Date of Award
3-14-2014
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Electrical and Computer Engineering
First Advisor
Richard K. Martin, PhD.
Abstract
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.
AFIT Designator
AFIT-ENG-14-M-24
DTIC Accession Number
ADA602463
Recommended Citation
Danella, Thea S., "Identifying High-Traffic Patterns in the Workplace With Radio Tomographic Imaging in 3D Wireless Sensor Networks" (2014). Theses and Dissertations. 596.
https://scholar.afit.edu/etd/596