Abstract : Benefiting from its abundant computing resources and low computing latency, mobile edge computing (MEC) is a promising approach for enhancing the computing capacity of the 5G Internet of vehicles (IoV). Because of the high mobility, handover is frequent and inevitable in IoV networks. In this paper, we investigate an edge collaborative task offloading and splitting strategy in MEC-enabled IoV networks, in which the task is splitted on the edge and paralleling executed by each part of the task on several MEC servers when handover is occured. Applications in IoV networks have flexible requirements on latency and energy consumption. To realize the tradeoff between latency and energy consumption, we formulate the task offloading and splitting as an optimization problem with the aim of minimizing the total cost of latency and energy consumption by jointly optimizing the task splitting ratio and uplink transmit power of vehicle terminal (VT). Because the proposed problem is non-smooth and non-convex, we divide the original problem into two convex subproblems, and apply an alternate convex search (ACS) algorithm to obtain the optimized solution with low computational complexity. Numerical simulation results show that the proposed method can adjust the offloading strategy properly according to task preference, and obtain a lower total cost compared with the baseline algorithms.
Index terms : Internet of vehicles, mobile edge computing, resource allocation.