@PHDTHESIS{ 2025:406324241,
 	title = {Site Survey-free Indoor Positioning System based on RSSI Diversity and Sequential Least Squares Programming},
 	year = {2025},
 	url = "https://tede.ufam.edu.br/handle/tede/11139",
 	abstract = "Indoor positioning systems (IPSs) are essential for enabling location-based services
 in complex environments such as retail, healthcare, and logistics. However,
 existing IPSs often require labor-intensive and time-consuming offline fingerprinting
 phases that limit scalability and adaptability in dynamic indoor settings.
 This thesis presents OPTIMAPS (Optimized Positioning Technique Integrating
 Model-based and Pairwise Selection), a novel site survey-free solution leveraging
 Bluetooth Low Energy (BLE) technology. OPTIMAPS utilizes a log-distance path
 loss model, with parameters automatically identified from scenario geometry
 and signal diversity analysis, thus eliminating the need for pre-deployment RSSI
 collection. During online operation, the system combines a nearest-neighbor
 (NN) estimation — chosen for its computational efficiency and robustness as
 an initializer—with Sequential Least Squares Programming (SLSQP) for constrained
 nonlinear optimization, refining position estimates within a restriction
 circle empirically matched to testbed granularity. A major innovation in OPTIMAPS
 is the adoption of the Chebyshev metric for quantifying RSSI vector
 dissimilarity, which, together with mean pairwise distance analysis, leads to
 superior signal discrimination and enhances positioning accuracy. The system
 was rigorously validated in a large-scale real-world scenario — a 720 m² testbed
 with 15 BLE access points and 148 testing locations — using a publicly available
 dataset. OPTIMAPS achieved an average positioning error (APE) of 2.65
 meters, competitive with state-of-the-art techniques, and outperformed comparable
 survey-free methods on anchor density-normalized error. Furthermore,
 OPTIMAPS demonstrated linear computational scaling with environmental size,
 a critical advantage over metaheuristic optimization approaches such as genetic
 algorithms, which exhibit quadratic or worse growth in resource demand.
 Spatially resolved performance analysis revealed that the SLSQP refinement
 yields the greatest improvements in complex, multipath-rich environments, with
 error reductions surpassing 30% in the most challenging rooms, and negligible
 overhead in open spaces. Effect size quantification and statistical testing further
 established OPTIMAPS’ robustness and practical deployability. Overall,
 OPTIMAPS proves to be a scalable, accurate, and energy-efficient solution that
 eliminates the need for site surveys, positioning it as a highly promising option
 for real-world deployment across diverse and dynamic indoor scenarios.",
 	publisher = {Universidade Federal do Amazonas},
 	scholl = {Programa de Pós-graduação em Informática},
 	note = {Instituto de Computação}
}