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Dynamic Inference Approach Based on Rules Engine in Intelligent Edge Computing for Building Environment Control.

Wenquan Jin1, Rongxu Xu2, Sunhwan Lim3

  • 1Big Data Research Center, Jeju National University, Jeju 63243, Korea.

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|January 22, 2021
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Summary
This summary is machine-generated.

This study introduces intelligent edge computing for building control using a dynamic inference approach. It enables efficient deployment of deep learning models on edge gateways for real-time environmental management.

Keywords:
computational offloadingdeep learningedge computinginference modelrules engine

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Edge Computing

Background:

  • Computation offloading addresses hardware limitations in edge computing by distributing tasks.
  • Deep learning inference requires substantial data and computing resources, posing deployment challenges at the network edge.
  • Deploying domain-specific inference on edge devices enables intelligent services closer to users.

Purpose of the Study:

  • To propose an intelligent edge computing framework for building environment control.
  • To develop a dynamic inference approach for selecting and executing inference functions on edge gateways.
  • To enhance edge gateway capabilities with microservices for flexibility and extensibility.

Main Methods:

  • A rules engine on the edge gateway dynamically selects inference functions based on triggered rules.
  • Microservices architecture supports comprehensive gateway functions: device management, proxy, client service, intelligent service, and rules engine.
  • Deep learning models are trained on edge servers using building user data and deployed as inference models on the edge gateway.

Main Results:

  • The proposed system enables intelligent building environment control through dynamic inference.
  • Edge gateways effectively manage IoT devices and provide intelligent services on constrained hardware.
  • Microservices facilitate flexible updates of inference models and intelligent services.

Conclusions:

  • The intelligent edge computing approach with a dynamic inference mechanism is effective for building environment control.
  • The microservices-based architecture enhances the flexibility and scalability of edge gateways.
  • This framework successfully bridges IoT devices with the internet, offering intelligent services at the network edge.