IoT-TD: IoT Dataset for Multiple Model BLE-based Indoor Localization/Tracking
Mohammad Salimibeni, Zohreh Hajiakhondi-Meybodi, Parvin Malekzadeh, Mohammadamin Atashi, Konstantinos N. Plataniotis, Arash Mohammadi
Abstract
Bluetooth Low Energy (BLE) is one of the key enabling technologies of the emerging Internet of Things (IoT) concept. When it comes to BLE-based dynamic indoor tracking, however, due to drastic fluctuations of the Received Signal Strength Indicator (RSSI), highly acceptable accuracies are not yet achieved. Although very recent introduction of BLE v 5.1 promises prosperous future for BLE-based dynamic tracking, the following two key issues are in the path: (i) Despite of being in the age of big data with huge emphasis on reproducibility of research, there is no unified dataset with precise ground truth available for performing dynamic BLE-based tracking, and; (ii) The main focus of existing works are on utilization of stand-alone models. The paper addresses these gaps. At one hand, we introduce a reliable dataset, referred to as the IoT-TD, leveraging specific set of four optical cameras to provide ground truth with millimeter accuracies. The introduced IoT-TD dataset consists of RSSI values collected from five BLE sensors together with synchronized Inertial Measurement Unit (IMU) signals from the target's mobile device. On the other hand, the paper introduces a multiple-model dynamic estimation framework coupling RSSI-based particle filtering with IMU-based Pedestrian Dead Reckoning (PDR). Experimental results based on the IoT-TD dataset corroborate effectiveness of multiple modeling fusion frameworks for providing enhanced BLE-based tracking accuracies.