  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 necessary channel
switching decisions. Two averaging techniques, Sliding Window Average (SWA) and
Exponentially Weighted Moving Average (EWMA), are employed to leverage past
samples and are evaluated against the no-memory case. Due to the high
variability of the environment (e.g., high velocities, changing density of
vehicles on the road), we propose to calculate the CBR by employing non-uniform
channel sampling allocations as well as evaluate it using both simplified
numerical and realistic Vehicle-to-Vehicle (V2V) computer simulations. The
numerical simulation results show that gains in the probability of the best
channel selection can be achieved relative to a uniform sampling allocation
approach. By utilizing memory, we observe an additional increase in the channel
selection performance. Similarly, we see an increase in the probability of
successful reception when utilizing the bumblebee algorithm via a system-level
simulator.
