面向工业场景数据安全的优化卸载方法

Study on Optimized Offloading for Data Security in Industrial Scene

针对工业场景数据传输过程中存在的安全卸载问题,文中首次将安全策略作为决策变量融入优化问题,应用计算卸载原理以及差分进化算法,提出了一种数据安全卸载算法。首先针对工业现场设备的本地计算、本地边缘计算、跨车间边缘计算和云计算4种计算模式以及数据安全进行数学建模,将多级安全策略、任务卸载和资源分配相融合,构建了数据安全卸载模型。综合考虑时延和安全风险概率的影响,设计最大化设备满意度的目标函数,形成了安全优化卸载方案。针对该优化问题,提出了一种基于改进的差分进化策略的数据安全卸载算法,在满足最优解的同时,在满足时延和安全风险的要求下实现系统的设备满意度最大化。相比GASORA算法、GSOJRA算法和DEDSTO-NS算法,所提算法不仅使现场设备满足了时延和风险概率的要求,并在保障数据安全性的同时,将设备满意度提高了35%。仿真结果证实了所提方法的有效性,且有一定的现实应用价值。

The problem of security offloading in data transmission in industrial scenarios has gained wide attention.This paper is the first to integrate security policy as a decision variable into the optimization problem.It applies computational offloading principles and differential evolutionary algorithms,and proposes a data security offloading algorithm.Firstly,mathematical modeling conducted for four computing modes of industrial field devices:local computing,local edge computing,cross-plant edge computing in this paper,and cloud computing,as well as data security,and a data security offloading model is constructed by integrating multi-level security policies,task offloading,and resource allocation.Then,the security-optimized offloading scheme is formed by designing the objective function of maximizing device satisfaction by considering the effects of time delay and security risk probability.Finally,for this optimization problem,a data security offloading algorithm based on an improved differential evolution stra-tegy is proposed to maximize the device satisfaction of the system while satisfying the optimal solution with the latency and secu-rity risk requirements.Compared with the GASORA,GSOJRA and DEDSTO-NS algorithms,the proposed algorithm enables the field devices to satisfy the delay and risk probability requirements.Furthermore,it improves the device satisfaction by 35%while guaranteeing data security.Simulation results confirm the effectiveness of the proposed method and have some realistic application value.