Chinese Journal on Internet of Things ›› 2021, Vol. 5 ›› Issue (2): 60-70.doi: 10.11959/j.issn.2096-3750.2021.00225

Special Issue: 边缘计算

• Topic: Edge Intelligence and Fog Computing in IoT • Previous Articles     Next Articles

Resource allocation based on optimal transport theory in IoT edge computing

Qi ZHANG1, Yuna JIANG1, Xiaohu GE1, Yonghui LI2   

  1. 1 Huazhong University of Science and Technology, Wuhan 430074, China
    2 The University of Sydney, Sydney NSW, Australia
  • Revised:2021-02-03 Online:2021-06-30 Published:2021-06-01
  • Supported by:
    The National Natural Science Foundation of China(U2001210)


With the development of the Internet of things (IoT) and edge computing, the computation-intensive tasks of IoT devices can be offloaded to edge devices and processed at the edge of networks.Due to the variation of the distribution and computation requirements of IoT devices, the computation resources of edge networks need to be managed dynamically.The optimal transport theory was adopted to optimize the computation resources allocation in IoT networks.An optimized regional partition mechanism was proposed based on the distribution of IoT devices and locations of edge computing devices.Under constraints on the computing capabilities of edge computing devices, the energy consumption and delay of IoT devices were optimized.The simulation results show that, compared with the traditional Voronoi partition scheme, the proposed optimization mechanism shows better balance.The average transmitting power can be reduced by 21% and the average delay can be reduced by 45%.

Key words: Internet of things, edge computing, resource allocation, optimal transport theory, energy consumption, delay

CLC Number: 

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