A study of existing Ontologies in the IoT-domain

Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.Comment: Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of Thing

A Two-Level Information Modelling Translation Methodology and Framework to Achieve Semantic Interoperability in Constrained GeoObservational Sensor Systems

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation. Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data models underpinning these information systems are typically too rigid in their data representation to allow for the ever-changing and evolving nature of geo-spatial domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in an interoperable and computable way. The health domain experiences similar challenges with representing complex and evolving domain information concepts. Within any complex domain (such as Earth system science or health) two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves, and new discoveries are made. Health informaticians have developed a sophisticated two-level modelling systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how data, information, and knowledge interoperability among heterogenous systems can be achieved. This research investigates whether two-level modelling can be translated from the health domain to the geo-spatial domain and applied to observing scenarios to achieve semantic interoperability within and between spatial data infrastructures, beyond what is possible with current state-of-the-art approaches. A detailed review of state-of-the-art SDIs, geo-spatial standards and the two-level modelling methodology was performed. A cross-domain translation methodology was developed, and a proof-of-concept geo-spatial two-level modelling framework was defined and implemented. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard was re-profiled to aid investigation of the two-level information modelling approach. An evaluation of the method was undertaken using II specific use-case scenarios. Information modelling was performed using the two-level modelling method to show how existing historical ocean observing datasets can be expressed semantically and harmonized using two-level modelling. Also, the flexibility of the approach was investigated by applying the method to an air quality monitoring scenario using a technologically constrained monitoring sensor system. This work has demonstrated that two-level modelling can be translated to the geospatial domain and then further developed to be used within a constrained technological sensor system; using traditional wireless sensor networks, semantic web technologies and Internet of Things based technologies. Domain specific evaluation results show that twolevel modelling presents a viable approach to achieve semantic interoperability between constrained geo-observational sensor systems and spatial data infrastructures for ocean observing and city based air quality observing scenarios. This has been demonstrated through the re-purposing of selected, existing geospatial data models and standards. However, it was found that re-using existing standards requires careful ontological analysis per domain concept and so caution is recommended in assuming the wider applicability of the approach. While the benefits of adopting a two-level information modelling approach to geospatial information modelling are potentially great, it was found that translation to a new domain is complex. The complexity of the approach was found to be a barrier to adoption, especially in commercial based projects where standards implementation is low on implementation road maps and the perceived benefits of standards adherence are low. Arising from this work, a novel set of base software components, methods and fundamental geo-archetypes have been developed. However, during this work it was not possible to form the required rich community of supporters to fully validate geoarchetypes. Therefore, the findings of this work are not exhaustive, and the archetype models produced are only indicative. The findings of this work can be used as the basis to encourage further investigation and uptake of two-level modelling within the Earth system science and geo-spatial domain. Ultimately, the outcomes of this work are to recommend further development and evaluation of the approach, building on the positive results thus far, and the base software artefacts developed to support the approach

Internet of things in health: Requirements, issues, and gaps

Background and objectives: The Internet of Things (IoT) paradigm has been extensively applied to several sectors in the last years, ranging from industry to smart cities. In the health domain, IoT makes possible new scenarios of healthcare delivery as well as collecting and processing health data in real time from sensors in order to make informed decisions. However, this domain is complex and presents several tech- nological challenges. Despite the extensive literature about this topic, the application of IoT in healthcare scarcely covers requirements of this sector. Methods: A literature review from January 2010 to February 2021 was performed resulting in 12,108 articles. After filtering by title, abstract, and content, 86 were eligible and examined according to three requirement themes: data lifecycle; trust, security, and privacy; and human-related issues. Results: The analysis of the reviewed literature shows that most approaches consider IoT application in healthcare merely as in any other domain (industry, smart cities…), with no regard of the specific requirements of this domain. Conclusions: Future effort s in this matter should be aligned with the specific requirements and needs of the health domain, so that exploiting the capabilities of the IoT paradigm may represent a meaningful step forward in the application of this technology in healthcare.Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía P18-TPJ - 307

Design of semantic information broker for localized computing environments in the internet of things

Emerging communication technologies of the Internet of Things (IoT) make all the devices of a spatial-limited physical computing environment locally interconnected as well as connected to the Internet. Software agents running on devices make the latter “smart objects” that are visible in our daily lives as real participating entities. Based on the M3 architecture for smart spaces, we consider the problem of creating a smart space deploying a Semantic Information Broker (SIB) in a localized IoT-environment. SIB supports agent interaction in the smart space via sharing and self-generating information and its semantics. This paper proposes a renewed SIB design with increased extensibility, dependability, and portability. The research done is a step towards an efficient open interoperability platform for the smart space application development

Batch and Streaming Data Ingestion towards Creating Holistic Health Records

The healthcare sector has been moving toward Electronic Health Record (EHR) systems that produce enormous amounts of healthcare data due to the increased emphasis on getting the appropriate information to the right person, wherever they are, at any time. This highlights the need for a holistic approach to ingest, exploit, and manage these huge amounts of data for achieving better health management and promotion in general. This manuscript proposes such an approach, providing a mechanism allowing all health ecosystem entities to obtain actionable knowledge from heterogeneous data in a multimodal way. The mechanism includes diverse techniques for automatically ingesting healthcare-related information from heterogeneous sources that produce batch/streaming data, managing, fusing, and aggregating this data into new data structures (i.e., Holistic Health Records (HHRs)). The latter enable the aggregation of data coming from different sources, such as Internet of Medical Things (IoMT) devices, online/offline platforms, while to effectively construct the HHRs, the mechanism develops various data management techniques covering the overall data path, from data acquisition and cleaning to data integration, modelling, and interpretation. The mechanism has been evaluated upon different healthcare scenarios, ranging from hospital-retrieved data to patient platforms, combined with data obtained from IoMT devices, having produced useful insights towards its successful and wide adaptation in this domain. In order to implement a paradigm shift from heterogeneous and independent data sources, limited data exploitation, and health records, the mechanism has combined multidisciplinary technologies toward HHRs. Doi: 10.28991/ESJ-2023-07-02-03 Full Text: PD

AIoTES: Setting the principles for semantic interoperable and modern IoT-enabled reference architecture for Active and Healthy Ageing ecosystems

[EN] 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 crossplatform 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.We thank to all the people who have participated in the development and validation of AIoTES. This work has been developed under the framework of the ACTIVAGE project. The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 732679.Valero-López, CI.; Medrano-Gil, A.; González-Usach, R.; Julián-Seguí, M.; Fico, G.; Arredondo, MT.; Stavropoulos, TG.... (2021). AIoTES: Setting the principles for semantic interoperable and modern IoT-enabled reference architecture for Active and Healthy Ageing ecosystems. Computer Communications. 177:96-111. https://doi.org/10.1016/j.comcom.2021.06.0109611117

4W1H in IoT semantics

International audienceIoT systems are now being deployed worldwide to sense phenomena of interest. The existing IoT systems are often independent which limits the use of sensor data to only one application. Semantic solutions have been proposed to support reuse of sensor data across IoT systems and applications. This allows integration of IoT systems for increased productivity by solving challenges associated with their interoperability and heterogeneity. Several ontologies have been proposed to handle different aspects of sensor data collection in IoT systems, ranging from sensor discovery to applying reasoning on collected sensor data for drawing inferences. In this paper, we study and categorise the existing ontologies based on the fundamental ontological concepts (e.g., sensors, context, location, and more) required for annotating different aspects of data collection and data access in an IoT application. We identify these fundamental concepts by answering the 4Ws (What, When, Who, Where) and 1H (How) identified using the 4W1H methodology

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways

A Holistic and Interoperable Approach towards the Implementation of Services for the Digital Transformation of Smart Cities: The Case of Vitoria-Gasteiz (Spain)

Cities in the 21st century play a major role in the sustainability and climate impact reduction challenges set by the European agenda. As the population of cities grows and their environmental impact becomes more evident, the European strategy aims to reduce greenhouse gas emissions—the main cause of climate change. Measures to reduce the impact of climate change include reducing energy consumption, improving mobility, harnessing resources and renewable energies, integrating nature-based solutions and efficiently managing infrastructure. The monitoring and control of all this activity is essential for its proper functioning. In this context, Information and Communication Technology (ICT) plays a key role in the digitisation, monitoring, and managing of these different verticals. Urban data platforms support cities on extracting Key Performance Indicators (KPI) in their efforts to make better decisions. Cities must be transformed by applying efficient urban planning measures and taking into account not only technological aspects, but also by applying a holistic vision in building solutions where citizens are at the centre. In addition, standardisation of platforms where applications are integrated as one is necessary. This requires interoperability between different verticals. This article presents the information platform developed for the city of Vitoria-Gasteiz in Spain. The platform is based on the UNE 178104 standard to provide a holistic architecture that integrates information from the different urban planning measures implemented in the city. The platform was constructed in the context of the SmartEnCity project following the urban transformation strategy established by the city. The article presents the value-added solutions implemented in the platform. These solutions have been developed by applying co-creation techniques in which stakeholders have been involved throughout the process. The platform proposes a step forward towards standardization, harmonises the integration of data from multiple vertical, provides interoperability between services, and simplifies scalability and replicability due to its microservice architecture.This work has been supported by the Department of Education, Universities, and Research of the Basque Government under the projects Ikerketa Taldeak (Software and Systems Engineering research group of Mondragon Unibertsitatea) and the European Union’s Horizon 2020 research and innovation programme under the project SmartEnCity with the grant agreement no. 691883

A Semantic-enabled Framework For Future Internet Of Things Applications

While the challenge of connecting Internet of Things (IoT) devices at the lowest layer has been widely studied, integrating and interoperating huge amounts of sensed data of heterogeneous IoT devices is becoming increasingly important because of the possibility of consuming such data in supporting many potential novel IoT applications. A common approach to processing and consuming IoT data is a centralized paradigm: sensor data is sent over the network to a comparatively powerful central server or a cloud service, where all processing takes place. However, this approach has some limitations as it requires devices to interact directly with a cloud which is not cost effective. First, it has high demands on the device's storage and computational capabilities. Second, as devices grow rapidly in a deployment area, sending all the data to a centralized cloud server requires high network bandwidth. Moreover, this often creates data privacy concerns as all raw data will be sent to a centralized place. To address the above limitations for building future Internet of Things applications, we present an early design of a novel framework that combines Internet of Things, Semantic Web, and Big Data concepts. We not only present the core components to build an IoT system, but also list existing alternatives with their merits. This framework aims to incorporate open standards to address the potential challenges in building future IoT applications. Therefore, our discussion revolves around open standards to build the framework, rather than proprietary standards