Dynamic Q‑Learning‑Based Handover in VANETs: An Approach for Li‑Fi Based Handover Techniques

Journal of Intelligent Communication

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Dynamic Q‑Learning‑Based Handover in VANETs: An Approach for Li‑Fi Based Handover Techniques

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https://doi.org/10.54963/jic.v4i2.1496
Kalita, C. S., & Barooah, M. (2025). Dynamic Q‑Learning‑Based Handover in VANETs: An Approach for Li‑Fi Based Handover Techniques. Journal of Intelligent Communication, 4(2), 26–39. https://doi.org/10.54963/jic.v4i2.1496
Volume 4 Issue 2: September 2025
Copyright (c) 2025 Chinmoy Sailendra Kalita, Maushumi Barooah
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Authors

  • Chinmoy Sailendra Kalita

    Department of Computer Application, Assam Engineering College, Guwahati, Assam 781013, India
  • Maushumi Barooah

    Department of Computer Application, Assam Engineering College, Guwahati, Assam 781013, India

Received: 4 July 2025; Revised: 13 August 2025; Accepted: 16 August 2025; Published: 1 September 2025

Vehicular Ad hoc Networks (VANETs) face significant challenges in maintaining seamless connectivity due to frequent handovers caused by high vehicular mobility, fluctuating signal strength, and dynamic traffic conditions. Conventional static threshold‑based handover schemes are unable to adapt to these variations, often resulting in excessive latency, packet loss, handover failures, and poor reliability. Such drawbacks are particularly detrimental to critical applications like collision avoidance and real‑time traffic management. To address these limitations, this paper proposes a dynamic handover framework based on Q‑learning integrated with Li‑Fi communication technology. The framework intelligently evaluates real‑time parameters, such as vehicle speed, signal strength, and network occupancy, to make adaptive handover decisions rather than relying on preset thresholds. The proposed approach is modeled and tested using OMNeT++ and SUMO simulation platforms. Experimental results demonstrate that the proposed framework achieves a handover success rate of 90.0%, reduces failure rates to 9.8%, and limits ping‑pong handovers to 4.7%. Moreover, throughput improves by 20.3%, and handover delay decreases by 30.2% compared to other state‑of‑the‑art approaches. The packet delivery ratio is sustained at 95.6% even under high traffic density, indicating system robustness and scalability. These findings highlight that reinforcement learning‑based handover management provides a promising solution for next‑generation VANETs, offering adaptability, reliability, and efficiency for emerging intelligent transportation systems and critical vehicular applications, thereby ensuring safety.

Keywords:

Adaptive Communication Li‑Fi Handover Decision Reinforcement Learning Vehicular Networks

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