Fuzzy Q-Learning-Based Opportunistic Communication for MEC-Enhanced Vehicular Crowdsensing
<|reference_start|>Fuzzy Q-Learning-Based Opportunistic Communication for MEC-Enhanced Vehicular Crowdsensing: 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.<|reference_end|>
@article{nguyen2024fuzzy,
    title={Fuzzy Q-Learning-Based Opportunistic Communication for MEC-Enhanced
  Vehicular Crowdsensing},
    author={Trung Thanh Nguyen, Truong Thao Nguyen, Thanh Hung Nguyen, Phi Le
  Nguyen},
    journal={arXiv preprint arXiv:2405.01057},
    year={2024},
    doi={10.1109/TNSM.2022.3192397},
    archivePrefix={arXiv},
    eprint={2405.01057},
    primaryClass={cs.NI}
}