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

Towards interoperability of entity-based and event-based IoT platforms: The case of NGSI and EPCIS standards

With the advancement of IoT devices and thanks to the unprecedented visibility and transparency they provide, diverse IoT-based applications are being developed. With the proliferation of IoT, both the amount and type of data items captured have increased dramatically. The data generated by IoT devices reside in different organizations and systems, and a major barrier to utilizing the data is the lack of interoperability among the standards used to capture the data. To reduce this barrier, two major standards have emerged: the Global Standards One (GS1) Electronic Product Code Information Service (EPCIS) and the FIWARE Next Generation Services Interface (NGSI). However, the two standards differ not only in the data encoding but also in the underlying philosophy of representing IoT data; namely, EPCIS is event-based, and NGSI is entity-based. Interoperability between FIWARE and EPCIS is essential for system integration. This paper presents OLIOT Mediation Gateway, now one of the incubated generic enablers offered by the FIWARE Foundation, that realizes the required interoperability between NGSI and EPCIS systems. It also demonstrates the applicability and feasibility of the Gateway by applying it to a real-life case study of integrating transparency systems used in a meat supply chain

Hybrid Solution for Integrated Trading

Integrated applications are complex solutions, whose complexity are determined by the economic processes they implement, the amount of data employed (millions of records grouped in hundreds of tables, databases, hundreds of GB) and the number of users. Service oriented architecture (SOA), is now the most talked-about integration solution in mainstream journals, addressing both simple applications, for a department but also at enterprise level. SOA can refer to software architecture or to a way of standardizing the technical architecture of an enterprise and it shows its value when operating in several distinct and heterogeneous environments.System Integration, Data Integration, Web Services, Java, XML, Stock Market

A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine learning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes.Comment: This work has been submitted to the ELSEVIER for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Lifecycle Based Modeling of Smart City Ecosystem

International audienceSmart city services have an inevitable role in addressing the complexity of modern city operation. Smart transport, smart parking, smart energy, smart water and many others are examples of vertical smart city systems that are mainly concerned with its particular domain. Realizing the full promise of smart city will require interoperability among those systems and data fusion between heterogeneous components from different domains. In this regard, many standardization organizations have been working on modeling smart city and similar or related systems and concepts, such as Internet of Things (IoT) and Cyber Physical Systems (CPS), to ensure common technical grounding and architectural principles. Though, there is still a need to address the higher-level requirements of smart city as a complete ecosystem. To this end, this paper discusses different Smart City solutions and highlights lifecycle based modeling to better integrate people, processes, and systems; and assure information consistency, traceability, and long-term archiving