Multiple Model BLE-based Tracking via Validation of RSSI Fluctuations under Different Conditions

Of particular interest to this paper is indoor positioning via integration of information fusion, localization, and tracking technologies with Internet of Things (IoT) devices equipped with sensing, processing, and Bluetooth Low Energy (BLE) communication capabilities. In particular, the objective is development of advanced signal processing and machine learning solutions to micro-locate and track a person within a delimited physical space (e.g. building) using BLE locating infrastructure installed within this space. In this regard and as the first step, the paper focuses on evaluation and validation of RSSI fluctuations under different environmental conditions. Therefore, the first goal of the paper is to implement a Location-Based Services (LBS) platform consisting of two main sub-systems, i.e., acquisition sub-system, and the Fusion Centre (FC). The second goal of the paper is to test and validate effects of different parameters on the RSSI values and on tracking performance. Based on real experiments, the implemented LBS platform shows potential capabilities for incorporation of different fusion frameworks and providing accurate tracking results.