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Related Experiment Video

Updated: Jul 1, 2026

Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing
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DRID: a spatiotemporal relational framework for robust IoT device identification in smart grids.

Zheheng Liang1,2, Mingjie Xu3, Ziyang Zhang1

  • 1Guangdong Power Grid Corporation, Guangzhou, China.

Scientific Reports
|June 29, 2026
PubMed
Summary

Robust IoT device identification (DI) is crucial for smart grids. Our DRID framework accurately identifies devices by analyzing communication patterns and temporal dynamics, even with incomplete data.

Keywords:
Device identificationInternet of thingsNetwork traffic analysisSelf-supervised learning

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Area of Science:

  • Computer Science
  • Network Security
  • Internet of Things (IoT)

Background:

  • Accurate device-type identification (DI) is essential for securing large-scale Internet of Things (IoT) deployments in smart grids.
  • Existing DI methods struggle with dynamic environments, temporal behavior, inter-device dependencies, and sparse data.

Purpose of the Study:

  • To propose DRID (Spatiotemporal Dynamic Relational Framework), a novel method for robust IoT device identification.
  • To address limitations of existing DI methods in capturing temporal dynamics and inter-device relationships.

Main Methods:

  • DRID utilizes a spatiotemporal dynamic relational framework to capture structural communication patterns and multi-scale temporal dynamics.
  • Employs a structure-time interaction mechanism and multi-scale temporal modeling.
  • Incorporates a differentiation-aware adaptive learning strategy for feature enhancement under sparse/noisy data.

Main Results:

  • DRID consistently outperforms state-of-the-art baselines across diverse sampling scenarios on public IoT traffic datasets.
  • Demonstrates superior performance in identifying device types even with sparse or incomplete traffic data.
  • Effectively fuses structural and temporal information for enhanced accuracy.

Conclusions:

  • DRID offers a scalable and accurate solution for IoT device identification in smart grids.
  • The framework enhances security and intelligent management of critical smart grid infrastructures.
  • Provides robustness under data scarcity, a common challenge in practical IoT deployments.