In this paper, we developed a business model of Social Web of Services that combines the idea of common social web and usual service selling, enhancing usual suggested ways of controlling smart devices. We modelled human-thing interactions using an agent-based simulation (ABM) to investigate the impact of IoT on human (user) behaviour patterns in order to provide analytical support and enhance the analysed business model. Results of this work can be used to predict people's way of living in the era of Smart things observing viral effects of Things application.
The paper presents the results of the review and analysis of media content management systems on the basis of the mirrors. We present descriptive model and architecture of the developed system. The novelty consists in the development of a new device that allows users to interact with the Internet and manage media content using augmented reality. The article considers designed logical and physical model of system hardware and describes developed algorithms of the system. There are described a soft shell and new algorithms that significantly increase the amount of user interaction with his reflection, internet and media content using augmented reality.
In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her actions. A consumer chooses a scenario of home appliance use to balance her comfort level and the energy bill. We propose a Bayesian learning algorithm to estimate the comfort level function from the history of appliance use. In numeric experiments with datasets generated from a simulation model of a consumer interacting with small home appliances the algorithm outperforms popular regression analysis tools. Our approach can be extended to control an air heating and conditioning system, which is responsible for up to half of a household's energy bill.