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

Attributable to emerging of Mobile Edge Computing (MEC), the hardware-constrained industrial devices have further computational and service capability in Industrial Internet of Thing (IIoT) systems. Nevertheless, unreliable network environments and unpredictable processing delays are intolerable factors for any service application. Therefore, this paper 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 achieve this purpose, the optimization problem is mathematically derived as a mixed-integer nonlinear programming (MINLP) 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.