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Updated: May 25, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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RCoD: Reputation-Based Context-Aware Data Fusion for Mobile IoT.

Samia Tasnim1, Niki Pissinou2, S Sitharama Iyengar2

  • 1Department of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH 43606, USA.

Sensors (Basel, Switzerland)
|February 26, 2025
PubMed
Summary

This study introduces a novel Reputation-Based Context-Aware Data-Fusion (RCoD) mechanism to enhance data accuracy in mobile Internet of Things (IoT) systems. RCoD effectively identifies malicious participants and recovers corrupted data, ensuring reliable sensor information.

Keywords:
air-quality monitoringcontextdata analyticsdata fusiondata reliabilityinternet of thingsmachine learningreputationtrust

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Area of Science:

  • Computer Science
  • Data Science
  • Sensor Networks

Background:

  • Mobile sensing technologies and the Internet of Things (IoT) are rapidly expanding.
  • Ensuring data reliability and accuracy in mobile IoT systems is challenging due to inherent vulnerabilities.
  • People-centric architectures in mobile IoT can lead to inaccurate or corrupted data, especially with malicious participants.

Purpose of the Study:

  • To address the challenge of inaccurate data prediction in mobile IoT systems.
  • To develop a mechanism resilient against malicious data injection and corruption attacks.
  • To recover missing or imprecise data values from correlated data streams.

Main Methods:

  • Proposing a Reputation-Based Context-Aware Data-Fusion (RCoD) mechanism.
  • Utilizing a Contextual Hidden Markov Model for accurate real-time data prediction.
  • Evaluating RCoD's resilience against on-off and data-corruption attacks.

Main Results:

  • RCoD accurately identified honest participants even when the majority were malicious.
  • The mechanism demonstrated resilience against various malicious data injection rates.
  • Empirical evaluation using Beijing's air-quality dataset confirmed RCoD's superiority over state-of-the-art methods.

Conclusions:

  • The proposed RCoD mechanism significantly improves data accuracy and reliability in mobile IoT environments.
  • RCoD offers a robust solution for data fusion and prediction in the presence of adversarial participants.
  • Context-aware data fusion combined with reputation systems is effective for secure and dependable IoT data.