LiBertaS: Enhancing Location Based Services by tackling the market barrier of costly data collection requirements of fingerprinting positioning systems

LiBertaS aims to liberate the access of Location Based Services (LBS) to the highly accurate fingerprinting methods in business practice, by greatly downscaling the data volume needed for their operation. Location Based Services (LBS) have recently undergone a tremendous increase in popularity. Despite the proven superiority of the fingerprinting methods in terms of localization accuracy, their requirement for an extensive data collection and the related costs, proves to be the main barrier preventing their market adoption.

LiBertaS plans to tackle this issue following a multifaceted approach. LiBertaS will operate as a synergistic power that will study the optimal combination of previous findings, linking pieces in a puzzle, to construct a first-of-its-kind comprehensive framework. In LiBertaS, we will study, evolve and combine 3 main research directions in a synergistic manner. We will explore the individual and combined capabilities of three innovative concepts: (i) estimate selection based on the Dynamic Accuracy Estimation, (ii) Proximity-based fingerprint augmentation, and (iii) Generative Modeling. The work plan of LiBertaS aims to bring the relevant research line of our work full circle.

LiBertaS will combine our previous contributions, which were mainly produced within the Eratosthenes project. More particularly, LiBertaS builds on top of these works:

Dr. Grigorios Anagnostopoulos, research associate of the dmml group, is the grantee of the LiBertaS project. LiBertaS is funded under the Small Projects funding scheme of the Halser Foundation, who is gratefully acknowledged by the grantee.

Related previous projects:

 


Results at a glance:

  • The work "Efficient Fingerprint Augmentation Evaluation on the Antwerp LoRaWAN Setting" aims to evaluate whether the fingerprint Augmentation method's performance remains relevant in different datasets and whether it outperforms published works in those settings. To set the ground, it work offers a detailed analysis of the usage of the Antwerp LoRaWAN datasets in the relevant literature, discussing localization methods, evaluation methodologies, and comparing performances. Subsequently, this work (i) evaluates ProxyFAUG's capacity to improve the performance of the voluminous training set, while it also (ii) explores its capacity to improve label efficiency in settings of severe data scarcity. Results indicate a significant reduction in the median localization error (19%) when the full training set gets augmented by ProxyFAUG. Moreover, the results demonstrate that the performance obtained when using the full original training set can be matched by only using 40% of the training data to feed ProxyFAUG. The work uses Open Data and provides all described methods and experimentation as Open Code, promoting transparency of its claims and facilitating reproducibility of research results.