An information-theoretic, optimal framework is developed for tracking the residents in a Context-aware Heterogenous Smart Environment. The resident-tracking problem is formulated in terms of weighted entropy. The framework provides an optimal, online learning and prediction of users movement, location as well as most probable path segments from the symbolic domain. Successful prediction helps in on-demand operations of automated indoor devices along the users future paths and locations, thus providing the necessary comfort at a near-optimal cost. Simulation results corroborate the high prediction success, thereby providing resident-comfort while reducing energy consumption and manual operations.