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.