Scalable Semantic Adaptive Communication for Task Requirements in WSNs
Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost effectiveness, and ease of deployment. The rapid advancement of the Internet of Things (IoT), Artificial Intelligence (AI), and sixth-generation mobile c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2823 |
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| author | Hong Yang Xiaoqing Zhu Jia Yang Ji Li Linbo Qing Xiaohai He Pingyu Wang |
| author_facet | Hong Yang Xiaoqing Zhu Jia Yang Ji Li Linbo Qing Xiaohai He Pingyu Wang |
| author_sort | Hong Yang |
| collection | DOAJ |
| description | Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost effectiveness, and ease of deployment. The rapid advancement of the Internet of Things (IoT), Artificial Intelligence (AI), and sixth-generation mobile communication technology (6G) and Mobile Edge Computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices, and changed the image sensing and understanding to novel modes (such as machine-to-machine or human-to-machine interactions). However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. To address these issues, this paper proposes a Scalable Semantic Adaptive Communication (SSAC) for task requirement. Firstly, we design an Attention Mechanism-based Joint Source Channel Coding (AMJSCC) in order to fully exploit the correlation among semantic features, channel conditions, and tasks. Then, a Prediction Scalable Semantic Generator (PSSG) is constructed to implement scalable semantics, allowing for flexible adjustments to achieve channel adaptation. The experimental results show that the proposed SSAC is more robust than traditional and other semantic communication algorithms in image classification tasks, and achieves scalable compression rates without sacrificing classification performance, while improving the bandwidth utilization of the communication system. |
| format | Article |
| id | doaj-art-e2b6461c62164a0d8f3a7d8f4d6ad2f7 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e2b6461c62164a0d8f3a7d8f4d6ad2f72025-08-20T02:59:08ZengMDPI AGSensors1424-82202025-04-01259282310.3390/s25092823Scalable Semantic Adaptive Communication for Task Requirements in WSNsHong Yang0Xiaoqing Zhu1Jia Yang2Ji Li3Linbo Qing4Xiaohai He5Pingyu Wang6College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610065, ChinaWireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost effectiveness, and ease of deployment. The rapid advancement of the Internet of Things (IoT), Artificial Intelligence (AI), and sixth-generation mobile communication technology (6G) and Mobile Edge Computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices, and changed the image sensing and understanding to novel modes (such as machine-to-machine or human-to-machine interactions). However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. To address these issues, this paper proposes a Scalable Semantic Adaptive Communication (SSAC) for task requirement. Firstly, we design an Attention Mechanism-based Joint Source Channel Coding (AMJSCC) in order to fully exploit the correlation among semantic features, channel conditions, and tasks. Then, a Prediction Scalable Semantic Generator (PSSG) is constructed to implement scalable semantics, allowing for flexible adjustments to achieve channel adaptation. The experimental results show that the proposed SSAC is more robust than traditional and other semantic communication algorithms in image classification tasks, and achieves scalable compression rates without sacrificing classification performance, while improving the bandwidth utilization of the communication system.https://www.mdpi.com/1424-8220/25/9/2823adaptive sensingscalable semanticsattention mechanismtask requirements |
| spellingShingle | Hong Yang Xiaoqing Zhu Jia Yang Ji Li Linbo Qing Xiaohai He Pingyu Wang Scalable Semantic Adaptive Communication for Task Requirements in WSNs Sensors adaptive sensing scalable semantics attention mechanism task requirements |
| title | Scalable Semantic Adaptive Communication for Task Requirements in WSNs |
| title_full | Scalable Semantic Adaptive Communication for Task Requirements in WSNs |
| title_fullStr | Scalable Semantic Adaptive Communication for Task Requirements in WSNs |
| title_full_unstemmed | Scalable Semantic Adaptive Communication for Task Requirements in WSNs |
| title_short | Scalable Semantic Adaptive Communication for Task Requirements in WSNs |
| title_sort | scalable semantic adaptive communication for task requirements in wsns |
| topic | adaptive sensing scalable semantics attention mechanism task requirements |
| url | https://www.mdpi.com/1424-8220/25/9/2823 |
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