In recent years,with the integration of the Internet of Things(IoTs)and artificial intelligence(Al)technologies,the concept of artificial intelligence in IoT(AIoT)has gradually emerged as a prominent frontier.Against this backdrop,deep learning-driven intelligent applications are increasingly permeating various domains such as smart cities and public safety.To extend intelligent computing from the cloud to IoT terminals and edge devices,the collaborative efforts of multiple mobile terminal devices in AIoT face challenges including limited available resources and dynamic environmental changes.In AIoT,multiple mobile terminals possess ubiquitous perception,intelligent computing,and autonomous decision-making capabilities,participating in the processes of perception,computation,learning,and decision-making.This paper proposes a collaborative enhancement method for lightweight perception,computation,and decision-making among multiple mobile terminals,aiming to overcome the limitations of single-terminal perspectives,resources,and performance,improve perception coverage and computational efficiency,and enhance task performance in various application scenarios.
关键词
智能物联网/数据融合感知/深度模型伸缩卸载/大小模型互馈决策/异构系统跨层优化
Key words
intelligent Internet of Things/data fusion perception/deep model scalability offloading/mutual feedback decision of large and small models/cross-layer optimization of heterogeneous systems
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