Enhancing Fault Tolerance in Self-Healing P2P Networks for Consumer Electronics

  • Sudhakar Kumar
  • , Sunil K. Singh
  • , Harshit Vashisht
  • , Vandana Sharma
  • , Varsha Arya
  • , Kwok Tai Chui
  • , Brij B. Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Modern consumer electronics depend on distributed systems, yet Peer-to-Peer (P2P) networks remain vulnerable to Byzantine faults. This paper presents an Adaptive Fault Tolerance (AFT) approach integrating probabilistic modeling and a Deep Neural Network (DNN) for real-time fault detection and recovery. The DNN, central to AFT, achieves 99.28% training and 99.07% validation accuracy, outperforming XGBoost (98.03%) and SVM (97.20%). Results, including ablation and real-time benchmarks, confirm AFT's effectiveness for self-healing P2P networks.

Original languageEnglish
Title of host publicationICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan
Subtitle of host publicationGenerative AI in Innovative Consumer Technology, Proceedings
Pages179-180
Number of pages2
ISBN (Electronic)9798331587413
DOIs
Publication statusPublished - 2025
Event12th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2025 - Kaohsiung, Taiwan, Province of China
Duration: 16 Jul 202518 Jul 2025

Publication series

NameICCE-Taiwan 2025 - 12th IEEE International Conference on Consumer Electronics - Taiwan: Generative AI in Innovative Consumer Technology, Proceedings

Conference

Conference12th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2025
Country/TerritoryTaiwan, Province of China
CityKaohsiung
Period16/07/2518/07/25

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