Automated selection of remote control user interfaces in pervasive smart spaces

Pervasive computing offers a vision in which every room in the home and at work will become an interactive smart space filled with wireless and wired devices whose behavior can be remotely controlled by a user, including monitors, speakers, printers, kiosks, soda machines, appliances, toys, sensors, and networked software services. The goal of this paper is to address what we term the active device resolution problem in pervasive smart spaces. Given a user with a wireless PDA or cell phone who wishes to remotely control one of N devices or services within a smart room, which of the N devices is the intended or “active” device whose user interface (UI) should be automatically selected and prefetched before the user manipulates the remote control PDA? Automated selection of the UI eases the user’s interaction with a pervasive environment, in comparison to manual selection, which is cumbersome, and directional remote control, e.g. pointing and clicking, which is insufficient to resolve the active device ambiguity when a group of devices is closely spaced, as in a home entertainment system. Our goal is to anticipate the next user interface desired by the user based on each user’s history of remote control accesses. We present a study of various prediction algorithms applied to user behavior, including first and second order Markov prediction as well as naïve Bayesian prediction, and show that the accuracy of prediction can range from 75-90%, depending upon the algorithm and the degree of training.