Near-optimal tracking for residents' comfort in context-aware heterogeneous smart environments

An information-theoretic, optimal framework is developed for tracking the residents in a context-aware heterogeneous smart environment. The framework envisions that each individual sensor system operates fairly independently and does not require public knowledge of individual topologies. The resident-tracking problem is formulated in terms of a new concept of weighted entropy. The framework is truly universal and provides an optimal, online learning and prediction of inhabitant's movement (location) profiles from the symbolic domain. The overall optimal tracking in heterogeneous smart homes is proved to be an NP-complete problem, and a greedy heuristic for near-optimal tracking is proposed. The concept of asymptotic equipartition property is also explored to predict the inhabitant's most likely path segments (comprising coverage areas of different sensor systems) with very good accuracy. Successful prediction helps in on-demand operations of automated indoor devices along the inhabitant's future paths and locations, thus providing the necessary comfort at a near-optimal cost. Simulation results on a typical smart home corroborate a high prediction success of ∼91%, thereby providing sufficient resident-comfort (≥7 in the scale of 10) while reducing the daily energy consumption and manual operations to less than one-third of its original values.