A 3D Mobile Crowdsensing Framework for Sustainable Urban Digital Twins

TL;DR

This paper introduces a comprehensive 3D mobile crowdsensing framework for creating sustainable urban digital twins, combining gamified active sensing, passive data collection, spatial management, and real-time visualization, validated through real-world experiments.

Contribution

It presents a novel integrated framework that combines active and passive 3D crowdsensing models with spatial management and dynamic point cloud integration for urban digital twins.

Findings

Effective data collection verified through subjective evaluation.

Successful integration of point clouds into digital twins demonstrated.

Framework's components perform well in real-world experiments.

Abstract

In this article, we propose a 3D mobile crowdsensing (3D-MCS) framework aimed at sustainable urban digital twins (UDTs). The framework comprises four key mechanisms: (1) the 3D-MCS mechanism, consisting of active and passive models; (2) the Geohash-based spatial information management mechanism; (3) the dynamic point cloud integration mechanism for UDTs; and (4) the web-based real-time visualizer for 3D-MCS and UDTs. The active sensing model features a gamified 3D-MCS approach, where participants collect point cloud data through an augmented reality territory coloring game. In contrast, the passive sensing model employs a wearable 3D-MCS approach, where participants wear smartphones around their necks without disrupting daily activities. The spatial information management mechanism efficiently partitions the space into regions using Geohash. The dynamic point cloud integration mechanism incorporates point clouds collected by 3D-MCS into UDTs through global and local point cloud registration. Finally, we evaluated the proposed framework through real-world experiments. We verified the effectiveness of the proposed 3D-MCS models from the perspectives of subjective evaluation and data collection and analysis. Furthermore, we analyzed the performance of the dynamic point cloud integration using a dataset.