Indiana University-Purdue University Indianapolis (IUPUI) / Indoor tracking is a challenging problem. The level of accepted error is on a much
smaller scale than that of its outdoor counterpart. While the global positioning system has
become omnipresent, and a widely accepted outdoor tracking system it has limitations in
indoor environments due to loss or degradation of signal. Many attempts have been made
to address this challenge, but currently none have proven to be the de-facto standard. In
this thesis, we introduce the concept of opportunistic tracking in which tracking takes
place with whatever sensing infrastructure is present – static or mobile, within a given
indoor environment. In this approach many of the challenges (e.g., high cost, infeasible
infrastructure deployment, etc.) that prohibit usage of existing systems in typical
application domains (e.g., asset tracking, emergency rescue) are eliminated. Challenges
do still exist when it comes to provide an accurate positional estimate of an entities
location in an indoor environment, namely: sensor classification, sensor selection, and
multi-sensor data fusion. We propose an enhanced tracking framework that through the
infusion of QoS-based selection criteria of trust and reliability we can improve the overall
accuracy of the tracking estimate. This improvement is predicated on the introduction of
learning techniques to classify sensors that are dynamically discovered as part of this opportunistic tracking approach. This classification allows for sensors to be properly
identified and evaluated based upon their specific behavioral characteristics through
performance evaluation. This in-depth evaluation of sensors provides the basis for
improving the sensor selection process. A side effect of obtaining this improved accuracy
is the cost, found in the form of system runtime. This thesis provides a solution for this
tradeoff between accuracy and cost through an optimization function that analyzes this
tradeoff in an effort to find the optimal subset of sensors to fulfill the goal of tracking an
object as it moves indoors. We demonstrate that through this improved sensor
classification, selection, data fusion, and tradeoff optimization we can provide an
improvement, in terms of accuracy, over other existing indoor tracking systems.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/11816 |
| Date | January 2016 |
| Creators | Rybarczyk, Ryan Thomas |
| Contributors | Raje, Rajeev |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Detected Language | English |
| Type | Thesis |
| Rights | Attribution-NonCommercial-NoDerivs 3.0 United States, http://creativecommons.org/licenses/by-nc-nd/3.0/us/ |
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