A Computing Resource Allocation Optimization Strategy for Massive Internet of Health Things Devices Considering Privacy Protection in Cloud Edge Computing Environment

Abstract

With the development of 5G and the explosive growth of massive Internet of Health Things devices, existing computing resource allocation strategies have many problems such as long delay and poor security performance. Therefore, this paper proposes an optimization strategy for computing resource allocation of massive IoHT devices considering privacy protection in cloud edge computing environment. Firstly, a 5G heterogeneous cloud edge computing network is constructed. Besides, according to network status, the computing requirements of devices are allocated to local execution or edge computing. The computing delay, communication and computing resource allocation of edge servers are modeled accordingly. Then, taking the delay and energy consumption of network computing resource allocation as optimization goal, the priority of subtasks is sorted to realize the optimal allocation of computing resources. Finally, a protection model for instant messaging privacy data is designed by considering the risk of large-scale privacy data leakage in IoHT. Terminal devices under the same local area network are connected to edge servers by Socket without cloud server forwarding, which improves the security performance of privacy data. Experiment and demonstrate the performance of our proposed strategy on MATLAB simulation platform. The results show that the increase of edge computing server will affect the CPU proportion. Moreover, compared with other strategies, the number of users, the number of edge computing servers, the computing capacity of devices and the task arrival rate have the least impact on the average delay of proposed strategy, which effectively improves the performance of allocation strategy.