Modern ubiquitous computing environments are increasingly populated with smart devices that need to know the identity of users interacting with them. At the same time, the number of authentications that a user needs to perform increases, as nowadays devices such as smart TVs require authentication which was not the case in earlier times. Even for single-person households, the need to authenticate against present smart devices in the environment appears at regular intervals, ranging from TVs to voice assistants, to gaming consoles. To reduce the need for repeated authentication, we explore the concept of a system that allows the sharing of users’ authenticated identity information between smart devices, similar to the concept of Single-Sign-On on the internet. Following a preliminary field study, we show that such a system can decrease the number of necessary authentications in a ubiquitous computing environment by 84.4%, increasing usability and security.

Energy harvesting, batteryless devices can mitigate many shortcomings of battery-operated devices in the coming years, particularly in the area of the Internet of Things. Solar energy is an accessible and effective source of power for such devices. However, the uncontrollable nature of solar energy makes the task of designing and evaluating energy harvesting devices a challenging one. In this work, we propose PROGNOES, a tool that allows the user to forecast the harvestable solar energy by a particular solar cell at a specific time and location. To carry out the forecasts, various algorithms and models are used not only to provide the theoretical solar irradiation but also to provide the weather condition data corresponding to the time and location of the forecasting. PROG-NOES can generate forecasts in the form of IV curves, similar to those generated by physical solar cells. Such IV curves can then be emulated with suitable tools to provide energy traces to the energy harvesting devices at design time, therefore facilitating the task of designing robust, reliable energy harvesting, batteryless devices. In our experiments, we find that the energy forecasts carried out by PROGNOES can reach a mean absolute error of 7.16 mW.