基于信息安全强度的路侧设备任务调度优化策略

Task scheduling optimization strategy for roadside unit based on security strength

车路协同系统中,车辆由于自身计算资源局限性将任务卸载至路侧设备(RSU)进行执行,然后由RSU考虑在本地处理任务或将任务卸载至云端的“车-边-云”架构,是近年来受到广泛关注的边缘计算新模式。由于“边-云”侧无线信道的开放接入以及不确定性,需要增加安全机制来保证数据传输的可靠性,然而增加安全机制会使RSU的计算负载增加,进而使得RSU的能耗增加。针对如何在满足任务时延约束条件下优化路侧设备能耗及信息安全效用,提出了车路协同系统下路侧设备基于能耗-队列均衡的联合任务调度及加密优化(EPTS),并建立车速状态模型、任务加密模型、数据缓存队列模型、任务计算模型及优化目标函数。利用李雅普诺夫优化理论将优化问题模型进行转化,并将转化后的模型描述为背包问题进行求解,仿真结果验证了所提出的EPTS具有较好的收敛性和有效性,平均目标价值优于平均分配策略17%,优于任务队列长度加权分配策略21%。

In the context of cooperative vehicle-infrastructure systems(CVIS),vehicles often offload computational tasks to roadside units(RSUs)for execution due to their own constrained computing resources.A novel approach that has garnered increased attention involves the“vehicle-edge-cloud”framework,wherein RSUs decide whether to process tasks locally or to offload them to the cloud.The open and unpredictable nature of the wireless channels on the“edge-cloud”interface necessitates the incorporation of a security mechanism to safeguard the integrity of information transmission.However,integrating such a security mechanism can escalate the energy consumption of RSUs.Addressing the challenge of jointly optimizing roadside energy consumption and information security utility,without compromising task delay constraints,is a complex issue.To tackle this,an energy-packets queue tradeoff of task scheme and encryption strategy(EPTS)was introduced.This approach involved the development of a vehicle speed state model,a task encryption model,a data cache queue model,and a task calculation model,along with the formulation of an optimization objective function.The optimization model was subsequently transformed using Lyapunov optimization theory and was reformulated as a knapsack problem for resolution.The simulation results confirm the EPTS's commendable convergence and effectiveness.The average objective value achieved by the proposed EPTS was found to be 17%superior to that of the Equal Allocation Strategy(EAS)and 21%superior to the queue-weighted strategy(QS).