Identifying High-Traffic Patterns in the Workplace with Radio Tomographic Imaging in 3D Wireless Sensor Networks
AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT
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The rapid progress of wireless communication and embedded mircro-sensing electro-mechanical systems MEMS technologies has resulted in a growing confidence in the use of wireless sensor networks WSNs comprised of low-cost, low-power devices performing various monitoring tasks. Radio Tomographic Imaging RTI is a technology for localizing, tracking, and imaging device-free objects in a WSN using the change in received signal strength RSS of the radio links the object is obstructing. This thesis employs an experimental indoor three-dimensional 3-D RTI network constructed of 80 wireless radios in a 100 square foot area. Experimental results are presented from a series of stationary target localization and target tracking experiments using one and two targets. Preliminary results demonstrate a 3-D RTI network can be effectively used to generate 3-D RSS-based images to extract target features such as size and height, and identify high-traffic patterns in the workplace by tracking asset movement.
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