Memory Enabled Bumblebee-based Dynamic Spectrum Access for Platooning Environments
Kuldeep S. Gill, Pawel Kryszkiewicz, Pawel Sroka, Adrian Kliks,, Alexander M. Wyglinski

TL;DR
This paper introduces a memory-enabled bumblebee-inspired algorithm for dynamic spectrum access in vehicular platoons, improving channel selection and communication success in highly variable environments.
Contribution
It develops a novel memory-based bumblebee foraging algorithm that enhances channel selection in vehicular networks by leveraging past observations and non-uniform sampling techniques.
Findings
Memory improves channel selection probability.
Non-uniform sampling outperforms uniform sampling.
Enhanced channel selection increases communication success.
Abstract
In this paper, we propose a novel memory-enabled non-uniform sampling-based bumblebee foraging algorithm (MEB) designed for optimal channel selection in a distributed Vehicular Dynamic Spectrum Access (VDSA) framework employed in a platoon operating environment. Given how bumblebee behavioral models are designed to support adaptation in complex and highly time-varying environments, these models can be employed by connected vehicles to enable their operation within a dynamically changing network topology and support their selection of optimal channels possessing low levels of congestion to achieve high throughput. As a result, the proposed VDSA-based optimal channel selection employs fundamental concepts from the bumblebee foraging model. In the proposed approach, the Channel Busy Ratio (CBR) of all channels is computed and stored in memory to be accessed by the MEB algorithm to make the…
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