On June 9th, Pavel defended his PhD thesis entitled ‘Collaborative Technique for Indoor Positioning Systems‘ under the supervision of Sven Casteleyn and Joaquín Torres along with Tampere University professors Simona Lohan and Jary Nurmi. This thesis is part of the cohort of ESR from the European Joint Doctorate Marie Sklodowska-Curie in A Network for Dynamic Wearable Applications with Privacy Constraints (A-WEAR).

Pavel’s work provides a number of contributions in the field of collaborative techniques of wearables devices to enhance the position accuracy of traditional indoor positioning systems in real-world conditions. Therefore, the main motivation behind Pavel’s doctoral thesis is to address the limitations of these traditional indoor positioning systems for human positioning based on RSSI (Received Signal Strength Indicator), which often suffer from low accuracy due to signal fluctuations and hardware heterogeneity, and deployment cost constraints, considering the advantages provided by the ubiquity of mobile devices and collaborative and machine learning-based techniques.

Pavel’s research over the past 3 years focused on developing and evaluating mobile device-based collaborative indoor techniques, using Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs). As part of the research activities, Pavel conducted an impressive systematic review to analyse what’s going on in the field (see below).

The results demonstrate the usefulness and usability of  Collaborative Indoor Positioning Systems to improve the positioning accuracy of traditional Indoor Positioning Systems, namely Indoor Positioning Systems based on BLE– lateration, BLE–fingerprinting, and Wi-Fi–fingerprinting under specific conditions. Mainly, conditions where the collaborative devices have short and medium distances between them. The integration of MLP ANNs model in Collaborative Indoor Positioning Systems allows to use Pavel’s approach under different scenarios and technologies, showing its level of generalizability, usefulness, and feasibility.

Overall, Pavel’s work provides a relevant number of novel and efficient solutions well beyond the state-of-the-art in the field of collaborative techniques to improve indoor positioning. Enjoy the party. You deserve it!