Natural disasters are more and more often present in our daily life. Many are the cases where these events affect people and economies. In this context, there is the need for a technological intervention in support of first responders, with solutions capable of make decisions on the disaster areas. Indeed, considering these scenarios are time-sensitive, the intention is moving the computation units closer to those areas. In this paper, we propose a computing continuum architecture for offloading distributed intelligences over cloud, edge and deep edge layers. Exploiting the federated learning paradigm, enables mobile and stationary devices to independently train local models, contributing to the creation of the global common model.

During laparoscopic surgery, carbon dioxide is insufflated for expanding the abdominal volume. However, the abdominal compliance identifies the critical point over the Pressure-Volume (P-V) curve beyond which the patient is exposed to harmful effects. Calculating this point is, therefore, critical. We aim to model a P-V curve for pediatric patients by fitting a sigmoidal equation with the Levenberg-Marquardt algorithm.

EDGEmergency is a platform designed for disaster management that can dynamically leverage the edge infrastructure potentially already present within the emergency perimeter. Edge devices, from IoT to smartphones, possess an increasingly significant computational capacity that can be exploited, by changing their behavior in real-time and creating a pervasive local environment, capable of adapting perfectly to the specific context of reference. EDGEmergency, in fact, allows the creation of a unified computation environment leveraging the Cloud-Edge-Client Continuum concept, through which a computation cluster with zero configurations is created on-the-fly. The platform thus allows the deployment of distributed microservices on existing edge devices, installed by default for other purposes, through a modular and incremental logic that has the role of adapting best to the needs of the individual emergency, through advanced tools for analysis and monitoring, using artificial intelligence.

The use of the Federated Learning paradigm could be disruptive in robotics, where data are naturally distributed among teams of agents and centralizing them would increase latency and break privacy. Unfortunately there are a lack of robot oriented framework for federated learning that use state of the art machine learning libraries. ROS2 (Robot Operating Systems) is a standard de-facto in robotics for building up teams of robots in a multi-node fully distributed manner. In this paper we presents the integration of ROS2 with PyTorch allowing an easy training of a global machine learning model starting from a set of local datasets. We present the architecture, the used methodology and finally we discuss the experimentation results over a well-known public dataset.

We propose the OpenSEM hardware/software framework as an holistic tool to build structural and environmental monitoring platforms for public decision makers. After the definition of a minimal set of system requirements, we show in this poster the main contributions in terms of hardware design, data synchronization and storage.

Internet of Things (IoT) became more and more popular because of the raise of ubiquitous internet connected devices. In this regard, IoT nodes are often organized in wireless sensor networks to facilitate communication and perform a coral computation. Such a network is often employed in urban or rural areas, i.e., for traffic, fires, and floods monitoring. Nodes are, therefore, deployed in remote areas, preventing the possibility to frequently access them, i.e., for firmware update. In this context, over-the-air (OTA) firmware update is used to remotely change the behavior of one or more nodes. In this paper, we firstly build a wireless mesh network with microcontrollers (i.e., ESP32) and, therefore, propose a secure filtered O TA firmware update involving firmware freshness (i.e., quarantine when firmware is not up-to-date), key pairing, and digital signature for data integrity and non-repudiation. The system is evaluated in terms of deactivation time $(s)$, energy consumption $(kWh)$, and greenhouse gases $(\mathrm{kgCO}_{2}\mathrm{e})$, highlighting good results in terms of scalability for grouped updates.