both the curation of a vocabulary collection and effective selection mechanisms, (ii) modern information retrieval techniques are underrepresented, and (iii) the reviewed tools that emerged from Semantic Web use cases are not yet sufficiently extended to fit today’s IoT projects.

Abstract-A present challenge in today's Internet of Things (IoT) ecosystem is to enable interoperability across heterogeneous systems and service providers. Restricted access to data sources and services limits the capabilities of a smart city to improve social, environmental and economic aspects. Interoperability in the IoT is concerned with both, messaging interfaces and semantic understanding of heterogeneous data. In this paper, the first building blocks of an emerging open IoT ecosystem developed at the EU level are presented. Semantic web technologies are applied to the existing messaging components to support and improve semantic interoperability. The approach is demonstrated with a proof-of-concept for connected vehicle services in a smart city setting.

Efficient ontology reuse is a key factor in the Semantic Web to enable and enhance the interoperability of computing systems. One important aspect of ontology reuse is concerned with ranking most relevant ontologies based on a keyword query. Apart from the semantic match of query and ontology, the state-of-the-art often relies on ontologies' occurrences in the Linked Open Data (LOD) cloud to determine relevance. We observe that ontologies of some application domains, in particular those related to Web of Things (WoT), often do not appear in the underlying LOD datasets used to define ontologies' popularity, resulting in ineffective ranking scores. This motivated us to investigate -based on the problematic WoT case -whether the scope of ranking models can be extended by relying on qualitative attributes instead of an explicit popularity feature. We propose a novel approach to ontology ranking by (i) selecting a range of relevant qualitative features, (ii) proposing a popularity measure for ontologies based on scholarly data, (iii) training a ranking model that uses ontologies' popularity as prediction target for the relevance degree, and (iv) confirming its validity by testing it on independent datasets derived from the state-of-the-art. We find that qualitative features help to improve the prediction of the relevance degree in terms of popularity. We further discuss the influence of these features on the ranking model.

Abstract. Situation awareness is a key feature of pervasive computing and requires external knowledge to interpret data. Ontology-based reasoning approaches allow for the reuse of predefined knowledge, but do not provide the best reasoning capabilities. To overcome this problem, a hybrid model for situation awareness is developed and presented in this paper, which integrates the Situation Theory Ontology into Context Space Theory for inference. Furthermore, in an effort to rely as much as possible on open IoT messaging standards, a domain-independent framework using the O-MI/O-DF standards for sensor data acquisition is developed. This framework is applied to a smart neighborhood use case to reduce food waste at the consumption stage.