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Dynamic Protocol Selection: A Hybrid Triangular FAHP-FTOPSIS and Contextual Bandit Framework for Adaptive V2I Communications
Journal article   Open access   Peer reviewed

Dynamic Protocol Selection: A Hybrid Triangular FAHP-FTOPSIS and Contextual Bandit Framework for Adaptive V2I Communications

Danladi Suleman, Rania Shibl, Mingzhong Wang and Keyvan Ansari
Vehicular Communications, Vol.62, pp.1-32
2026
DOI: https://doi.org/10.1016/j.vehcom.2026.101081
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Dynamic Protocol Selection: A Hybrid Triangular FAHP-FTOPSIS and Contextual Bandit Framework for Adaptive V2I Communications23.60 MBDownloadView
Published Version Open Access CC BY-NC V4.0

Abstract

adaptive communication contextual bandit data quality dynamic communications fahp-ftopsis internet of things transport protocols V2I communications
Vehicle-to-infrastructure (V2I) communications involve interactions between mobile vehicles and static roadside unit (RSU) nodes, resulting in limited connectivity and short interaction windows, particularly at high speeds. While there is consensus on using IEEE 802.11p and C-V2X radio technologies at the physical layer, the upper layers are technology-agnostic. These upper-layer communication technologies exhibit varied capabilities, each with distinct strengths and limitations in dynamic environments, leading to one-size-fits-all inefficiencies in V2I ecosystems. Existing methodologies primarily focus on lower-layer strategies, such as scheduling, radio access, dynamic resource allocation, and message prioritisation, to enhance network performance. Additionally, upper-layer approaches often assume ideal network conditions and fail to adapt to real-time variability. To overcome these limitations, this study introduces a novel protocol fusion module (FM) that dynamically leverages the strengths of twenty combinations of Internet of Things (IoT) application-layer protocols (ALPs) and transport-layer protocols. The FM is guided by fuzzy-based multi-criteria decision analysis (MCDA) and contextual multi-armed bandit machine learning models as decision engines (DEs). Extensive performance evaluations across multiple case studies and node densities indicate that the framework can optimise critical performance metrics under both single and shared-spectrum conditions. All DEs achieved an accumulated PDR averaging of at least 75% under high vehicle penetration, with higher values under low-and-mild-traffic conditions, aggregated over the set of protocol combinations selected for each communication session. These improvements were most visible under shared-spectrum, where fuzzy MCDA-based DE recommendations maintained desirable latency (in the 3ms range) and PDR averages within 90% and higher across both the SCH and CCH channels is most simulated scenarios and node densities. Overall, the framework demonstrated that it could improve critical metrics across various V2I scenarios.

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