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| Title | Lightweight Sensing-Computing-Decision Collaboration Enhancement For Multi-Mobile Terminals; [多移动终端轻量化感 – 算 – 策协同增强方法] |
|---|---|
| ID_Doc | 35278 |
| Authors | Gao Y.; Liu S.; Guo B.; Xu X.; Bian H.; Hao J.; Xu W.; Yu Z. |
| Year | 2024 |
| Published | Scientia Sinica Informationis, 54, 9 |
| DOI | http://dx.doi.org/10.1360/SSI-2024-0089 |
| Abstract | In recent years, with the integration of the Internet of Things (IoTs) and artificial intelligence (AI) 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. © 2024 Science China Press. All rights reserved. |
| Author Keywords | cross-layer optimization of heterogeneous systems; data fusion perception; deep model scalability offloading; intelligent Internet of Things; mutual feedback decision of large and small models |
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