Deep Learning Coupled with Multiple-Model Adaptive Estimation for Indoor Localization/Tracking in IoT Applications******

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This NSERC-ENGAGE project aims to develop/design a localization/tracking solutions based on their Bluetooth Low Energy (BLE) enabled devices for dormakaba Canada Inc., the industrial partner. dormakaba Canada Inc., designs and manufactures access control systems such as electronic locks and readers and is one of the top three companies offering access control and security solutions in Canada and across the world incorporated in variety of venues such as hotels, airports, hospitals, and in the office environment. The advantage of leveraging their devices for indoor localization/tracking is that it avoids duplicating infrastructures and devices for their customers. Despite having the infrastructure in place, currently dormakaba Canada Inc. does not have localization/tracking solutions developed based on their BLE enabled devices. This NSERC-ENGAGE project aims to address this gap. In particular, dormakaba Canada Inc. is looking to find a solution to micro-locate and track a person within a delimited physical space (e.g. building) using their BLE-enabled locks and readers installed within this space in conjunction with a mobile device. In this regard, initially and based on the BLE technologies provided by dormakaba Canada Inc., a sensor network will be installed throughout a test corridor. Positional data will be collected to train deep neural learning architectures (application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) will be investigated) to construct the Received Signal Strength Indicator (RSSI) map of the selected venue. The trained models are then coupled and integrated within a dynamic multi-model estimation framework to improve the accuracy of indoor localization.**