  Artificial Intelligence of Things (AIoT) is an emerging frontier based on the
deep fusion of Internet of Things (IoT) and Artificial Intelligence (AI)
technologies. Although advanced deep learning techniques enhance the efficient
data processing and intelligent analysis of complex IoT data, they still suffer
from notable challenges when deployed to practical AIoT applications, such as
constrained resources, and diverse task requirements. Knowledge transfer is an
effective method to enhance learning performance by avoiding the exorbitant
costs associated with data recollection and model retraining. Notably, although
there are already some valuable and impressive surveys on transfer learning,
these surveys introduce approaches in a relatively isolated way and lack the
recent advances of various knowledge transfer techniques for AIoT field. This
survey endeavors to introduce a new concept of knowledge transfer, referred to
as Crowd Knowledge Transfer (CrowdTransfer), which aims to transfer prior
knowledge learned from a crowd of agents to reduce the training cost and as
well as improve the performance of the model in real-world complicated
scenarios. Particularly, we present four transfer modes from the perspective of
crowd intelligence, including derivation, sharing, evolution and fusion modes.
Building upon conventional transfer learning methods, we further delve into
advanced crowd knowledge transfer models from three perspectives for various
AIoT applications. Furthermore, we explore some applications of AIoT areas,
such as human activity recognition, urban computing, multi-robot system, and
smart factory. Finally, we discuss the open issues and outline future research
directions of knowledge transfer in AIoT community.
