车联网中基于雾计算的最小化功率开销任务卸载策略

Task Offloading Strategy for Minimizing Power Consumption Based on Fog Computing in Internet of Vehicle

LTE-V有LTE-V-Direct和LTE-V-Cell两种网络连接模式。为了解决智能车辆在两种模式下产生的大量数据请求导致的网络拥塞问题,考虑了一种在宏蜂窝基站和车辆处均部署雾计算服务器的网络结构LVF(LTE-V-Fog)。同时为了降低LVF系统的能量开销,在保证网络时延的前提下,提出了一种LTE-V网络中最小化系统功率开销的LTOS(LTE-V Task Offloading Strategy)优化策略。并以时延和功耗为限制条件,构建了最小化功率开销的数学优化模型,使用模拟退火算法对优化模型进行求解。仿真结果证明此策略在缩短任务执行时延的基础上,能够有效节约系统功耗。

LTE-V has two network connection modes:LTE-V-Direct and LTE-V-Cell.In order to solve the network congestion problem caused by huge number of data requests generated from the smart vehicle in both modes,a network structure LVF(LTE-V-Fog)in which a fog computing server is deployed at both the macrocell base station and the vehicle is considered.At the same time,in order to reduce the energy cost of the LVF system,an LTE-V Task Offloading Strategy(LTOS)optimization strategy for minimizing system power overhead in LTE-V networks is proposed under the premise of ensuring network delay.Based on the constraints of delay and power consumption,a mathematical optimization model for minimizing power overhead is constructed.The simulated annealing algorithm is used to solve the optimization model.The simulation results show that this strategy can effectively save system power consumption on the basis of reducing task execution delay.