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Sound urban water management relies on extensive and reliable monitoring of water infrastructure. As low-cost sensors and networks have become increasingly available for environmental monitoring, urban water researchers and practitioners must consider the benefits and disadvantages of such technologies. In this perspective paper, we highlight six technical and socio-technological considerations for low-cost monitoring technology to reach its full potential in the field of urban water management, including: technical barriers to implementation, complementarity with traditional sensing technologies, low-cost sensor reliability, added value of produced information, opportunities to democratize data collection, and economic and environmental costs of the technology. For each consideration, we present recent experiences from our own work and broader literature and identify future research needs to address current challenges. Our experience supports the strong potential of low-cost monitoring technology, in particular that it promotes extensive and innovative monitoring of urban water infrastructure. Future efforts should focus on more systematic documenting of experiences to lower barriers to designing, implementing, and testing of low-cost sensor networks, and on assessing the economic, social, and environmental costs and benefits of low-cost sensor deployments.

This article explores the design, development and deployment of a digital platform for scholarly work at Notre Dame Cathedral and demonstrates the transformative impact of digital technology on heritage disciplines. By merging technology and human expertise, the platform facilitates the creation, integration, sharing, and analysis of extensive scientific data on the multidisciplinary post-fire study of the cathedral. This multi-layered approach includes community building for collaborative efforts, digital tools tailored to different stakeholders, data structuring approaches for managing multidimensional features, and experience-based workflows for documenting, categorising and semantically enriching scientific and restoration data. The overall goal is to introduce an integrated solution for collaborative studies and to promote a digital memory of the collective initiative in accordance with the principles of FAIR for scientific heritage data. This initiative not only supports the research and restoration of Notre Dame, but also serves as a paradigm for future conservation and documentation efforts in the field of cultural heritage. International audience

Mise en avant d'une partie des recherches récentes de l'utilisation d'images hyperspectrales pour l’étude des écosystèmes fluviaux à l’UMR 5600 EVS. Highlighting some of the recent research into the use of hyperspectral images for the study of river ecosystems at UMR 5600 EVS. National audience

The Internet of Things (IoT) is generally defined as a global infrastructure that enables advanced services by interconnecting physical and virtual things based on interoperable network technologies. It provides access to a wide range of applications, such as smart cities, smart agriculture, and asset tracking. The increase in the number and diversity of network technologies brings new challenges to IoT architects, who are in charge of the design and deployment of IoT solutions. Indeed, the selection and configuration of network technologies are crucial to ensure the good behavior of IoT applications. In this thesis, we address this issue through the development of a set of methods and associated tools that help IoT architects in their process of designing and deploying IoT solutions. First, we propose a generic framework for the performance evaluation of an IoT network technology given a specific application context. Second, we introduce a new algorithm relying on multi-criteria optimization methods that enables the automatic selection of the most adapted network technology for a given application context. Then, we propose a solution for optimizing the configuration of any IoT network technology using simulation models and regression methods. For the sake of reliability, we also explore the possibility of calibrating simulation models with data collected from real deployments, which may find a potential application on network digital twins. Finally, we implemented these methods in a no-code tool, integrated in the Stackilab platform, relying on simulation and aiming to help IoT architects easily future-proof an IoT solution.; L'Internet des objets (IoT) est une infrastructure mondiale interconnectant des objets physiques et virtuels grâce à des technologies de réseau interopérables. Il offre un large éventail d'applications dans des domaines tels que les villes intelligentes, l'agriculture intelligente et le suivi des actifs. Cependant, la diversité croissante des technologies de réseau pose des défis aux architectes IoT chargés de concevoir et déployer des solutions IoT. En effet, la sélection et la configuration des technologies de réseau sont cruciales pour assurer le bon fonctionnement des solutions IoT. Dans cette thèse, nous proposons un ensemble de méthodes et d'outils pour aider les architectes IoT dans leur processus de conception et de déploiement. D'abord, nous introduisons un canevas générique pour évaluer les performances des technologies de réseau IoT dans des contextes spécifiques. Ensuite, nous présentons un algorithme d'optimisation multi-critères pour sélectionner automatiquement la meilleure technologie de réseau pour une application donnée. De plus, nous proposons une solution pour optimiser la configuration des technologies de réseau IoT en utilisant des modèles de simulation et des méthodes de régression. Nous explorons également la possibilité de calibrer les modèles de simulation avec des données réelles pour une meilleure fiabilité, ce qui pourrait trouver une application potentielle sur les jumeaux numériques des réseaux. Enfin, nous avons implémenté ces méthodes dans un outil no-code intégré à la plateforme Stackilab, facilitant ainsi le déploiement durable des solutions IoT pour les architectes.

Understanding human-nature relationships has become both a theoretical and an operational issue with the increasing adoption of participatory approaches in environmental management. PPGIS methods are widely used for their ability to demonstrate these relationships as spatial patterns on a map and to reach a large and spatially distributed audience. The resulting spatial data are generally decoupled from participants' discourses, even though the latter can provide original and nuanced insights into what lies underneath the map, therefore human-nature relationships. Here, we conducted a web-mapping survey in Martinique (Lesser Antilles) to understand inhabitants’ relationships with nature. We proposed a quantitative method combining the spatial data sketched by inhabitants with their discourse data to investigate their perceptions of “rich nature”. Our results indicated a strong relationship between the “rich nature” perception and its spatial representation. The validity of the method was strengthened by good participation rates (n = 573) and geographical representativeness, although socio-demographic representativeness was not reached. This approach confirmed the added value of discourses in participatory mapping, hence supporting a PPGIS method that combines both mapping and participants' discourses. International audience

River rehabilitation and ecological engineering are becoming critical issues for improving river status when ecological habitats and connectivity have been altered by human pressures. Amongst the range of existing rehabilitation options, some specifically focus on rebuilding fluvial forms and improving physical processes. The aim of this contribution is to illustrate how geomorphological expertise and process-based thinking contribute to river rehabilitation success. This semantic contribution is intended to feed the rehabilitation debate, particularly concerning the design of actions and the proposed references for monitoring target reaches and evaluating rehabilitation effects empirically. This article is also based on lessons learned from practical cases, mainly in gravel-bed rivers. Geomorphic understanding is needed at a local level to achieve an adequate diagnosis of river functioning, estimate human impacts and potential remnant river responsiveness, and to assess the gains and risks from rehabilitation, as well as to appraise success or failure through several pre- and post-project assessment strategies. Geomorphological studies can also be upscaled in a top-down manner (from high-order controls to small-scale processes, understanding detailed processes in their regional or basin-wide context), providing large-scale information at the regional, national, or even global level, information that can be used to diagnose the health of riverscapes in relation to local site-specific contexts. As such, geomorphological studies support strategic planning and prioritization of rehabilitation works according to specific contexts and river responsiveness, so as to move from opportunistic to objective-driven strategies. International audience

The last decade has seen the explosion of the Internet of Things (IoT), which is enabling a range of new applications based on the connection of physical objects to the Internet. The growing diversity of IoT connectivity technologies is bringing new challenges to IoT solution designers. Indeed, it is increasingly difficult to choose and configure the network technology for a given use-case. In this article, we formalize and investigate the design optimization problem for selecting and configuring the IoT connectivity technology of an application that can evolve over time. Finding the right abstractions and the good balance between performance and evaluation complexity to compare networking options is a key research challenge. To address this problem we propose to separate the concerns of IoT application architects from those of network experts and to provide a methodology, HINTS, to help designers in making customized decision. HINTS combines IoT application requirements and goals abstraction, IoT network modeling, discrete-event network simulation and a multiple attribute decision making method. The application of the methodology on three use-cases highlights how it helps in (i) selecting the best network technology option, (ii) defining an appropriate configuration and (iii) anticipating the behavior when device density or workload intensity scales up. The main contribution of this paper is to propose the first formal approach and associated algorithm to automatically optimize the design of the IoT connectivity of an application. Results show that it can yield up to a factor two improvement in the solution performance. International audience

The Internet of Things (IoT) is transforming all economic sectors by connecting physical assets to the virtual world. The range of low-power connectivity options is continuously widening the range of possible IoT applications. However, too many possibilities often make it hard for industrial specialists to choose the right technology and configuration settings, yet these are crucial decisions. To deeply analyze and compare the performance and the scalability of various solution designs, one proven method is simulation. In this article, we show how IoT network simulation can help to future-proof an IoT connectivity design, without the burden of installing a lot of hardware and writing complicated scripts. Then, as the network simulation process is too complex for most IoT teams, we propose a no-code online IoT network simulation platform to make this powerful tool accessible to all. In particular, we explain how we hide the simulation workflow complexity via relevant abstractions and transform it into intuitive interactions. We illustrate the method and the usage of this promising approach, which can be integrated in a network digital twin. We show how it permits to easily evaluate what-if scenarios in order to answer a set of questions that may arise all along the life cycle of a smart connected solution. International audience

Precision Beekeeping is a subfield of Precision Agriculture. It collects data on colonies of bees using sensors to assist beekeepers and preserve bees. The existing solutions include collecting voluminous data, such as images and sound, from up close or inside a beehive. The collection, transfer, and processing of such data are energy-intensive processes. However, most Precision Beekeeping systems work under a limited energy budget. Thus, such systems must take this constraint into account. This article focuses on the placement of energy-aware Precision Beekeeping services at the edge and in the cloud. We deploy a set of smart beehives in order to conduct experiments and collect real data. Then, we simulate large-scale systems with embedded services to add more realism to the experimental study. Our results show that parameters like the number of smart beehives, the frequency of data collection, and the characteristics of the cloud server impact the placement of these services to enhance energy efficiency. International audience