  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.
