LAUSR

Vehicular edge computing (VEC) has become a promising enabler for ultrareliable and low-latency communications (URLLC) vehicular networks by providing computational resources for task offloading. In this article, we investigate an online task offloading problem for heterogeneous VEC (HVEC) network in the face of unknown environment dynamics. To overcome the unavailability of state information, we aim for minimizing the expectation of total offloading energy consumption while satisfying stringent delay requirements by learning the relationship between historical observations and rewards. Hence, this problem constitutes a contextual multiarmed bandit (MAB) problem. By grouping users according to their task preferences, we propose a contextual clustering of bandits-based online vehicular task offloading (CBTO) solution, which is aware of the task popularity. Simulation results reveal that the proposed solution outperforms other contextual and context-free benchmarkers in terms of both offloading energy consumption and delay performance.