A Resource Allocation Scheme for Joint Optimizing Energy Consumption and Delay in Collaborative Edge Computing-Based Industrial IoT

Attributable to the emergence of mobile edge computing (MEC), the hardware-constrained industrial devices have further computational and service capability in industrial Internet of Things (IIoT) systems. Nevertheless, unreliable network environments and unpredictable processing delays are intolerable factors for any service application. Therefore, this article studies the associated constraint problem of how to optimize the offloading decision and resource allocation in collaborative edge computing networks with multiple IIoT devices and MEC servers. In order to attain this purpose, the optimization problem is mathematically derived as a mixed-integer nonlinear programming problem which is a large-scale NP-hard problem. Then, an improved differential evolution algorithm (IDE) is proposed to obtain the optimal solutions in an accessible time complexity. Finally, the performance of the IDE-based resource allocation scheme has been compared with other baseline schemes. Simulation results demonstrate that the IDE-based optimization scheme could significantly reduce the system delay and energy consumption.

I. Introduction

With the rapid development of mobile communication technology and the popularization of smart terminals, users have increasingly higher requirements for network performance, such as network service quality and data transmission delay. Given the limited battery capacity and the computing power of mobile devices, the excessive energy consumption caused by running compute-intensive applications locally is intolerable. Facing massive data transmissions and huge amounts of computing requirements in industrial Internet of Things (IIoT) environment [1], traditional cloud computing suffers from a considerable waiting delay and consumes the limited network resources [2], which seriously affects the quality of network services and user experiences.