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An Approach to Share Self-Taught Knowledge between Home IoT Devices at the Edge.

Ingook Jang1, Donghun Lee2, Jinchul Choi3

  • 1IoT Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea. ingook@etri.re.kr.

Sensors (Basel, Switzerland)
|February 21, 2019
PubMed
Summary

This study introduces a Knowledge of Things (KoT) framework for intelligent Internet of Things (IoT) devices. The KoT framework enables edge-based knowledge sharing, reducing response times and power consumption in smart applications.

Keywords:
Internet of Thingsedge computingintelligent IoTknowledge sharingsmart home

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Internet of Things

Background:

  • The Internet of Things (IoT) is evolving towards intelligent applications leveraging AI.
  • Knowledge sharing among IoT devices is a significant challenge in this evolution.
  • Current systems often rely on cloud infrastructure for knowledge processing.

Purpose of the Study:

  • To propose a Knowledge of Things (KoT) framework for decentralized knowledge sharing in intelligent IoT.
  • To enable IoT devices to share self-taught knowledge at the network edge without cloud dependency.
  • To reduce behavioral repetition and computational redundancy in intelligent IoT applications.

Main Methods:

  • Development of a Knowledge of Things (KoT) framework.
  • Implementation of a prototype smart home system based on the KoT framework.
  • Experimental evaluation of the KoT framework's performance in a smart home setting.

Main Results:

  • The KoT framework facilitates effective production, accumulation, and sharing of self-taught knowledge among nearby IoT devices.
  • Demonstrated reduction in user-perceived response time for intelligent IoT devices.
  • Observed decrease in computational power consumption for the system.

Conclusions:

  • The proposed KoT framework is feasible and effective for enabling edge-based knowledge sharing in intelligent IoT applications.
  • KoT enhances the efficiency of intelligent IoT systems by minimizing redundancy and improving response times.
  • This approach offers a promising solution for decentralized intelligence in the evolving IoT landscape.