Aplikace na platformě Windows 10 IoT pro získávání dat ze sensorů

The paper describes the creation of Windows IoT applications in Windows 10, specifically on mobile device for collecting and displaying data obtained from sensors connected to the Arduino development board. This article describes all the steps necessary to create an application in C #, development tools, hardware devices, and libraries in a compact summary which forms the algorithm of the implementation of Windows Universal Application on so-called IoT devices from different manufacturers.Příspěvek popisuje tvorbu aplikace v prostředí Windows 10, konkrétně na mobilním zařízení pro sběr a zobrazení dat získaných ze sensorů připojených na vývojovou desku Arduino. Příspěvek popisuje všechny nezbytné kroky k vytvoření aplikace v jazyce C#, vývojové prostředky, hw zařízení a knihovny v uceleném souhrnu, který tvoří postup implementace Windows Universal Apps na tzv. IoT zařízení

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

The Internet of Hackable Things

The Internet of Things makes possible to connect each everyday object to the Internet, making computing pervasive like never before. From a security and privacy perspective, this tsunami of connectivity represents a disaster, which makes each object remotely hackable. We claim that, in order to tackle this issue, we need to address a new challenge in security: education

Development of IoT applications in civil engineering classrooms using mobile devices

This is the peer reviewed version of the following article: [Chacón R, Posada H, Toledo Á, Gouveia M. Development of IoT applications in civil engineering classrooms using mobile devices. Comput Appl Eng Educ. 2018;26:1769–1781. https://doi.org/10.1002/cae.21985], which has been published in final form at https://doi.org/10.1002/cae.21985. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-ArchivingThis paper presents academic efforts aimed at integrating methodologies associated with the use of mobile devices, the potential of the Internet of Things (IoT), and the role of experimental education in civil engineering. This integration is developed by encompassing the use of sensors, microcontrollers, civil engineering problems, app development, and fabrication. The proposal provides an explorative way of approaching the numerous possibilities that arise in civil engineering when it comes to IoT, automation, monitoring, and control of civil engineering processes. The used tools represent accessible and affordable ways for application in classrooms and in educational laboratories for beginners. The initial explorative approach implies the fusion of three realms: (i) the phenomenology and mathematics of varied civil engineering problems; (ii) the systematic use of digital fabrication technologies and electronic prototyping platforms; and (iii) the creative and visual way of developing codes provided by block-based development platforms. This integration of perspectives is an attempt of approaching civil engineering mathematics to technology and arts with a rigorous scientific approach. A set of different examples is presented with the corresponding findings in educational terms. These examples are developed in a constructive, scaffolding-based way and may contribute as a potential alternative in the development of open-source teaching labs in civil engineering schools.Peer ReviewedPostprint (author's final draft

Peekaboo: A Hub-Based Approach to Enable Transparency in Data Processing within Smart Homes (Extended Technical Report)

We present Peekaboo, a new privacy-sensitive architecture for smart homes that leverages an in-home hub to pre-process and minimize outgoing data in a structured and enforceable manner before sending it to external cloud servers. Peekaboo's key innovations are (1) abstracting common data pre-processing functionality into a small and fixed set of chainable operators, and (2) requiring that developers explicitly declare desired data collection behaviors (e.g., data granularity, destinations, conditions) in an application manifest, which also specifies how the operators are chained together. Given a manifest, Peekaboo assembles and executes a pre-processing pipeline using operators pre-loaded on the hub. In doing so, developers can collect smart home data on a need-to-know basis; third-party auditors can verify data collection behaviors; and the hub itself can offer a number of centralized privacy features to users across apps and devices, without additional effort from app developers. We present the design and implementation of Peekaboo, along with an evaluation of its coverage of smart home scenarios, system performance, data minimization, and example built-in privacy features.Comment: 18 page