Secure and Efficient Authentication Protocol for Supply Chain Systems in Artificial-Intelligence-Based Internet of Things
With the rapid development of globalization and technology, the industrial structure has undergone profound changes and market competition is becoming increasingly fierce. In order to enhance their competitive advantage, enterprises are integrating resources through supply chain systems (SCS) and establishing strategic partnerships to achieve rapid market response and optimized resource allocation. Artificial Intelligence (AI) -based Internet of Things (IoT) has brought unprecedented changes to supply chain systems. AIoT not only improves the transparency and efficiency of the supply chain, but also significantly reduces carbon emissions, making a positive contribution to green and sustainable development. However, with the widespread application of AIoT in SCS, its security issues have become increasingly prominent, posing a serious threat to the stable operation of the supply chain. Therefore, this article proposes a secure and efficient authentication protocol for supply chain systems in AIoT. This protocol implements user authentication, ensures forward security of session key and guarantees secure data transmission. Through security analysis, this article proves that the protocol can resist various known attacks. In addition, through performance analysis, the protocol has low overhead and can meet the efficiency requirements of SCS.
Authors
- Junfeng Miao (ORCID: https://orcid.org/0000-0001-5899-7737)
- Shuangxi Hong (ORCID: https://orcid.org/0000-0002-7773-7241)
- Xin Ning (ORCID: https://orcid.org/0000-0001-7897-1673)
- Bo Liu (ORCID: https://orcid.org/0000-0002-3603-6617)
- Lanlan Wang (ORCID: https://orcid.org/0000-0001-6730-5237)
Institutions
- Northeastern University (US)
- Chinese Academy of Sciences (CN)
- Furness College (GB)
- Institute of Semiconductors (CN)
- Henan Normal University (CN)
Publication Details
- Journal
- IEEE Internet of Things Journal
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1109/jiot.2025.3592401
- Citations
- 16
- Primary Topic
- E-commerce and Technology Innovations
- Type
- article
- Field-Weighted Citation Impact
- 39.15