Optimizing Energy-Latency Tradeoff for Computation Offloading in SDIN-Enabled MEC-based IIoT

• Xinchang Zhang,
• Changsen Xia,
• Tinghuai Ma,
• Lejun Zhang,
• Zilong Jin,

Vol. 16, No. 12, December 31, 2022
10.3837/tiis.2022.12.017, Download Paper (Free):

• SDIN
• Edge Computing
• computation offloading
• Evolution Computation

Abstract

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

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