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Wireless sensing devices are frequently used in smart spaces, ubiquitous and proactive computing, and situation awareness applications (Satyanarayanan 2001), (Vildjiounaite, Malm et al.), (Weiser), (Ilyas & Mahgoub). One could list a plethora of applications suitable for the use of wireless sensor networks and other sensing instruments, for instance, health care (wellness system for aging), environmental monitoring (pollution of air, water, and soil), atmospheric science (severe weather prediction), structural health monitoring (equipment or material fatigue detection), military surveillance (vehicle movement detection), facility monitoring (security and life-cycle of a facility), wild life monitoring (animal migration), or intelligent vehicle design (obstacle detection) (Dishman), (Gupta & Kumar), (Mainwaring, Polastre et al.), (Wang, Estrin et al.), (Roush, Goho et al.), (East), (Rom'an, Hess et al.), (Abowd), (Dey), (Kidd, Orr et al.). The list of on-going projects that include wireless sensor networks and other sensing instrumentation is also growing every day (see NSF, NIST and DARPA projects such as NSF NEON, LOOKING, SCCOOS, ROADNet, USArray, TeraBridge, ORION, CLEANER, NIST SHIELD or DARPA Active Networks, Connectionless networks, DTT). All projects have in common the fact that they represent multi-instrument and multi-sensor systems that can be characterized as smart outdoor, indoor or embedded spaces. The challenge is to build smart spaces that can intelligently sense environments, gather information, integrate information across disparate sensing systems over time, space and measurement, and finally detect and recognize events of interest to trigger event-driven actions. We have been interested in the hazard awareness application scenarios (Bajcsy, Johnson et al. 2008) (Bajcsy, Kooper et al. 2006) that concern humans due to (a) natural disastrous events, (b) failures of human hazard attention or (c) intentional harmful behaviors of humans. Our focus is on the problems related to building hazard aware spaces (HAS) to alert innocent people, similar to the problem related to swimming pool surveillance systems to prevent human drowning (e.g. Poseidon developed by Vision IQ). While building a real-time HAS system, one has to address the issues of (1) setting up the system to achieve desired accuracy and (2) operating it to achieve reliable performance with or without human intervention. In order to setup a HAS system, one ought to find ways

Integration of Data Across Disparate Sensing Systems over Both Time and Space to Design Smart Environments