Crowdsensing-based Wireless Indoor Localization using an Innovative AI & ML Algorithm

Smartphone based indoor navigation services are desperately needed in an indoor GPS-denied environment, such as in Combat-zone Surveillance, Health Monitoring, Fire Detection, etc. The Receive Signal Strength (RSS) based algorithms are commonly used in indoor localization, which rely on the WiFi fingerprint data built by the Mobile Crowdsensing approach. Conventional statistical and probability techniques are used to deal with crowdsensing-based RRS fingerprinting information, but there are some issues such as low localization accuracy, highly relevance to the device/software they used, large database required, unfriendly to new encountered smartphone, etc. The proposed project will develop a novel integrated approach that combines AI technology (e.g., Artificial Neural Networks) and ML methods (e.g., Feed-forward Multilayer Perceptron Regressor and Support Vector Machine) to solve aforementioned problems, as well as build a crowdsensing-based RRS Wi-Fi fingerprinting dataset in a university building in Regina, SK Canada for indoor positioning studies

Faculty Supervisor:

Wei Peng


Xiaoning Fei


Ericsson Canada


Engineering - mechanical



University of Regina



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