On-Body Device Clustering for Security Preserving in Internet of Things

Bingxian Lu, Lei Wang, Wei Wang*, Keping Yu*, Sahil Garg, Md Jalil Piran*, Atif Alamri


研究成果: Article査読


The ability to detect which wireless devices are belonging to the same person from Wi-Fi access point (AP) enables many potential Internet-of-Things (IoT) applications, including continuous authentication and user-oriented devices isolation. The existing cryptographic-based solutions are not suitable for IoT devices with limited power and computing capabilities. The development of electronics and chip technology makes it possible to deploy machine learning (ML) algorithms on APs. In this article, we propose an on-body device clustering (OBDC) scheme. First, the OBDC extracts the trajectory and gait patterns from wireless signals when the user is moving. Second, it utilizes a hierarchical clustering algorithm to measure the similarity of wireless signal patterns between devices. Finally, if the devices are clustered into the same cluster, they are considered to be carried by the same person. Our real-world experimental results show that the devices from about 90% of users can be clustered correctly, while maintaining the devices from only 0.7% of users may be clustered into the same cluster with others' devices incorrectly.

ジャーナルIEEE Internet of Things Journal
出版ステータスPublished - 2023 2月 15

ASJC Scopus subject areas

  • 信号処理
  • 情報システム
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信


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