Journal
DIGITAL COMMUNICATIONS AND NETWORKS
Volume 9, Issue 1, Pages 56-66Publisher
KEAI PUBLISHING LTD
DOI: 10.1016/j.dcan.2022.03.009
Keywords
Computation offloading; MEC -enabled vehicular networks; Mobile edge computing; Task scheduling
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This paper presents a joint offloading strategy based on quantum particle swarm optimization for mobile edge computing (MEC) enabled vehicular networks to reduce task execution delay cost and energy consumption. Simulation results show that the proposed strategy effectively reduces system overhead and task completion delay.
With the development of the mobile communication technology, a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular communications. Most of computation-intensive and power-hungry applications result in a large amount of energy consumption and computation costs, which bring great challenges to the on-board system. It is necessary to exploit traffic offloading and scheduling in vehicular networks to ensure the Quality of Experience (QoE). In this paper, a joint offloading strategy based on quantum particle swarm optimization for the Mobile Edge Computing (MEC) enabled vehicular networks is presented. To minimize the delay cost and energy consumption, a task execution optimization model is formulated to assign the task to the available service nodes, which includes the service vehicles and the nearby Road Side Units (RSUs). For the task offloading process via Vehicle to Vehicle (V2V) communication, a vehicle selection algorithm is introduced to obtain an optimal offloading decision sequence. Next, an improved quantum particle swarm optimization algorithm for joint offloading is proposed to optimize the task delay and energy consumption. To maintain the diversity of the population, the crossover operator is introduced to exchange information among individuals. Besides, the crossover probability is defined to improve the search ability and convergence speed of the algorithm. Meanwhile, an adaptive shrinkage expansion factor is designed to improve the local search accuracy in the later iterations. Simulation results show that the proposed joint offloading strategy can effectively reduce the system overhead and the task completion delay under different system parameters.
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