A Two-Phase Clustering Framework for Adaptive Load Balancing in Vehicular Networks using OPTICS and K-Medoid algorithms
DOI:
https://doi.org/10.31838/NJAP/07.02.24Keywords:
Urban VANET Scenarios, Mobility-Aware Networking, OPTICS algorithm, Handoff Performance, CommunicationAbstract
Vehicular Ad Hoc Networks (VANETs) are a cornerstone of intelligent transportation systems, yet high vehicle mobility and overlapping base station (BS) coverage often result in spectrum inefficiency and frequent handoffs. This paper introduces a spectrum-agnostic adaptive load balancing framework that combines OPTICS clustering with K-Medoid refinement to dynamically redistribute vehicles between neighboring BSs. In the first phase, OPTICS detects high-density groups, enabling efficient cluster formation. In the second phase, K-Medoid reassigns vehicles across adjacent BSs with imbalanced loads, ensuring balanced traffic without altering spectrum distribution. Simulation results demonstrate that the proposed method significantly improves throughput, reduces handoff latency, and lowers load imbalance by over 30% compared with conventional clustering techniques. Unlike prior resource-centric approaches, this work provides a lightweight, scalable, and spectrum-independent solution suitable for dense urban VANET deployments.
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