Memory Enabled Bumblebee-Based Dynamic Spectrum Access for Platooning Environments

In this paper, we propose a novel memory-enabled non-uniform sampling-based bumblebee foraging algorithm (MEB) designed for optimal channel selection in distributed Vehicular Dynamic Spectrum Access (VDSA) framework employed platoon operating environment. Given how behavioral models are to support adaptation complex and highly time-varying environments, these can be by connected vehicles enable their operation within dynamically changing network topology of channels possessing low levels congestion achieve high throughput. As result, the proposed VDSA-based employs fundamental concepts from model. approach, Channel Busy Ratio (CBR) all is computed stored memory accessed MEB make necessary switching decisions. Two averaging techniques, Sliding Window Average (SWA) Exponentially Weighted Moving (EWMA), leverage past samples evaluated against no-memory case. Due variability environment ( e.g. , velocities, density on road), calculate CBR employing sampling allocations as well evaluate it using both simplified numerical realistic Vehicle-to-Vehicle (V2V) computer simulations. The simulation results show that gains probability best achieved relative uniform allocation approach. By utilizing memory, observe an additional increase performance. Similarly, see successful reception when via system-level simulator.