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Adaptive Blockchain-Oriented Trust Management in IoV using Proximal Policy Optimization.

S Francis Shamili1, R Gopi2

  • 1Faculty of Computer Science & Engineering, Dhanalakshmi Srinivasan Engineering College, Perambalur, TamilNadu, India.

Scientific Reports
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an Adaptive Blockchain-Oriented Trust Management (ABTM) architecture using Proximal Policy Optimization (PPO) for secure Internet of Vehicles (IoV) communication. The system enhances trust assessment and malicious node detection in dynamic vehicular networks.

Keywords:
BlockchainInternet of vehiclesProximal Policy OptimizationReinforcement learningTrust managementVehicular security

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Area of Science:

  • Intelligent Transport Systems
  • Cybersecurity
  • Distributed Ledger Technology

Background:

  • The Internet of Vehicles (IoV) enables real-time connectivity for intelligent transport systems, but dynamic environments pose challenges for secure communication, cooperative routing, and vehicle coordination.
  • Existing trust management approaches in IoV, such as static scoring or traditional machine learning, fail due to dynamic topology changes, malicious node insertions, and complex vehicle interactions.
  • This failure impairs adaptive and secure trust assessment in decentralized IoV ecosystems.

Purpose of the Study:

  • To propose and evaluate an Adaptive Blockchain-Oriented Trust Management architecture with Proximal Policy Optimization (ABTM-PPO) designed to address the limitations of current trust management systems in dynamic IoV environments.
  • To enhance the security, robustness, and adaptability of trust assessment mechanisms within vehicular networks.
  • To improve the resilience of IoV systems against malicious activities and dynamic network changes.

Main Methods:

  • The proposed ABTM-PPO architecture utilizes a dynamic trust state encoder (DTSE) to process vehicle data streams, capturing temporal patterns, node behaviors, and communication needs.
  • A blockchain-validated reputation consensus module (BRCM) ensures data integrity and prevents manipulation by maintaining trust information on an immutable distributed ledger.
  • Proximal Policy Optimization (PPO)-driven adaptive trust optimization engines (PATOEs) dynamically adjust trust rules based on network conditions and adversarial situations using reinforcement learning.

Main Results:

  • The ABTM-PPO system demonstrated superior performance compared to baseline trust assessment methods across various traffic volumes and coordinated attacks.
  • Achieved high accuracy rates, with 97.1% for malicious node detection and 98.4% for trust classification.
  • Significantly reduced decision reaction time by 21.6% in high-mobility, dense communication scenarios while maintaining overall performance.

Conclusions:

  • The developed trust-aware security management system is scalable and robust, offering a viable solution for next-generation intelligent vehicular networks.
  • The integration of blockchain and PPO provides an effective mechanism for adaptive and secure trust management in dynamic IoV environments.
  • The ABTM-PPO architecture successfully enhances security by improving malicious node detection and trust classification, ensuring reliable vehicle coordination.