Title: Context-Aware Resource Management in Multi-Inhabitant Smart Homes:
A Nash H-Learning based Approach
Authors: Nirmalya Roy, Abhishek Roy and Sajal K Das
Abstract: A smart home aims at building intelligence automation with a goal to
provide its inhabitants with maximum possible comfort, minimize the
resource consumption and thus overall cost of maintaining the home.
"Context Awareness" is perhaps the most salient feature of such an
intelligent environment. Clearly, an inhabitant's mobility and
activities play a significant role in defining his contexts in and
around the home. Although there exists an optimal algorithm for
location and activity tracking of a single inhabitant, the
correlation and dependence between multiple inhabitants' contexts
within the same environment make the location and activity tracking
more challenging. In this paper, we first prove that the optimal
location prediction across multiple inhabitants in smart homes is an
NP-hard problem. Next, to capture the correlation and interactions
between different inhabitants' movements (and hence activities), we
develop a novel framework based on a game theoretic, Nash
H-learning approach that attempts to minimize the joint location
uncertainty. Our framework achieves a Nash equilibrium such that no
inhabitant is given preference over others. This results in more
accurate prediction of contexts and more adaptive control of
automated devices, thus leading to a mobility-aware resource (say,
energy) management scheme in multi-inhabitant smart homes.
Experimental results demonstrate that the proposed framework is
capable of adaptively controlling a smart environment, thus reduces
energy consumption and enhances the comfort of the inhabitants.
Biography: Nirmalya Roy is a PhD student at Department
of Computer Science and Engineering, University of Texas at Arlington.
He is a member of CReWMaN Lab. from 2002 and his advisor is Professor Sajal K. Das.