Browsing by Author "Rybarczyk, Ryan"

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    e-DOTS: AN INDOOR TRACKING SOLUTION
    (Office of the Vice Chancellor for Research, 2012-04-13) Rybarczyk, Ryan; Raje, Rajeev; Tuceryan, Mihran
    Accurately tracking an object as its moves in a large indoor area is at-tractive due to its applicability to a wide range of domains. For example, a typical healthcare setup may benefit from tracking their assets, such as spe-cialized equipment, in real-time and thus optimize their usage. Existing techniques, such as the GPS, that focus on outdoor tracking do not provide accurate estimations of location within the confines of an indoor setup. Prev-alent approaches that attempt to provide the ability to perform indoor track-ing primarily focus on a homogenous type of sensor when providing an esti-mation of an object’s location. Such a homogeneous view is neither benefi-cial nor sufficient due to specific characteristics of single type of sensors. This research aims to create a distributed tracking system composed out of many different kinds of inexpensive and off-the-shelf sensors to address this challenge. Specifically, the proposed system, called Enhanced Distributed Object Tracking System (e-DOTS), will incorporate sensors such as web cameras, publically available wireless access points, and inexpensive RFID tracking tags to achieve accurate tracking over a large indoor area in real-time. As an object, in addition to moving in a known indoor setup, may move through an unknown confined area, the e-DOTS needs to incorporate opportunistic discovery of available sensors, select a proper subset of them, and fuse their readings in real-time to achieve an accurate estimation of the current position of that object. A preliminary prototype of e-DOTS has been created and experimented with. The results of these validations are promis-ing and suggest the possibility of e-DOTS achieving its desired goals. Further research is aimed at incorporating different kinds of sensors, different fusion techniques (e.g., Federated Kalman Filtering) and various discovery mecha-nisms to improve the tracking accuracy and the associated response time.
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    Heuristic Based Sensor Ranking Algorithm for Indoor Tracking Applications
    (Office of the Vice Chancellor for Research, 2013-04-05) Rybarczyk, Ryan; Raje, Rajeev R.; Tuceryan, Mihran
    Location awareness in an indoor setup is an important function necessary in many application domains such as asset management, critical care, and augmented reality. Location awareness, or tracking, of an object within an indoor setting requires a high degree of accuracy, as room-to-room location may be very important. With the current proliferation of smart devices, with often a multitude of built-in sensors, and inexpensive sensors it is now possible to build a network of sensors, for the purpose of tracking, within an indoor environment without the high cost of installing the needed tracking infrastructure. In an effort to increase accuracy, as well as coverage area, various different sensors may be used in the tracking of an object. In this heterogeneous tracking situation, it is important for the tracking infrastructure to quickly and accurately decide which, all or a subset, of available sensors to use. Challenges related to heterogeneous data fusion and clock synchronization, must be addressed in order to provide accurate location estimates. We have proposed a heuristic based ranking algorithm to address these challenges. In this algorithm, the individual sensors are ranked based upon their quality of service (QoS) attributes and the resulting ranking is used by a filtering service during the sensor selection process. This information is provided to the filtering service when a sensor joins the tracking infrastructure and is subsequently only updated during idle periods, thereby, there avoiding additional overhead. We have implemented this algorithm into the existing prototypical Enhanced Distributed Object Tracking System or e-DOTS. e-DOTS has been extensively experimented with and the results of these experimentation validate the hypothesis that accurate indoor tracking can be achieved using a heterogeneous ensemble of cheap and mobile sensors. Our current investigation involves the incorporation of trust associated with sensors and deploying e-DOTS in a typical healthcare setup.
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    Poster: Infusing Trust in Indoor Tracking
    (ACM, 2016-06) Rybarczyk, Ryan; Raje, Rajeev; Tuceryan, Mihran; Department of Computer & Information Science, School of Science
    An indoor tracking system is inherently an asynchronous and distributed system that contains various types (e.g., detection, selection, and fusion) of events. One of the key challenges with regards to indoor tracking is an efficient selection and arrangement of sensor devices in the environment. Selecting the "right" subset of these sensors for tracking an object as it traverses an indoor environment is the necessary precondition to achieving accurate indoor tracking. With the recent proliferation of mobile devices, specifically those with many onboard sensors, this challenge has increased in both complexity and scale. No longer can one assume that the sensor infrastructure is static, but rather indoor tracking systems must consider and properly plan for a wide variety of sensors, both static and mobile, to be present. In such a dynamic setup, sensors need to be properly selected using an opportunistic approach. This opportunistic tracking allows for a new dimension of indoor tracking that previously was often infeasible or unpractical due to logistic or financial constraints of most entities. In this paper, we are proposing a selection technique that uses trust as manifested by its a quality-of-service (QoS) feature, accuracy, in a sensor selection function. We first outline how classification of sensors is achieved in a dynamic manner and then how the accuracy can be discerned from this classification in an effort to properly identify the trust of a tracking sensor and then use this information to improve the sensor selection process. We conclude this paper with a discussion of results of this implementation on a prototype indoor tracking system in an effort to demonstrate the overall effectiveness of this selection technique.