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A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection.

Declan T Delaney1, Gregory M P O'Hare2,3

  • 1School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland. declan.delaney@ucd.ie.

Sensors (Basel, Switzerland)
|December 6, 2016
PubMed
Summary

Selecting the optimal Internet of Things (IoT) network solution is challenging. This framework autonomously selects the best IoT network solution for an application using a predictive performance model, achieving up to 85% accuracy.

Keywords:
Internet of Thingsframeworkmodellingperformancestandardised testing

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Area of Science:

  • Computer Science
  • Network Engineering
  • Software Engineering

Background:

  • The Internet of Things (IoT) landscape features diverse network solutions, each suited for specific applications and environments.
  • A lack of standardized comparison methods hinders developers in selecting optimal IoT network solutions, leading to fragmented practices.
  • The increasing complexity and number of IoT solutions exacerbate the challenge of choosing the most effective one for a given application.

Purpose of the Study:

  • To introduce a novel framework for autonomously selecting the most suitable network solution for Internet of Things (IoT) applications.
  • To develop a performance model capable of predicting solution efficacy within diverse deployment environments.
  • To facilitate a more harmonized approach to testing and comparing IoT communication solutions.

Main Methods:

  • Development of a framework that utilizes a performance model to predict the expected performance of various IoT network solutions.
  • Collection of simulation data to build and train the performance models.
  • Implementation of an autonomous selection mechanism within the framework to choose the best-suited solution based on predicted performance.

Main Results:

  • The developed performance models can predict the best-performing solution for a given metric with up to 85% accuracy.
  • The framework successfully demonstrates autonomous selection of appropriate IoT network solutions based on environmental context.
  • Identified significant discrepancies in current practices for evaluating and comparing IoT communication solutions.

Conclusions:

  • The proposed framework offers a viable solution for the autonomous selection of IoT network solutions, addressing the challenge of environmental variability.
  • Accurate performance prediction models are crucial for optimizing IoT application deployment and Quality of Service (QoS).
  • There is a critical need for harmonized testing procedures to enable direct comparison of diverse IoT network solutions and foster interoperability.