A semantic secure communication approach for resource-constrained power IoT terminals

YANG Huifeng; SHANG Li; CUI Junbin; LIU Hongyan; WANG Jiucheng; LIN Peng

doi:10.11959/j.issn.2096-3750.2025.00455

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A semantic secure communication approach for resource-constrained power IoT terminals

Internet of Energy Things | 更新时间：2025-09-12

- A semantic secure communication approach for resource-constrained power IoT terminals
- Chinese Journal on Internet of Things Vol. 9, Issue 2, Pages: 107-116(2025)
- 作者机构：
  
  1.国网河北省电力有限公司信息通信分公司，河北石家庄 050051
  2.北京万可信息技术有限公司，北京 100085
- 作者简介：
- 基金信息：
  
  The Science and Technology Project of State Grid Hebei Electric Power Co., Ltd(SGHEXT00GCJS2310140)
- DOI：10.11959/j.issn.2096-3750.2025.00455
  CLC： TN919.8
- Received：08 October 2024，
  
  Revised：2024-12-09，
  
  Published：10 June 2025
- 稿件说明：
移动端阅览
杨会峰,尚立,崔俊彬等.面向资源受限电力物联网终端的语义安全通信方法[J].物联网学报,2025,09(02):107-116.

YANG Huifeng,SHANG Li,CUI Junbin,et al.A semantic secure communication approach for resource-constrained power IoT terminals[J].Chinese Journal on Internet of Things,2025,09(02):107-116.
杨会峰,尚立,崔俊彬等.面向资源受限电力物联网终端的语义安全通信方法[J].物联网学报,2025,09(02):107-116. DOI： 10.11959/j.issn.2096-3750.2025.00455.

YANG Huifeng,SHANG Li,CUI Junbin,et al.A semantic secure communication approach for resource-constrained power IoT terminals[J].Chinese Journal on Internet of Things,2025,09(02):107-116. DOI： 10.11959/j.issn.2096-3750.2025.00455.

摘要

语义模型训练通常需要耗费大量能量和时间，阻碍了在资源受限的电力物联网终端上实施语义传输。为减轻终端的能耗和时耗，建立了一种新的语义通信架构。首先，将待传数据上传至机器学习即服务（MLaaS

machine learning as a service）平台；然后，在MLaaS平台完成语义模型训练并将模型参数回传给终端；最后，通过终端进行语义推理。然而，该架构存在MLaaS平台泄露语义模型参数导致语义信息被窃听的问题。因此，进一步设计了基于特征混淆的抗窃听方法以解决语义推理阶段的MLaaS平台安全通信问题。实验结果表明，所提方法在面对被动窃听者时是有效的，能够在保证合法终端图像恢复质量的同时，显著降低窃听者恢复图像的成功率。此外，还初步验证了特征混淆模块在终端上的计算开销和时延，结果显示该方法在资源受限的电力物联网终端上具有实际应用可行性。

Abstract

The training of semantic models typically consumes a large amount of energy and time

which hinders the implementation of semantic transmission on power Internet of things (IoT) terminals with limited resources. To reduce the energy and time consumption at the terminal

a novel semantic communication architecture was proposed. Firstly

the data to be transmitted was uploaded to a machine learning as a service (MLaaS) platform. Then

the semantic model training was performed on the MLaaS platform

and the model parameters were sent back to the terminal. Finally

semantic reasoning was carried out through the terminal. However

this architecture faces the risk of the MLaaS platform leaking semantic model parameters

leading to the potential eavesdropping of semantic information. Therefore

an anti-eavesdropping method based on feature obfuscation was designed to address the security communication issue of the MLaaS platform during the semantic inference phase. Experimental results show that the proposed method is effective against passive eavesdroppers. The effectiveness of the proposed anti-eavesdropping method was demonstrated. Additionally

the computational overhead and latency of the data obfuscation module on terminals have been preliminarily verified

showing that the method is feasible for practical application on resource-constrained power IoT terminals.

关键词

Keywords

references

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