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April 8, 2026International Journal of Sensor Networks0 citations

Deep Reinforcement Learning-based Secure and Reliable Opportunistic Routing for VANETs

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HXHuibin Xu

Key Points

  • The central aim is to develop a secure and reliable routing mechanism for vehicular ad hoc networks (VANETs) using deep reinforcement learning techniques.
  • Utilized deep reinforcement learning algorithms for routing decisions.
  • Developed a framework for opportunistic routing in VANETs.
  • Evaluated the performance based on security and reliability metrics.
  • Enhanced routing efficiency with significant reductions in communication delays.
  • Improved network security against potential attacks and failures.

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

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Cite This Study

Huibin Xu (2025) studied this question.

synapsesocial.com/papers/69d5f0ee74eaea4b11a7a5fdhttps://doi.org/10.1504/ijsnet.2025.10077459
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A lightweight blockchain-based key management scheme for secure VANET communication2026
  2. 2Multiagent deep reinforcement learning based resource management technique for vehicular networks in heterogeneous traffic2026
  3. 3Deep Q-learning for smart intersection routing in vehicular ad hoc network2025
  4. 4Autonomous navigation of robots in dynamic environments using reinforcement learning algorithms2026
  5. 5Intelligent cyber-attack detection in autonomous vehicles using residual network based deep learning model2026