Elements of information theory
Elements of information theory
DOMINO: databases fOr MovINg Objects tracking
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
The anatomy of a context-aware application
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Location Aware Resource Management in Smart Homes
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Modeling coping behavior in virtual humans: don't worry, be happy
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Challenge: ubiquitous location-aware computing and the "place lab" initiative
Proceedings of the 1st ACM international workshop on Wireless mobile applications and services on WLAN hotspots
CARP: Context-Aware Resource Provisioning for Multimedia over 4G Wireless Networks
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
IEEE Transactions on Information Theory
A distributed location system for the active office
IEEE Network: The Magazine of Global Internetworking
Integrating encrypted mobile agents with smart homes
NTMS'09 Proceedings of the 3rd international conference on New technologies, mobility and security
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An information-theoretic, optimal framework is developed for tracking the residents in a Context-aware Heterogenous Adaptive Smart Environments (CHASE). 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 weighted entropy. The framework is truly universal and provides an optimal, online learning and prediction of inhabitants movement (location) profiles from the symbolic domain. Since the optimal tracking in heterogeneous smart homes is a NP-complete problem, a greedy heuristic for near-optimal tracking is proposed. The concept of Asymptotic Equipartition Property (AEP) is also explored to predict the inhabitants most likely path-segments (comprising of coverage areas of different sensor-systems) with very good accuracy. Successful prediction helps in on-demand operations of automated indoor devices along the inhabitants future paths and locations, thus providing the necessary comfort at a near-optimal cost. Simulation results on a typical smart home corroborate this high prediction success, thereby providing sufficient resident-comfort while reducing the daily energy consumption and manual operations.