Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Intelligent Computation Offloading and Resource Allocation in IIoT with End-Edge-Cloud Computing Using NSGA-III
AU - Peng, Kai
AU - Huang, Hualong
AU - Zhao, Bohai
AU - Jolfaei, Alireza
AU - Xu, Xiaolong
AU - Bilal, Muhammad
PY - 2023/9/1
Y1 - 2023/9/1
N2 - The Industrial Internet of things (IIoT), which consists of massive IoT devices and wireless access points in industrial infrastructures to acquire intelligent services, has been considered as a vital physical information platform for implementing Industry 4.0. Further, mobile edge computing (MEC) has brought an opportunity to accelerate the development of IIoT. However, the existing MEC methods may not be directly used for IIoT scenarios due to the large size of IIoT devices and the characteristics of the applications, as well as the limited and heterogeneous resources of edge servers. In view of this, the computation offloading and resource allocation are formulated as a multi-objective optimization problem, and an end-edge-cloud collaborative intelligent optimization method is devised in this paper. Comprehensive experiments and evaluations are carried out to prove the effectiveness and efficiency of our proposed method with regard to the energy consumption and time consumption of IIoT devices, as well as resource utilization and load balancing of edge servers.
AB - The Industrial Internet of things (IIoT), which consists of massive IoT devices and wireless access points in industrial infrastructures to acquire intelligent services, has been considered as a vital physical information platform for implementing Industry 4.0. Further, mobile edge computing (MEC) has brought an opportunity to accelerate the development of IIoT. However, the existing MEC methods may not be directly used for IIoT scenarios due to the large size of IIoT devices and the characteristics of the applications, as well as the limited and heterogeneous resources of edge servers. In view of this, the computation offloading and resource allocation are formulated as a multi-objective optimization problem, and an end-edge-cloud collaborative intelligent optimization method is devised in this paper. Comprehensive experiments and evaluations are carried out to prove the effectiveness and efficiency of our proposed method with regard to the energy consumption and time consumption of IIoT devices, as well as resource utilization and load balancing of edge servers.
KW - Collaboration
KW - Computation Offloading
KW - Costs
KW - Energy consumption
KW - IIoT
KW - Industrial Internet of Things
KW - MEC
KW - Multi-Objective Optimization
KW - Resource Allocation
KW - Resource management
KW - Servers
KW - Task analysis
U2 - 10.1109/TNSE.2022.3155490
DO - 10.1109/TNSE.2022.3155490
M3 - Journal article
AN - SCOPUS:85126310494
SP - 3032
EP - 3046
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
SN - 2327-4697
ER -