Modeling and Monitoring of Indoor Populations using Sparse Positioning Data (Extension)

TL;DR

This paper introduces probabilistic models and learning-based estimators for continuous indoor population monitoring using sparse positioning data, improving accuracy and efficiency in large venues.

Contribution

It presents novel probabilistic modeling and estimators for indoor populations, along with a unified framework for continuous monitoring with caching and validity mechanisms.

Findings

Estimators outperform state-of-the-art methods.

Framework effectively reduces monitoring costs.

Experimental results validate accuracy and efficiency.

Abstract

In large venues like shopping malls and airports, knowledge on the indoor populations fuels applications such as business analytics, venue management, and safety control. In this work, we provide means of modeling populations in partitions of indoor space offline and of monitoring indoor populations continuously, by using indoor positioning data. However, the low-sampling rates of indoor positioning render the data temporally and spatially sparse, which in turn renders the offline capture of indoor populations challenging. It is even more challenging to continuously monitor indoor populations, as positioning data may be missing or not ready yet at the current moment. To address these challenges, we first enable probabilistic modeling of populations in indoor space partitions as Normal distributions. Based on that, we propose two learning-based estimators for on-the-fly prediction of population distributions. Leveraging the prediction-based schemes, we provide a unified continuous query processing framework for a type of query that enables continuous monitoring of populated partitions. The framework encompasses caching and result validity mechanisms to reduce cost and maintain monitoring effectiveness. Extensive experiments on two real data sets show that the proposed estimators are able to outperform the state-of-the-art alternatives and that the query processing framework is effective and efficient.