NDLI: Infusing trust in indoor tracking: poster

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Infusing trust in indoor tracking: poster

Content Provider	ACM Digital Library
Author	Tuceryan, Mihran Rybarczyk, Ryan Raje, Rajeev
Abstract	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.
Starting Page	374
Ending Page	377
Page Count	4
File Format	PDF
ISBN	9781450340212
DOI	10.1145/2933267.2933538
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-06-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Accuracy Sensors Indoor tracking Subset selection Trust
Content Type	Text
Resource Type	Article

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