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Context-Aware Handover Thresholds for 5G Vehicle-to-Vehicle Networks Under Harsh Weather Conditions | International Journal of Machine Learning and Engineering

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Abstract
Abstract
The rapid deployment of fifth-generation wireless networks has significantly advanced the capabilities of vehicle-to-vehicle communications, enabling transformative applications in autonomous driving and intelligent transportation systems. A critical requirement for these applications is ultra-reliable low-latency communication, which must be maintained consistently even as vehicles traverse varying cell coverage areas. Handover management in highly mobile environments poses a substantial challenge, particularly when subjected to harsh meteorological conditions such as heavy rain, dense fog, and snowstorms. These environmental factors severely degrade signal propagation, particularly at millimeter-wave frequencies, leading to frequent handover failures, excessive ping-pong effects, and unacceptable communication delays. This paper proposes a novel context-aware handover threshold adaptation mechanism designed specifically for fifth-generation vehicle-to-vehicle networks operating under severe weather conditions. By continuously monitoring environmental parameters alongside traditional mobility metrics, the proposed system dynamically adjusts handover margins and time-to-trigger parameters. The research systematically models the attenuation effects of various weather states on signal strength and integrates these models into a centralized edge-computing architecture. Comprehensive simulations demonstrate that integrating weather context into the handover decision-making process significantly reduces handover failure rates and minimizes unnecessary handovers compared to standard static threshold methods. The findings underscore the necessity of environmental awareness in mobile network management and provide a robust framework for ensuring uninterrupted vehicular connectivity in unpredictable climates.