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  • 标题:Optimized Bandwidth Allocation for MEC Server in Blockchain-Enabled IoT Networks
  • 本地全文:下载
  • 作者:Shengcheng Ma ; Wei-Tek Tsai ; Shuai Wang
  • 期刊名称:Scientific Programming
  • 印刷版ISSN:1058-9244
  • 出版年度:2022
  • 卷号:2022
  • DOI:10.1155/2022/6129150
  • 语种:English
  • 出版社:Hindawi Publishing Corporation
  • 摘要:Powered by the development of the fifth-generation mobile communication technology (5G), the Internet of things (IoT) has been widely applied in people’s life. Due to the limitation of storage and computing power, the data transmission of IoT devices faces challenges in terms of security and privacy. Therefore, many researchers provide the conjunction of blockchain and mobile edge computing (MEC) to make up for the lack of computing and security. MEC can meet the storage and computing requirements for IoT devices. Blockchain can provide a decentralized, antitamper solution that can help devices overcome security deficiencies, whereas the speed of blockchain communication is not fast enough because of the consensus mechanism. In this article, we focus on the permissioned blockchain and propose an optimized bandwidth allocation algorithm to promote the performance of consensus communication. The algorithm contains an In-Network control ideology and supports deployment on MEC servers. Deep reinforcement learning (DRL) is employed to perform the computation of available bandwidth in our scheme. We implement a prototype system in the testbed and perform a simulation, and the results show the advantages compared with the current widely used algorithm. By applying our method, the Internet of things devices can transmit data safely and efficiently.
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