Chinese Journal on Internet of Things ›› 2023, Vol. 7 ›› Issue (1): 109-117.doi: 10.11959/j.issn.2096-3750.2023.00324

• Theory and Technology • Previous Articles     Next Articles

Research on the cooperative offloading strategy of sensory data based on delay and energy constraints

Peiyan YUAN1,2, Saike SHAO1,2, Ran WEI1,2, Junna ZHANG1,2, Xiaoyan ZHAO1,2   

  1. 1 College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
    2 Big Data Engineering Laboratory for Teaching Resources &Assessment of Education Quality, Henan Normal University, Xinxiang 453007, China
  • Revised:2023-01-03 Online:2023-03-30 Published:2023-03-01
  • Supported by:
    The National Natural Science Foundation of China(62072159);The National Natural Science Foundation of China(U1804164);The National Natural Science Foundation of China(61902112);The Key Project of the Education Department of Henan Province(19A510015);The Key Project of the Education Department of Henan Province(20A520019);The Key Project of the Education Department of Henan Province(20A520020)


The edge offloading of the internet of things (IoT) sensing data was investigated.Multiple edge servers cooperatively offload all or part of the sensing data initially sent to the cloud center, which protects data privacy and improves user experience.In the process of cooperative offloading, the transmission of the sensing data and the information exchange among edge servers will consume system resources, resulting in the cost of cooperation.How to maximize the offloading ratio of the sensing data while maintaining a low collaboration cost is a challenging problem.A joint optimization problem of sensing data offload ratio and cooperative scale satisfying the constraints of network delay and system energy consumption was formulated.Subsequently, a distributed alternating direction method of multipliers (ADMM) via constraint projection and variable splitting was proposed to solve the problem.Finally, simulation experiments were carried out on MATLAB.Numerical results show that the proposed method improved the network delay and energy consumption compared to the fairness cooperation algorithm (FCA), the distributed optimization algorithm (DOA), and multi-subtasks-to-multi-servers offloading scheme (MTMS) algorithm.

Key words: collaborative edge computing, data offloading, system energy consumption, network delay, distributed ADMM

CLC Number: 

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