  The advent of sixth-generation (6G) mobile networks introduces two
groundbreaking capabilities: sensing and artificial intelligence (AI). Sensing
leverages multi-modal sensors to capture real-time environmental data, while AI
brings powerful models to the network edge, enabling intelligent
Internet-of-Things (IoT) applications. These features converge in the
Integrated Sensing and Edge AI (ISEA) paradigm, where edge devices collect and
locally process sensor data before aggregating it centrally for AI tasks. Point
clouds (PtClouds), generated by depth sensors, are crucial in this setup,
supporting applications such as autonomous driving and mixed reality. However,
the heavy computational load and communication demands of PtCloud fusion pose
challenges. To address these, the FlyCom$^2$ framework is proposed, optimizing
distributed PtCloud fusion through on-the-fly communication and computing,
namely streaming on-sensor processing, progressive data uploading integrated
communication-efficient AirComp, and the progressive output of a global PtCloud
representation. FlyCom$^2$ distinguishes itself by aligning PtCloud fusion with
Gaussian process regression (GPR), ensuring that global PtCloud representation
progressively improves as more observations are received. Joint optimization of
local observation synthesis and AirComp receiver settings is based on
minimizing prediction error, balancing communication distortions, data
heterogeneity, and temporal correlation. This framework enhances PtCloud fusion
by balancing local processing demands with efficient central aggregation,
paving the way for advanced 6G applications. Validation on real-world datasets
demonstrates the efficacy of FlyCom$^2$, highlighting its potential in
next-generation mobile networks.
