Intelligent task-oriented semantic communication method in artificial intelligence of things
With the integration and development of Internet of things (IoT) and artificial intelligence (AI) technologies, traditional data centralized cloud computing processing methods are difficult to effectively remove a large amount of redundant information in data, which brings challenges to the low-late...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2021-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021214/ |
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| Summary: | With the integration and development of Internet of things (IoT) and artificial intelligence (AI) technologies, traditional data centralized cloud computing processing methods are difficult to effectively remove a large amount of redundant information in data, which brings challenges to the low-latency and high-precision requirements of intelligent tasks in the artificial intelligence of things (AIoT).In response to this challenge, a semantic communication method oriented to intelligent tasks in AIoT was proposed based on the deep learning method.For image classification tasks, convolutional neural networks (CNN) were used on IoT devices to extract image feature maps.Starting from semantic concepts, semantic concepts and feature maps were associated to extract semantic relationships.Based on the semantic relationships, semantic compression was implemented to reduce the pressure of network transmission and the processing delay of intelligent tasks.Experimental and simulation results show that, compared with traditional communication scheme, the proposed method is only about 0.8% of the traditional scheme, and at the same time it has higher classification task performance.Compared with the scheme that all feature maps are transmitted, the transmission delay of the proposed method is reduced by 80% and the effective accuracy of image classification task is greatly improved. |
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| ISSN: | 1000-436X |