Semantic communication-based convolutional neural network for enhanced image classification
In AI-IoT environments, the traditional centralized cloud computing approach leads to high network transmission volumes and communication delays, negatively affecting intelligent task performance. This study addresses these issues by introducing an innovative semantic communication system model for...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Franklin Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186324001221 |
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| author | Nivine Guler Zied Ben Hazem |
| author_facet | Nivine Guler Zied Ben Hazem |
| author_sort | Nivine Guler |
| collection | DOAJ |
| description | In AI-IoT environments, the traditional centralized cloud computing approach leads to high network transmission volumes and communication delays, negatively affecting intelligent task performance. This study addresses these issues by introducing an innovative semantic communication system model for intelligent tasks, leveraging deep learning techniques. The research focuses on image classification tasks constrained by bandwidth and delay in AI-IoT scenarios. The proposed model features a tailored semantic communication network architecture, where image feature maps are extracted on IoT devices. These semantic relations are then compressed based on the extracted feature maps to reduce power consumption on IoT devices and mitigate communication transmission pressures. Simulations and comparative analyses of various network performance metrics show that the proposed semantic communication system improves image classification accuracy by 90%% at low signal-to-noise ratios compared to traditional methods. With an 80%% compression ratio, the classification accuracy loss is minimal—within 2%%—when the signal-to-noise ratio exceeds 0. Additionally, at a signal-to-noise ratio of 20, the semantic compression transmission scheme enhances classification accuracy by 30%% compared to random compression. Moreover, the proposed system outperforms traditional approaches in execution time by approximately 80%%. |
| format | Article |
| id | doaj-art-fcc4dffbb5384335964469ba821029b3 |
| institution | Kabale University |
| issn | 2773-1863 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj-art-fcc4dffbb5384335964469ba821029b32024-12-19T11:03:35ZengElsevierFranklin Open2773-18632024-12-019100192Semantic communication-based convolutional neural network for enhanced image classificationNivine Guler0Zied Ben Hazem1Department of Computer Science, University of Central Asia, 722918 Naryn, Kyrgyzstan; Corresponding author.Automation and Sustainability Research Centre (ASRC), Department of Mechatronics Engineering, College of Engineering (COE), University of Technology Bahrain, Salmabad, BahrainIn AI-IoT environments, the traditional centralized cloud computing approach leads to high network transmission volumes and communication delays, negatively affecting intelligent task performance. This study addresses these issues by introducing an innovative semantic communication system model for intelligent tasks, leveraging deep learning techniques. The research focuses on image classification tasks constrained by bandwidth and delay in AI-IoT scenarios. The proposed model features a tailored semantic communication network architecture, where image feature maps are extracted on IoT devices. These semantic relations are then compressed based on the extracted feature maps to reduce power consumption on IoT devices and mitigate communication transmission pressures. Simulations and comparative analyses of various network performance metrics show that the proposed semantic communication system improves image classification accuracy by 90%% at low signal-to-noise ratios compared to traditional methods. With an 80%% compression ratio, the classification accuracy loss is minimal—within 2%%—when the signal-to-noise ratio exceeds 0. Additionally, at a signal-to-noise ratio of 20, the semantic compression transmission scheme enhances classification accuracy by 30%% compared to random compression. Moreover, the proposed system outperforms traditional approaches in execution time by approximately 80%%.http://www.sciencedirect.com/science/article/pii/S2773186324001221IoTCNNOptimization algorithmSemantic communication |
| spellingShingle | Nivine Guler Zied Ben Hazem Semantic communication-based convolutional neural network for enhanced image classification Franklin Open IoT CNN Optimization algorithm Semantic communication |
| title | Semantic communication-based convolutional neural network for enhanced image classification |
| title_full | Semantic communication-based convolutional neural network for enhanced image classification |
| title_fullStr | Semantic communication-based convolutional neural network for enhanced image classification |
| title_full_unstemmed | Semantic communication-based convolutional neural network for enhanced image classification |
| title_short | Semantic communication-based convolutional neural network for enhanced image classification |
| title_sort | semantic communication based convolutional neural network for enhanced image classification |
| topic | IoT CNN Optimization algorithm Semantic communication |
| url | http://www.sciencedirect.com/science/article/pii/S2773186324001221 |
| work_keys_str_mv | AT nivineguler semanticcommunicationbasedconvolutionalneuralnetworkforenhancedimageclassification AT ziedbenhazem semanticcommunicationbasedconvolutionalneuralnetworkforenhancedimageclassification |