Both the mobile edge computing (MEC) based and fog computing (FC) aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMA-based computation offloading scheme for vehicular platoons on highways, where the vehicles can offload their computing tasks to other platoon members. To cope with the rapidly fluctuating channel quality, we divide the timeline into successive time slots according to the channel's coherence time. Robust computing and offloading decisions are made for each time slot after taking the channel estimation errors into account. Considering a certain time slot, we first analytically characterize both the locally computed source data and the offloaded source data as well as the energy consumption of every vehicle in the platoons. We then formulate the problem of minimizing the long-term energy consumption by optimizing the allocation of both the communication and computing resources. To solve the problem formulated, we design an online algorithm based on the classic Lyapunov optimization method and block successive upper bound minimization (BSUM) method. Finally, the numerical simulation results characterize the performance of our algorithm and demonstrate its advantages both over the local computing scheme and the orthogonal multiple access (OMA)-based offloading scheme.
翻译:机动边缘计算(MEC)基于车辆的移动边缘计算(MEC)和雾计算(FC)辅助的车辆互联网(IoV)是满足低纬度普遍计算要求的有希望的范例。为此目的,我们为高速公路上的车辆排设计一个动态的NOMA计算卸载计划,车辆可以卸载其计算任务给其他排成员。为了应对快速波动的频道质量,我们根据频道的一致性时间将时间段分为连续的时间段。在考虑频道估计错误后,每个时段都做出硬化计算和卸载决定。考虑到某个时段,我们首先从分析角度确定本地计算的源数据和卸载源数据以及排中每部车辆的能源消耗。然后我们提出通过优化通信和计算资源的分配来尽量减少长期能源消耗的问题。为了解决问题,我们根据经典的Lyapunov优化方法设计了一个在线算法,并阻止连续采用上层最小化方法(BSUM)。最后,我们用数字模拟结果来描述我们多轨算法的绩效,并展示其优势。