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International audience; Energy Smart Appliances provides utilities and appliance manufacturers, and designers with new approaches to better understand real-world performance, assess actual energy benefits, and tailor each technology to the needs of their customers.With contributions from a fully international group of experts, including heads of prestigious research organizations and leading universities, and innovation managers of the main appliance manufacturers, Energy Smart Appliances includes discussion on:Enabling technologies for energy smart appliances, covering IoT devices and technology and active energy efficiency measures in residential environmentsSmart home and appliances, answering questions like ‘Where are we heading in terms of the overall smart homes’ future?’ and ‘What’s the energy impact from smart home devices?’Demand-side management and demand response, covering overall system/ appliances readiness and ideal energy management scenario to drive demand responseEnergy smart appliances’ best practices and success stories, including refrigerators, washers, dryers, and moreWith practical coverage of a wide range of potential scenarios and existing and future challenges, Energy Smart Appliances is an essential learning resource for electrical engineering professionals, equipment manufacturers, and designers, along with postgraduate electrical engineering students and researchers in related fields and programs of study.

The European Commission has promoted the deployment of the Digitalisation of Energy Action Plan (DoEAP), in order to develop an efficient, competitive market for a digital energy infrastructure and digital energy services that are both cyber-secure and sustainable. A central aspect of DoEAP is represented by the concept of Energy Data Spaces. Data exchange is crucial for emerging energy data services in the digital energy market and will help suppliers and energy service providers to innovate and cope with an increasing share of renewables in a more decentralised energy system. The data includes metering data, data from consumers such as home appliances, building automation, EV charging stations, or prosumers PV panel & inverters. Its availability and timely sharing and use among the relevant players is key for the energy transition. This document addresses main issues of data exchange in the three interconnected key sectors: energy, buildings and mobility; the analyses focus on existing concepts of data formats and data standards, reflecting on how to facilitate data sharing across the different sectors based on a common data framework. The foremost use cases of European projects and initiatives in the specific sector or at cross-sector level are presented, depicting the current state of data exchange deployments and identifying the necessary actions for the upcoming developments.

SAREF is an ontology created to enable interoperability between smart devices. While the IoT community has shown interest and understanding of SAREF as a means for interoperability, there is a lack in the literature of practical examples to implement SAREF in real applications. In order to validate the practical implementation of SAREF we perform two experiments. First we map IoT data available in a smart home into RDF using SAREF. In the second part of the paper an IoT environment is created by using the Knowledge Engine, a framework created to allow communication between smart devices, operating on Raspberry Pi’s emulating IoT devices, where the communication of the IoT devices is performed by sharing knowledge represented with SAREF. These experiments demonstrate that SAREF is an ontology that is successfully applicable in different situations, with data-mapping showing that SAREF is able to represent the information of different smart devices and by using the Knowledge Engine showing that SAREF can enable interoperability between smart devices.

International audience; Recently, a number of ontology-driven healthcare systems have been leveraged by the Internet-of-Things (IoT) 11 technologies, which offer opportunities to improve abnormal situation detection when integrating medical wearables and cloud 12 infrastructure. Usually, these systems rely on standardised IoT ontologies to represent sensor data observations. The ETSI 13 Smart Applications REFerence ontology (SAREF) is an extensible industry-oriented standard. In this paper, we explain the 14 need for interoperability of IoT healthcare applications and the role of standardised ontologies to achieve semantic 15 interoperability. In particular, we discuss the verbosity problem of SAREF when used for real-time electrocardiography (ECG), 16 emphasizing the requirement of representing time series. We compared the main ontologies in this context, according to 17 quality, message size (payload), IoT-orientation and standardisation. Here we describe the first attempt to extend SAREF for 18 specific e-Health use cases related to ECG data, the SAREF4health extension, which tackles the verbosity problem. Ontology-19 driven conceptual modelling was applied to develop SAREF4health, in which an ECG ontology grounded in the Unified 20 Foundational Ontology (UFO) plays the role of a reference model. The methodology was enhanced by following a 21 standardisation procedure and considering the RDF implementation of the HL7 Fast Healthcare Interoperability Resources 22 (FHIR) standard. The validation of SAREF4health includes the responses to competency questions, as well as the development 23 and tests of an IoT Early Warning System prototype that uses ECG data and collision identification to detect accidents with 24 truck drivers in a port area. This prototype integrates an existing ECG wearable with a cloud infrastructure, demonstrating the 25 performance impact of SAREF4health considering IoT constraints. Our results show that SAREF4health enables the semantic 26 interoperability of IoT solutions that need to deal with frequency-based time series. Design decisions regarding the trade-off 27 between ontology quality and aggregation representation are also discussed.

International audience; This paper is co-authored by an informal group of experts from a broad range of backgrounds, all of whom are active in standards groups, consortia, alliances and/or research projects in the Internet of Things (IoT) space. This paper has two objectives: 1) explain the need for semantic interoperability, 2) provide recommendations for semantic interoperability standards using ontologies. The target audience for this paper are: ● IoT system product owners who need to understand how they can effectively ensure interoperability of their products. ● IoT system and standardization engineers without background in semantic technologies. The document is made available under the ​Creative Commons Attribution 4.0 International License​.

International audience; Semantic technologies have recently gained significant support in a number of communities, in particular the IoT community. An important problem to be solved is that, on the one hand, it is clear that the value of IoT increases significantly with the availability of information from a wide variety of domains. On the other hand, existing solutions target specific applications or application domains and there is no easy way of sharing information between the resulting silos. Thus, a solution is needed to enable interoperability across information silos. As there is a huge heterogeneity regarding IoT technologies on the lower levels, the semantic level is seen as a promising approach for achieving interoperability (i.e. semantic interoperability) to unify IoT device description, data, bring common interaction, data exploration, etc.

Most reported MDA approaches give much attention to structural aspects in PSMs and in generated code, and less attention to the PIM level and the behaviour of the modelled applications. Consequently, application behaviour is generally not (well) defined at the PIM level. This paper presents an MDA-based approach that incorporates behaviour modelling at the PIM level in the development of a specific category of applications, i.e., context-aware mobile applications. The paper also illustrates a behaviour model transformation realized by using Medini QVT , which is a tool that implements the Query/ View/ Transformation (QVT) Relations specification defined by OMG for model-to-model transformations.

This research was performed in the GOOSE project, which is jointly funded by the MIST research program of the Dutch Ministry of Defense and the AMSN enabling technology program., The number of networked cameras is growing exponentially. Multiple applications in different domains result in an increasing need to search semantically over video sensor data. In this paper, we present the GOOSE demonstrator, which is a real-time general-purpose search engine that allows users to pose natural language queries to retrieve corresponding images. Top-down, this demonstrator interprets queries, which are presented as an intuitive graph to collect user feedback. Bottomup, the system automatically recognizes and localizes concepts in images and it can incrementally learn novel concepts. A smart ranking combines both and allows effective retrieval of relevant images., peer-reviewed

We describe the TNO system and the evaluation results for TRECVID 2013 Multimedia Event Detection (MED) and instance search (INS) tasks. The MED system consists of a bag-of-word (BOW) approach with spatial tiling that uses low-level static and dynamic visual features, an audio feature and high-level concepts. Automatic speech recognition (ASR) and optical character recognition (OCR) are not used in the system. In the MED case with 100 example training videos, support-vector machines (SVM) are trained and fused to detect an event in the test set. In the case with 0 example videos, positive and negative concepts are extracted as keywords from the textual event description and events are detected with the high-level concepts. The MED results show that the SIFT keypoint descriptor is the one which contributes best to the results, fusion of multiple low-level features helps to improve the performance, and the textual event-description chain currently performs poorly. The TNO INS system presents a baseline open-source approach using standard SIFT keypoint detection and exhaustive matching. In order to speed up search times for queries a basic map-reduce scheme is presented to be used on a multi-node cluster. Our INS results show above-median results with acceptable search times., This research for the MED submission was performed in the GOOSE project, which is jointly funded by the enabling technology program Adaptive Multi Sensor Networks (AMSN) and the MIST research program of the Dutch Ministry of Defense. The INS submission was partly supported by the MIME project of the creative industries knowledge and innovation network CLICKNL., peer-reviewed

In the context of Model-Driven Architecture (MDA), little attention has been given to behavioural aspects of service design. This paper proposes a MDA-based approach that considers these aspects in the development of context-aware mobile services. Starting from the specification of the external observable behaviour of a service, we gradually refine this behaviour considering the internal structure of the service. Particularly, we present a structure that is general enough to be used for several context-aware mobile services. However, it may be configured based on the specific service to be developed. An important step of our approach consists of identifying sequences of interactions, which we call interaction patterns, which can be mapped into a behaviour model of the components that execute the service. This model is platform-independent and may be realized in terms of several specific target technologies.

Coll. « New Politics of Autonomy »,; International audience