  This study focuses on MEC-enhanced, vehicle-based crowdsensing systems that
rely on devices installed on automobiles. We investigate an opportunistic
communication paradigm in which devices can transmit measured data directly to
a crowdsensing server over a 4G communication channel or to nearby devices or
so-called Road Side Units positioned along the road via Wi-Fi. We tackle a new
problem that is how to reduce the cost of 4G while preserving the latency. We
propose an offloading strategy that combines a reinforcement learning technique
known as Q-learning with Fuzzy logic to accomplish the purpose. Q-learning
assists devices in learning to decide the communication channel. Meanwhile,
Fuzzy logic is used to optimize the reward function in Q-learning. The
experiment results show that our offloading method significantly cuts down
around 30-40% of the 4G communication cost while keeping the latency of 99%
packets below the required threshold.
