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Related Experiment Videos

FedCARE: Fuzzy-Supervised Federated Inference with Confidence Gating for Resilient IIoT Sensor Networks.

Basma Mostafa1,2, Hanan Haj Ahmad3, Yazan Rabaiah4

  • 1Operations Research Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces FedCARE, a framework for intelligent routing in safety-critical Industrial Internet of Things (IIoT) networks. It enhances packet delivery and critical recall in disaster scenarios using fuzzy-supervised federated inference.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Safety-critical Industrial Internet of Things (IIoT) sensor networks require robust routing in disaster scenarios.
  • Existing federated learning on edge nodes faces challenges with interpretable signals, confidence-based actions, and node availability.

Purpose of the Study:

  • To propose FedCARE, a framework enhancing federated learning for safety-critical IIoT routing.
  • To address challenges in interpretable supervision, conservative action, and node availability in edge federated learning.

Main Methods:

  • Developed FedCARE framework utilizing a Mamdani Fuzzy Inference System for criticality labeling.
  • Implemented a dropout-aware aggregation protocol and a confidence-gated resolver for robust inference.
Keywords:
IIoT securityInternet of Thingsconfidence-gated fusioncriticality-aware routingcyber-physical systemsdropout-aware aggregationedge intelligencefederated learningfuzzy inferencesensor networks

Related Experiment Videos

  • Evaluated on Watts-Strogatz topologies with Edge-IIoTset and WUSTL-IIoT-2021 benchmarks under high fault rates.

    • Main Results:

    • Achieved 99.00% critical recall and up to 1.8x higher overall packet delivery compared to RPL-RP.
    • Demonstrated significant routing improvements attributed to fuzzy criticality labeling and multi-path replication.
    • Observed a 7.8% energy overhead per delivered packet.

    Conclusions:

    • Fuzzy-supervised federated inference provides a practical and interpretable solution for safety-critical IIoT routing.
    • FedCARE effectively handles partial node availability and ensures prioritization of critical data.
    • The framework enhances network resilience and performance in challenging disaster environments.