Improving the sustainability of WiFi-enabled indoor localization systems through meta-heuristic based instance selection approach

Abstract

Due to rapid urban development, many indoor and semi-indoor structures, such as underground malls, metro stations, and bus-stops, have emerged. Traditional GNSS outdoor navigation fails in these locations. To meet urban citizens’ needs, we require sustainable ubiquitous localization solutions, leveraging existing WiFi infrastructure. In such applications, processing data at the edge within constrained environments is imperative. Many prior studies neglect the practical challenge of implementing machine learning-based edge device localization, as they assume reliance on cloud servers. Selecting well-distributed data instances that preserve location class boundaries is essential for effective training in such conditions. In this work, we propose an instance selection approach that is modeled by applying a modified Binary Particle Swarm Optimization (BPSO) technique. The k-differentiating neighbor-based measure is incorporated to determine instance hardness while exploring the solution space through BPSO. The work is implemented on three publicly available benchmark datasets that are collected from two university campuses and one shopping mall. The proposed work is found to have achieved an instance reduction of 35% with only 1%–2% decrease in the accuracy measurement and an appreciable error deviation metric of 2.78 m.