Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration

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dc.contributor.authorNaghdi, Sharareh
dc.contributor.authorO'Keefe, Kyle
dc.date.accessioned2022-06-09T21:22:25Z
dc.date.available2022-06-09T21:22:25Z
dc.date.issued2022-06-07
dc.description.abstractThe demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Indoor positioning approaches apply different types of sensors to increase the accuracy of the user’s position. Among these technologies, Bluetooth Low Energy (BLE) appeared as a popular alternative due to its low cost and energy efficiency. However, BLE faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This work presents a method to compensate RSSI values by applying Artificial Neural Network (ANN) algorithms to RSSI measurements from three BLE advertising channels and a wearable camera as an additional source of information for the presence or absence of human obstacles. The resulting improved RSSI values are then converted into ranges using path loss models, and trilateration is applied to obtain indoor localization. The proposed artificial system provides significantly better localization solutions than fingerprinting or trilateration using uncorrected RSSI values.en_US
dc.description.grantingagencyNatural Sciences and Engineering Research Council (NSERC)en_US
dc.identifier.citationNaghdi, S., & O’Keefe, K. (2022). Combining multichannel RSSI and vision with artificial neural networks to improve BLE trilateration. Sensors, 22(4320). https://doi.org/10.3390/s22124320en_US
dc.identifier.doi10.3390/s22124320en_US
dc.identifier.grantnumberCRDPJ 514520–17en_US
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/1880/114715
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/39813
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.publisher.departmentGeomatics Engineeringen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.hasversionpublishedVersionen_US
dc.publisher.institutionUniversity of Calgaryen_US
dc.publisher.policyhttps://www.mdpi.com/about/openaccessen_US
dc.rightsUnless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.subjectadvertising channelsen_US
dc.subjecttrilaterationen_US
dc.subjectBLEen_US
dc.subjectartificial intelligenceen_US
dc.subjectlocalizationen_US
dc.subjecthuman body shadowingen_US
dc.titleCombining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilaterationen_US
dc.typejournal articleen_US
ucalgary.item.requestcopyfalseen_US
ucalgary.scholar.levelFacultyen_US
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