The Internet of Things connects physical devices offering sensing or actuating with their vicinity. The ever-growing capabilities of devices allow to imagine new architectures including them as first class citizens. New added-value applications can then be envisioned in smart agriculture, smart buildings, smart cities, energy and water management, e-health and ageing well... The Web of Things (WoT) allows to describe the devices semantics, bridging the gap between the different domain and service descriptions. In today WoT architectures, physical devices can be located at distance from systems that perform reasoning. A centralised approach does not take advantage of the devices capabilities and induces suboptimal data transfers as well as server overload. Besides, many devices are now smart enough to discover each other, exchange data, and collectively make decisions. CoSWoT objectives are to propose a distributed WoT-enabled software architecture embedded on constrained devices with two main characteristics: (1) it will use ontologies to specify declaratively the application logic of devices and the semantics of the exchanged messages; (2) it will add reasoning functionalities to devices, so as to distribute processing tasks among them. Doing so, the development of applications including devices of the WoT will be highly simplified: our platform will enable the development and execution of intelligent and decentralised smart WoT applications despite the heterogeneity of devices. In CoSWoT, WoT applications will rely on a platform hosting the base services. Besides traditional services, it will host extensions that correspond to two scientific barriers: (1) the use of ontologies as a generalised model for exchanges between heterogeneous devices. A joint statement from AIOTI WG3, IEEE P2413, oneM2M, W3C positions ontologies as key enablers for semantic interoperability on the WoT. However research questions remain concerning (i) the adequation of existing ontologies to the target application domains; (ii) the applicability of theoretical principles developed in a variety of protocols and standards, in the context of data streams; (iii) the discovery of heterogeneous devices, their services and how to solicit them. (2) distributed and embedded incremental reasoning. Devices become powerful enough to offer storage and processing; new architectures appear, based on edge computing including devices such as sensors and actuators. The data streams provided by sensors require to perform incremental reasoning tasks. Research questions remain on (i) how to embed reasoning in devices with various capacities, it requires specific optimisations; (ii) how to efficiently distribute reasoning tasks among devices. Smart agriculture is a typical application domain of such WoT architectures, where the surveillance of cultivated fields requires various sensors that push streaming data, which must be collected and reasoned upon to take decisions executed by actuators. Smart buildings is another such typical application domain where added-value application services involve other verticals such as energy management, e-health, or ageing well. We will define use cases and requirements for smart agriculture and smart buildings, run simulations, and then lead real experiments. The CoSWoT platform will foster the decoupling of the development of software and the development of hardware, so as to ease the emergence of a new economic sector in the digital industry around WoT applications development, disconnected from the development of the smart devices themselves.

Agronomy and biodiversity shall address several major societal, economical, and environmental challenges. However, data are being produced in such big volume and at such high pace, it questions our ability to transform them into knowledge and enable, for instance, translational agriculture i.e., rapidly and efficiently transferring results from agronomy research into the farms (“bench to farmside”). Semantic interoperability enables data integration and fosters new scientific discoveries by exploiting various data acquired from different perspectives and domains. D2KAB’s primary objective is to create a framework to turn agronomy and biodiversity data into –semantically described, interoperable, actionable, open– knowledge, along with investigating scientific methods and tools to exploit this knowledge for applications in science and agriculture. We will adopt an interdisciplinary semantic data science approach that will provide the means –ontologies and linked open data– to produce and exploit FAIR (Findable, Accessible, Interoperable, and Re-usable) data. To do so, we will develop original approaches and algorithms to address the specificities of our domain of interests, but also rely on existing tools and methods. D2KAB involves a multidisciplinary (and international) research consortium of three computer science labs (UM-LIRMM, CNRS-I3S, STANFORD-BMIR), four bioinformatics, biology, agronomy and agriculture labs (INRA-URGI, INRA-MaIAGE, INRA-IATE, IRSTEA-TSCF), two ecology and ecosystems labs (CNRS-CEFE, INRA-URFM), one scientific & technical information unit (INRA-DIST), and one association of agriculture stakeholders (ACTA). The consortium’s expertise ranges from ontologies and metadata, semantic Web, linked data, ontology alignment, knowledge reasoning and extraction, natural language processing to bioinformatics, agronomy, food science, ecosystems, biodiversity and agriculture. The project is structured with three work-packages of research and development in informatics and two work-packages of driving scenarios. WP1 will focus on ontologies/ vocabularies and turn the AgroPortal prototype into a reference platform that addresses the community needs and reaches a high level of quality regarding both content and services offered e.g., SKOS compliance, semantic search over linked data, text annotation, interoperability with other repositories. WP2 will focus on the critical issue of ontology alignment and develop new functionalities and state-of-the-art algorithms in AgroPortal using background knowledge methods validated in ag & biodiv. WP3 will design the methods and tools to reconcile the scenarios' heterogeneous ag & biodiv data sources and turn them into linked data within D2KAB distributed knowledge graph. It will also investigate exploitation of this graph through novel visualization, navigation and search methods. WP4 includes four interdisciplinary research driving scenarios implementing translational agriculture. For instances, an ontology-driven decision support system to select the most appropriate food packaging or an augmented semantic reader for Plant Health Bulletins. We will provide a unique scientific knowledge base for wheat phenotypes and offer the first agricultural data resource empowered by linked open data. WP5 will develop semantic resources for the annotation of ecosystem experiments data and functional biogeography observations. A plant trait-environment-relationships study will be conducted to understand the impacts of climatic changes on vegetation of the Mediterranean Basin. Within a dedicated work-package, we will focus on maximizing the impact of our research. Each of the project driving scenarios will produce concrete outcomes for ag & biodiv scientific communities and stakeholders in agriculture. We have planned multiple dissemination actions and events where we will use our driving scenarios as demonstrators of the potential of semantic technologies in agronomy and biodiversity.

This project MOBITER (“MOBIlité autonome en milieu tout TERrain”) aims to fully meet the ambitions of the MOBILEX challenge. For this, 2 SMEs (Sherpa Engineering / Logiroad) join their academic partners: TSCF INRAe, and Institut Pascal, confirming recent collaborations (Thesis, common laboratory, geographical proximity). This proximity allows to have already common links in the methodology of development (recourse to numerical models), tools (use of the middleware ROS), techniques of representation of the environment (Lambda-field) ... which will be necessary for the framework of this very applicative project, requiring a demonstration in the first year. The contributions are as follows: - Sherpa Engineering, coordinator of the project, will realize (on the basis of an existing), the hardware realization of a perception platform, totally adapted to the requirements, and to the specificity of the challenge. He will carry out the engineering tasks of the project, and will be in charge of a part of the algorithm. - INRAE will bring its experience in agricultural robotics, and will be responsible for the experimentation thanks to the use of its site of Montoldre (Allier). - The Pascal Institute will provide advice on scientific development (data fusion, traversability, risk management, planning), which will be implemented by an internal resource. - Logiroad will integrate its software module to classify the elements of the scene. The project will be carried out to increase the academics part in the software. Initially designed to manage functions at the state of the art level, via an adaptation of the existing modules, the vehicle will evolve during the 2 following challenges to better perceive the state of the terrain, to manage the passage of complex obstacles, to have redundancies to manage degraded environmental conditions or hardware faults. Thus, the basic technical definition Stereo Camera + Lidar 360° will be completed in the long term by a FMCW 360° radar. This project will allow the 2 SMEs to investigate in the Defense or Space sectors, but are also aimed the sector where the consortium is present: - The agricultural sector, requiring knowledge of a changing natural environment, with impact on the control. - The automotive sector, to allow the passage of autonomous vehicles in unmapped or poorly defined areas (especially rural areas).

The objectives of the FooSIN project are to establish and work within the recently proposed and endorsed GO FAIR Food Systems Implementation Network (IN) to 1) accelerate the implementation of FAIR principles in the agri-food community, and 2) position France as a leader in this evolution and make French actions and productions more visible at an international level. The Food Systems IN, co-led by Inra and Wageningen University and Research, gathers 22 major actors of the agriculture and nutrition domains worldwide, who commit to FAIR principles and collectively work for their wider and quicker adoption. As members of the Food Systems IN, we propose concrete actions towards the French community of people involved in data production and management for agriculture and food. We will organize a Bring-Your-Own-Data workshop (a.k.a datathon), seek for adapted training materials, and recommend tools and methodologies to FAIRify data and semantic resources, with the aim to leverage the FAIR awareness and skills, and the adoption of efficient tooling by our community. We will also propose original tools and services for data FAIRification to be adopted and disseminated by the Food Systems IN at the international level. These services and tools may also be transfered to other fields among the INs of the GO FAIR network.

Mobile robots are more and more efficient but limited by the problem of variation of motion properties, as is the case for applications in natural environments. Sharp transitions can be estimated reactively, but are difficult to predict, and lack of anticipation can lead to inappropriate or even hazardous behaviors. This project aims to overcome this problem by proposing adaptive mechanisms for robotic behavior by anticipating these variations from scene perception. The project proposes to develop machine learning approaches to predict and map the interaction conditions. It will also develop stable supervision processes to select and modify on-line several control modes. Tested on realistic scenarios using the robotic platforms available from the project members, such developments will strengthen the autonomy of robots to offer efficient and safe solutions to societal issues, particularly for agriculture.