An algorithm for three-dimensional indoor positioning based on Bayesian inference, Fingerprinting method and Wi-Fi technology

Nascimento, Hitalo Joseferson Batista; Cavalcanti, Francisco R. P.; Rodrigues, Emanuel B.; Paiva, Antonio

doi:10.22161/ijaers.4.10.26

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Subsequently, in the online phase, the smart device gathers the RSS from the APs and sends it to the server to compare the predefined fingerprint of the offline phase with the RSS data in the online phase in order to estimate the location on the grid map [27]. Different machine learning algorithms are suggested and used in order to compare offline and online data, such as k-nearest neighbors (KNNs) [28], weighted KNN [29], neural network [30], recurrent neural network (RNN) [31], and Naïve Bayes [32], among others. The positioning algorithm and the quality of observations can impact the performance of these positioning techniques.…”

Section: Wps Techniquesmentioning

confidence: 99%

Developing A Conceptual Passive Contact Tracing System for Commercial Buildings Using WiFi Indoor Positioning

Vosoughkhosravi

Jafari

2022

Sustainability

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Contact tracing is one of the critical tools for fighting against pandemic disease outbreaks, such as the fast-growing SARS-CoV-2 virus and its different variants. At present, automated contact tracing systems face two main challenges: (1) requiring application installation on smart devices and (2) protecting the users’ privacy. This study introduces a conceptual passive contact tracing system using indoor WiFi positioning to address these challenges and investigate the role of such a system in commercial buildings. In this regard, this study uses a simulated small-office layout in a case study to demonstrate the applicability of the proposed system. The special use of the proposed contact tracing system could be academic facilities and office buildings, where (1) the WiFi infrastructure already exists and therefore implementing such a system could be cost-effective, and (2) the same users use the facility regularly, enabling the system to notify the users upon a confirmed case once they are back in the building and connected to the WiFi system. Such technology can not only enhance the current automated contact tracing system in commercial buildings by illuminating the need to use smartphone applications while protecting users’ privacy, but could also reduce the risk of infection in indoor environments. The developed system can benefit facility managers, business owners, policy makers, and authorities in assisting to find occupants’ high-risk contacts and control the spread of SARS-CoV-2 or similar infectious diseases in commercial buildings, particularly university campuses and office buildings.

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