Indoor localization supporting smartphone advertising

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dc.contributor.advisorNickerson, Bradford
dc.contributor.advisorSong, Wei
dc.contributor.authorSeo, Amy
dc.date.accessioned2023-03-01T16:18:24Z
dc.date.available2023-03-01T16:18:24Z
dc.date.issued2016
dc.date.updated2023-03-01T15:01:28Z
dc.description.abstractThis thesis presents an indoor advertising server called Adaptive Real-Time Advertising Server (ARTAS) that provides advertisements to customer smartphones walking inside a retail store. Ultra-wideband (UWB) systems installed in the retail store locate the tag carried by the customer. ARTAS reads position data from UWB systems, and ranks advertisements based on the customer's position. Ranked advertisements are then sent to the customer smartphone. ARTAS was tested under line-of-sight (LOS) and non-line-of-sight (NLOS) environments with 2 experiments carried out for each environment. A simulated store with 25 advertising zones, and a simulated customer walking with a smartphone were used to carry out the tests. ARTAS system variables including position update and advertisement rates, advertisement delay, minimum advertising area radius, and time scale factor were changed for 18 test cases to determine variables giving better advertisement ranking. These experiments demonstrate that for LOS experiments, correct advertisements are top ranked 44.4% to 48.6% of the time, while for a NLOS environment correct advertisements are top ranked only 33.3% to 35.7% of the time. If the ranking order is ignored, then correct advertisements are ranked 64.1% to 68.3% of the time for a LOS environment, and 50% to 51.6% for a NLOS environment. A second technique (called the next zone method) used advertisements from the customer's current and predicted next advertising zones to compute the number of correctly shown advertisements. Using the next zone method, top ranked advertisements are shown correctly 59.2% to 62.7% of the time for the LOS environment, and 41.2% to 42.9% of the time for the NLOS environment.
dc.description.copyright© Amy Y.J. Seo, 2016
dc.formattext/xml
dc.format.extentxvii, 165 pages
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/13422
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineComputer Science
dc.titleIndoor localization supporting smartphone advertising
dc.typemaster thesis
thesis.degree.disciplineComputer Science
thesis.degree.fullnameMaster of Computer Science
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.C.S.

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