Citizens Vote to Act: smart contracts for the management of water resources in smart cities

Smart cities leverage Information and Communication Technologies (ICTs) to enhance the quality of urban services. However, it is nowadays clear that the success of a smart city largely depends on the level of engagement of its citizens. In this paper we explore to what extent disruptive blockchain technologies can be used to incentivise the democratic participation of citizen. The investigated approach extends the standard IoT cycle 1) sense data, 2) cloudify and elaborate them, and 3) push information to the users. Here, the user takes an active role by means of data-informed votes on policies, therefore influencing behaviours. We illustrate such an approach by means of a proof-of-concept decentralised application (dApp) supporting the negotiation of polices for the management of urban water resources. The dApp consists of a smart contract that manages the execution of other smart contracts (the policies) according to the data-driven choices of the community. This use case demonstrates how suitably blockchain technologies can support fair and safe access to data and user engagement in smart cities

Scenarios for Educational and Game Activities using Internet of Things Data

Raising awareness among young people and changing their behavior and habits concerning energy usage and the environment is key to achieving a sustainable planet. The goal to address the global climate problem requires informing the population on their roles in mitigation actions and adaptation of sustainable behaviors. Addressing climate change and achieve ambitious energy and climate targets requires a change in citizen behavior and consumption practices. IoT sensing and related scenario and practices, which address school children via discovery, gamification, and educational activities, are examined in this paper. Use of seawater sensors in STEM education, that has not previously been addressed, is included in these educational scenaria

Delivering elder-care environments utilizing TV-channel based mechanisms

In this paper, we present a smart environment for elderly. What makes the development of such system challenging is that the concept of smartness for elderly brings to the extreme the idea of invisibility of the technology. In our experience, elders are well-disposed to new technologies, provided that those will not require significant changes - namely, they are invisible - to their habits. Starting from this consideration, 200 caregivers responses were collected by questionnaire, so as to better understand elders' needs and habits. A system was subsequently developed allowing elders to access a number of "modern web services" as standard TV channels: at channel 43 there is the health status, at channel 45 the photos of the family, at 46 the agenda of the week, just to mention few of the available services. The content of such services is automatically generated by the smart devices in the environment and is managed by the caregivers (e.g., family members) by simple web apps. Fourteen families were asked to install the system in their house. The results of these experiments confirm that the proposed system is considered effective and user-friendly by elders

eIDeCert: a user-centric solution for mobile identification

The necessity to certify one's identity for different purposes and the evolution of mobile technologies have led to the generation of electronic devices such as smart cards, and electronic identities designed to meet daily needs. Nevertheless, these mechanisms have a problem: they don't allow the user to set the scope of the information presented. That problem introduces interesting security and privacy challenges and requires the development of a new tool that supports user-centrity for the information being handled. This article presents eIDeCert, a tool for the management of electronic identities (eIDs) in a mobile environment with a user-centric approach. Taking advantage of existing eCert technology we will be able to solve a real problem. On the other hand, the application takes us to the boundary of what the technology can cope with: we will assess how close we are to the boundary, and we will present an idea of what the next step should be to enable us to reach the goal

Wireless Sensor Networks in Structural Health Monitoring: a Modular Approach

In this paper, we present the Modular Monitoring System (MMS), a low-power wireless architecture dedicated to Structural Health Monitoring (SHM) applications. Our solution features an easily customizable modular architecture, fulfilling the needs of many SHM applications. The MMS supports mesh network topology and offers excellent coverage and reliability, taking advantage of Wireless Sensor Networks (WSN) technology. In this preliminary work we show how the flexibility of our approach offers great advantages with respect to the current state-of-the-art systems dedicated to SHM

Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals

Artificial Intelligence-based (AI) analysis of large, curated medical datasets is promising for providing early detection, faster diagnosis, and more effective treatment using low-power Electrocardiography (ECG) monitoring devices information. However, accessing sensitive medical data from diverse sources is highly restricted since improper use, unsafe storage, or data leakage could violate a person's privacy. This work uses a Federated Learning (FL) privacy-preserving methodology to train AI models over heterogeneous sets of high-definition ECG from 12-lead sensor arrays collected from six heterogeneous sources. We evaluated the capacity of the resulting models to achieve equivalent performance compared to state-of-the-art models trained in a Centralized Learning (CL) fashion. Moreover, we assessed the performance of our solution over Independent and Identical distributed (IID) and non-IID federated data. Our methodology involves machine learning techniques based on Deep Neural Networks and Long-Short-Term Memory models. It has a robust data preprocessing pipeline with feature engineering, selection, and data balancing techniques. Our AI models demonstrated comparable performance to models trained using CL, IID, and non-IID approaches. They showcased advantages in reduced complexity and faster training time, making them well-suited for cloud-edge architectures.Comment: Preprint of International Symposium on Algorithmic Aspects of Cloud Computing (ALGOCLOUD) 202