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Integration of Data across Disparate Sensing Systems Over Both Time and Space to Design Smart Environments.This work addresses a set of general problems during a design of smart spaces. The problems are related to knowing where sensing takes place, at what time, of what measurement type and what the interpretation of acquired measurements is for event detection, recognition and proactive event-driven action purposes. The work outlines multi-instrument and sensor measurement systems that provide sensing capabilities for smart spaces, as well as theoretical and practical limitations that have to be understood while working with novel sensing technologies. The disparate sensing systems include wireless micro-electro-mechanical systems (MEMS) sensor networks (such as MICA sensors by Crossbow Inc.) and cameras that capture a variety of spectra (such as visible, thermal infrared and hyperspectral information). The sensors represent a mobile system over both time and space by engaging a robot with its robotic arm. The robot performs remotely controlled sensor deployment, and adaptive sensor placement based on the sensing feedback. Our solution addresses:
Our prototype solution demonstrates primarily the integration of data across disparate sensing systems over both time and space. 
Figure 1: An overview of the current prototype. We applied the prototyped solutions to hazard detection and human alert in the context of hazard aware spaces (HAS). The goal of HAS is to alert people in the event of dangers like natural disasters, failures of human hazard attention, and intentionally harmful human behavior. Our prototype HAS system integrates both emerging and standard sensing technologies in order to deliver sensor and camera data streams to a central location. At the central location, our data analysis algorithms detect hazards to alert humans, and possibly analyze hazard sources. Once alerted decision makers know when, where and what events occur, they can use the gesture/voice/keyboard controlled robot to confirm the presence of hazards using sensor and video feedback, as well as to attempt to contain the hazards with the robot. Localization using Passive RFID Technology.We investigate the use of passive RFID technology for object localization and tracking. This work is motivated by the desire to design, and build, robust smart spaces (pervasive spaces), and in particular their application in the area of hazard aware spaces. In particular we are interested in building spaces that can detect hazards and autonomously take action to gain more information about the hazard and alert the users of the space. To enable a robot to operate in such a space, accurate localization and tracking must be carried out, allowing the robot to then sense and detect hazards and alert humans about when, where, and what hazards occur. We evaluate the strengths and weaknesses of passive RFID technology as a solution to this problem. In addition, we have researched and developed a methodology for building a sensor model which is required for accurate localization. We applied one specific method of performing probabilistic localization that demonstrated successful global localization of a robot using these approaches. 
Figure 2: Tag types used in our experimental analysis. 
Figure 3: Left: documentation of setup used to build the sensor model. Right: Image showing nominal detection range when Tags are distributed on the floor and the Reader is mounted on the robot and tilted forward 30 degrees. The 4th "row" has a radius of roughly 90 cm. 
Figure 4: Experimental setup. The Alien Reader was mounted on the platform of a robot (left). The RFID tags were placed on the floor in a regular grid pattern (right). 
Figure 5: These images show three time steps in the middle of an experimental run. Open circles represent undetected tags. Filled blue circles represent detected tags. The red circle represents the average robot location, and a green circle represents a ground truth measurement. People, Publications, Presentations
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This material is based upon work partially supported by the ONR funding. We acknowledge NCSA/UIUC support of this work. Conference Papers
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