Smart indoor localization using machine learning techniques
| dc.contributor.author | Ugave, Viney Anand, author | |
| dc.contributor.author | Pasricha, Sudeep, advisor | |
| dc.contributor.author | Anderson, Charles, committee member | |
| dc.contributor.author | Roy, Sourajeet, committee member | |
| dc.date.accessioned | 2007-01-03T06:51:08Z | |
| dc.date.available | 2007-01-03T06:51:08Z | |
| dc.date.issued | 2014 | |
| dc.description.abstract | The advancement of smartphone devices has led to a generation of new applications and solutions. These devices give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user's context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization whereas interest in location-based services is also rising. While numerous smartphone based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. More importantly, these techniques entail high-energy consumption that can quickly drain a smartphone battery. In this thesis, we propose innovative techniques based on machine learning algorithms and smart sensor management for effective Indoor Localization using smartphones. We evaluated our techniques on several indoor environments with diverse characteristics and show improvements over several state-of-the-art techniques from prior work. The extensive use of sensors and Wi-Fi scans can deplete the smartphone battery and so we quantitatively accounted for all the modules that consume the battery power. We also performed energy and accuracy tradeoff analysis to provide a broader understanding of how to smartly use these techniques. Furthermore, we investigated, implemented and tested both sensor and machine learning based techniques. Our best technique achieved an average accuracy between 1-3 meters across most of our evaluated indoor paths. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Ugave_colostate_0053N_12485.pdf | |
| dc.identifier.uri | http://hdl.handle.net/10217/84567 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | energy | |
| dc.subject | indoor | |
| dc.subject | localization | |
| dc.subject | navigation | |
| dc.subject | optimization | |
| dc.subject | smartphones | |
| dc.title | Smart indoor localization using machine learning techniques | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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