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Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms.

Shalmoly Mondal1, Prem Prakash Jayaraman1, Pari Delir Haghighi2

  • 1School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn 3122, Australia.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

A new situation-aware Internet of Things (IoT) data generation framework, SA-IoTDG, was developed to address performance testing challenges. It generates situation-specific IoT data, enabling effective pre-deployment application evaluation.

Keywords:
Fuzzy Situation InferenceIoTIoT data generationIoT middleware platformsbenchmarkingperformance evaluationsituation transition

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Area of Science:

  • Computer Science
  • Software Engineering
  • Data Science

Background:

  • The proliferation of Internet of Things (IoT) applications necessitates robust performance assessment methods for IoT middleware platforms.
  • Existing performance testing methodologies for databases and Big Data are insufficient for the complex and heterogeneous nature of IoT applications and their data.

Purpose of the Study:

  • To introduce a novel situation-aware IoT data generation framework (SA-IoTDG) to overcome limitations in current IoT performance testing.
  • To enable the generation of situation-specific data tailored to the requirements of event-driven IoT applications.

Main Methods:

  • SA-IoTDG employs a situation-based approach, incorporating a situation description system and a SysML model for IoT application requirements.
  • A novel Markov chain-based method facilitates dynamic transitions in IoT data generation according to specific situations.
  • The framework was demonstrated using a real-world IoT traffic monitoring scenario.

Main Results:

  • Experimental evaluations confirmed SA-IoTDG's capability to generate realistic IoT data comparable to real-world data.
  • The generated data effectively supported performance evaluations of IoT applications on different middleware platforms.
  • Promising outcomes validated the framework's efficacy in simulating IoT environments.

Conclusions:

  • SA-IoTDG provides a valuable tool for researchers and developers to generate realistic IoT data for application testing and performance analysis.
  • The situation-aware approach enhances the relevance and utility of generated data for evaluating IoT middleware performance.
  • The framework facilitates crucial initial testing before the actual deployment of IoT applications.