Distributed probabilistic offloading in edge computing for 6g-enabled massive internet of things

Zhuofan Liao, Jingsheng Peng, Jiawei Huang, Jianxin Wang, Jin Wang*, Pradip Kumar Sharma, Uttam Ghosh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Mobile-edge computing (MEC) is expected to provide reliable and low-latency computation offloading for massive Internet of Things (IoT) with the next generation networks, such as the sixth-generation (6G) network. However, the successful implementation of 6G depends on network densification, which brings new offloading challenges for edge computing, one of which is how to make offloading decisions facing densified servers considering both channel interference and queuing, which is an NP-hard problem. This article proposes a distributed-two-stage offloading (DTSO) strategy to give tradeoff solutions. In the first stage, by introducing the queuing theory and considering channel interference, a combinatorial optimization problem is formulated to calculate the offloading probability of each station. In the second stage, the original problem is converted to a nonlinear optimization problem, which is solved by a designed sequential quadratic programming (SQP) algorithm. To make an adjustable tradeoff between the latency and energy requirement among heterogeneous applications, an elasticity parameter is specially designed in DTSO. Simulation results show that compared to the latest works, DTSO can effectively reduce latency and energy consumption and achieve a balance between them based on application preferences.

Original languageEnglish
Pages (from-to)5298-5308
Number of pages11
JournalIEEE Internet of Things Journal
Volume8
Issue number7
Early online date23 Oct 2020
DOIs
Publication statusPublished - 1 Apr 2021

Keywords

  • 6G
  • channel interference
  • edge computing
  • Internet of Things (IoT)
  • nonlinear optimization
  • offloading
  • queuing theory

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