Final published version
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 - Service Offloading with Deep Q-Network for Digital Twinning-Empowered Internet of Vehicles in Edge Computing
AU - Xu, Xiaolong
AU - Shen, Bowen
AU - Ding, Sheng
AU - Srivastava, Gautam
AU - Bilal, Muhammad
AU - Khosravi, Mohammad R.
AU - Menon, Varun G.
AU - Jan, Mian Ahmad
AU - Wang, Maoli
PY - 2022/2/1
Y1 - 2022/2/1
N2 - With the potential of implementing computing-intensive applications, edge computing is combined with digital twinning (DT)-empowered Internet of vehicles (IoV) to enhance intelligent transportation capabilities. By updating digital twins of vehicles and offloading services to edge computing devices (ECDs), the insufficiency in vehicles’ computational resources can be complemented. However, owing to the computational intensity of DT-empowered IoV, ECD would overload under excessive service requests, which deteriorates the quality of service (QoS). To address this problem, in this article, a multiuser offloading system is analyzed, where the QoS is reflected through the response time of services. Then, a service offloading (SOL) method with deep reinforcement learning, is proposed for DT-empowered IoV in edge computing. To obtain optimized offloading decisions, SOL leverages deep Q-network (DQN), which combines the value function approximation of deep learning and reinforcement learning. Eventually, experiments with comparative methods indicate that SOL is effective and adaptable in diverse environments.
AB - With the potential of implementing computing-intensive applications, edge computing is combined with digital twinning (DT)-empowered Internet of vehicles (IoV) to enhance intelligent transportation capabilities. By updating digital twins of vehicles and offloading services to edge computing devices (ECDs), the insufficiency in vehicles’ computational resources can be complemented. However, owing to the computational intensity of DT-empowered IoV, ECD would overload under excessive service requests, which deteriorates the quality of service (QoS). To address this problem, in this article, a multiuser offloading system is analyzed, where the QoS is reflected through the response time of services. Then, a service offloading (SOL) method with deep reinforcement learning, is proposed for DT-empowered IoV in edge computing. To obtain optimized offloading decisions, SOL leverages deep Q-network (DQN), which combines the value function approximation of deep learning and reinforcement learning. Eventually, experiments with comparative methods indicate that SOL is effective and adaptable in diverse environments.
KW - Deep reinforcement learning (DRL)
KW - digital twinning (DT)
KW - edge computing
KW - Internet of vehicles (IoV)
KW - service offloading (SOL)
U2 - 10.1109/TII.2020.3040180
DO - 10.1109/TII.2020.3040180
M3 - Journal article
AN - SCOPUS:85097199623
VL - 18
SP - 1414
EP - 1423
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
IS - 2
ER -