Toward Translating Raw Indoor Positioning Data into Mobility Semantics

Indoor mobility analyses are increasingly interesting due to the rapid growth of raw indoor positioning data obtained from IoT infrastructure. However, high-level analyses are still in urgent need of a concise but semantics-oriented representation of the mobility implied by the raw data. This work studies the problem of translating raw indoor positioning data into mobility semantics that describe a moving object’s mobility event (What) someplace (Where) at some time (When). The problem is non-trivial mainly because of the inherent errors in the uncertain, discrete raw data. We propose a three-layer framework to tackle the problem. In the cleaning layer, we design a cleaning method that eliminates positioning data errors by considering indoor mobility constraints. In the annotation layer, we propose a split-and-match approach to annotate mobility semantics on the cleaned data. The approach first employs a density-based splitting method to divide positioning sequences into split snippets according to underlying mobility events, followed by a sem