QoI-Aware Mobile Crowdsensing for Metaverse by Multi-Agent Deep Reinforcement Learning

Abstract

Metaverse is expected to provide mobile users with emerging applications both in regular situation like intelligent transportation services and in emergencies like wireless search and disaster response. These applications are usually associated with stringent quality-of-information (QoI) requirements like throughput and age-of-information (AoI), which can be further guaranteed by using unmanned aerial vehicles (UAVs) as aerial base stations (BSs) to compensate the existing 5G infrastructures. In this paper, we consider a new QoI-aware mobile crowdsensing (MCS) campaign by UAVs which move around and collect data from mobile users wearing metaverse devices. Specifically, we propose "MetaCS", a multi-agent deep reinforcement learning (MADRL) framework with improvements on a Transformer-based user mobility prediction module between regions and a relational graph learning mechanism to enable the selection of most informative partners to communicate for each UAV. Extensive results and trajectory visualizations on three real mobility datasets in NCSU, KAIST and Beijing show that MetaCS consistently outperforms six baselines in terms of overall QoI index, when varying different numbers of UAVs, throughput requirement, and AoI threshold.