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.

翻译：本研究聚焦于依托车载设备的MEC增强型群智感知系统。我们探讨了一种机会通信范式，其中设备可通过4G通信信道将测量数据直接传输至群智感知服务器，或通过Wi-Fi传输至邻近设备及沿路部署的路侧单元（Road Side Units）。针对如何在保持时延的前提下降低4G通信成本这一新问题，我们提出了一种融合强化学习技术Q学习与模糊逻辑的卸载策略。Q学习协助设备自主学习通信信道选择决策，而模糊逻辑则用于优化Q学习中的奖励函数。实验结果表明，该卸载方法在保证99%数据包时延低于预设阈值的同时，显著降低了约30-40%的4G通信成本。