| JIN Kaiqi (金凯琦),WU Wenjun,GAO Yang,YIN Yufen,SI Pengbo.[J].高技术通讯(英文),2023,29(3):295~304 |
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| Deep reinforcement learning based task offloading in blockchain enabled smart city |
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| DOI：0. 3772/ j. issn. 1006-6748. 2023. 03. 008 |
| 中文关键词: |
| 英文关键词: mobile edge computing(MEC), blockchain, policy gradient, task offloading |
| 基金项目: |
| Author Name | Affiliation | | JIN Kaiqi (金凯琦) | (Faculty of Information Technology,Beijing University of Technology,Beijing 100124,P. R. China) | | WU Wenjun | | | GAO Yang | | | YIN Yufen | | | SI Pengbo | |
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| 中文摘要: |
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| 英文摘要: |
| With the expansion of cities and emerging complicated application,smart city has become an intelligent management mechanism. In order to guarantee the information security and quality of service (QoS) of the Internet of Thing(IoT) devices in the smart city,a mobile edge computing (MEC) enabled blockchain system is considered as the smart city scenario where the offloading process of computing tasks is a key aspect infecting the system performance in terms of service profit and latency.The task offloading process is formulated as a Markov decision process (MDP) and the optimal goal is the cumulative profit for the offloading nodes considering task profit and service latency cost,under the restriction of system timeout as well as processing resource. Then,a policy gradient based task offloading (PG-TO) algorithm is proposed to solve the optimization problem. Finally,the numerical result shows that the proposed PG-TO has better performance than the comparison algorithm,and the system performance as well as QoS is analyzed respectively. The testing result indicates that the proposed method has good generalization. |
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