Design of Automatic Identification Gateway System for Different IoT Devices and Services

Lee, Hyun-Seong; Lee, Jae-Gwang; Lee, Jae-Pil; Lee, Jae-Kwang

doi:10.1109/smartworld.2018.00338

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…In terms of the identification methods, there are many research results [9]- [16]. Meidan et al [17] firstly used machine learning method for traffic analysis to distinguish devices.…”

Section: Introductionmentioning

confidence: 99%

IIS: Intelligent Identification Scheme of Massive IoT Devices

Liu

Sun

et al. 2021

2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)

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Device identification is of great importance in system management and network security. Especially, it is the priority in industrial internet of things (IIoT) scenario. Since there are massive devices producing various kinds of information in manufacturing process, the robustness, reliability, security and real-time control of the whole system is based on the identification of the massive IIoT devices. Previous IIoT device identification solutions are mostly based on a centralized architecture, which brings a lot of problems in scalability and security. In addition, most traditional identification systems can only identify inherent types of devices which is not suitable for the adaptive device management in IIoT. In order to solve these problems, this paper proposes a Intelligent Identification Scheme(IIS) of Massive IoT Devices, a completely distributed intelligent identification scheme of massive IIoT devices. The scheme changes the traditional centralized architecture and realizes more efficient clustering identification of massive IIoT devices. Moreover, IIS can identify more and more types of devices intelligently with the continuous learning ability since the identification model is constantly updated according to the ledger which is maintained by all gateways collaboratively. We also conduct experiments to evaluate the performance of IIS based on the data obtained from real IIoT devices, which proves that IIS is efficient in device identification and intelligent for the adaptive device management in IIoT.

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“…In terms of the identification methods, there are many research results [9]- [16]. Meidan et al [17] firstly used machine learning method for traffic analysis to distinguish devices.…”

Section: Introductionmentioning

confidence: 99%