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 - Distributed Incentives for Intelligent Offloading and Resource Allocation in Digital Twin Driven Smart Industry
AU - Peng, Kai
AU - Huang, Hualong
AU - Bilal, Muhammad
AU - Xu, Xiaolong
PY - 2023/3/1
Y1 - 2023/3/1
N2 - Mobile edge computing is one of the key enabling technologies of smart industry solutions, providing agile and ubiquitous services for mobile devices (MDs) through offloading latency-critical tasks to edge service providers. However, it is challenging to make optimal decisions of computation offloading and resource allocation while ensuring the privacy and information security of MDs. Consequently, we consider a new vision of digital twin (DT) empowered edge networks, where the optimization problem is formulated as a two-stage incentive mechanism. First, the resource allocation strategy is determined by the interaction among DTs according to the credit-based incentives. Afterward, a distributed incentive mechanism based on the Stackelberg-based alternating direction method of multipliers is opted to obtain the optimal offloading and privacy investment strategies in parallel. Numerical results show that the proposed two-stage incentive mechanism achieves effective resource allocation and computation offloading while simultaneously improving the privacy and information security of MDs.
AB - Mobile edge computing is one of the key enabling technologies of smart industry solutions, providing agile and ubiquitous services for mobile devices (MDs) through offloading latency-critical tasks to edge service providers. However, it is challenging to make optimal decisions of computation offloading and resource allocation while ensuring the privacy and information security of MDs. Consequently, we consider a new vision of digital twin (DT) empowered edge networks, where the optimization problem is formulated as a two-stage incentive mechanism. First, the resource allocation strategy is determined by the interaction among DTs according to the credit-based incentives. Afterward, a distributed incentive mechanism based on the Stackelberg-based alternating direction method of multipliers is opted to obtain the optimal offloading and privacy investment strategies in parallel. Numerical results show that the proposed two-stage incentive mechanism achieves effective resource allocation and computation offloading while simultaneously improving the privacy and information security of MDs.
KW - Computation offloading
KW - digital twin (DT)
KW - Industrial Internet of Thing (IIoT)
KW - mobile edge computing (MEC)
KW - privacy investment
KW - resource allocation
KW - Stackelberg game
U2 - 10.1109/TII.2022.3184070
DO - 10.1109/TII.2022.3184070
M3 - Journal article
AN - SCOPUS:85133744432
VL - 19
SP - 3133
EP - 3143
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
IS - 3
ER -