论文标题

节能和物理层安全计算在区块链授权的物联网中卸载

Energy-Efficient and Physical Layer Secure Computation Offloading in Blockchain-Empowered Internet of Things

论文作者

Liu, Yiliang, Su, Zhou, Wang, Yuntao

论文摘要

本文研究了在区块链授权的物联网(IoT)中卸载的计算,其中任务数据上传链接从传感器到基站(BS)受到智能反射表面(IRS)辅助物理层安全性(PLS)的保护。接收任务数据后,BS分配了由移动边缘计算(MEC)服务器提供的计算资源来帮助传感器执行任务。现有的基于区块链的计算卸载方案通常集中于网络性能改进,例如能源消耗最小化或延迟最小化,而忽略了计算卸载的汽油费,从而导致高气提供者的不满意。同样,由于基于IRS的无线通道的时变特征,数据上传过程中的保密率无法通过稳定值来衡量,因此,在数据上传之前测量的秘密率的计算资源分配是不适当的。在本文中,我们设计了一种面向气体的计算卸载方案,该方案可以保证传感器的不满,同时降低能耗。此外,我们推断出IRS辅助PLS传输的急性保密率,该速度可以代表全球保密性能分配计算资源。模拟表明,与现有方案相比,所提出的计划的能源消耗较低,并确保支付较高气体的节点获得更强的计算资源。

This paper investigates computation offloading in blockchain-empowered Internet of Things (IoT), where the task data uploading link from sensors to a base station (BS) is protected by intelligent reflecting surface (IRS)-assisted physical layer security (PLS). After receiving task data, the BS allocates computational resources provided by mobile edge computing (MEC) servers to help sensors perform tasks. Existing blockchain-based computation offloading schemes usually focus on network performance improvements, such as energy consumption minimization or latency minimization, and neglect the Gas fee for computation offloading, resulting in the dissatisfaction of high Gas providers. Also, the secrecy rate during the data uploading process can not be measured by a steady value because of the time-varying characteristics of IRS-based wireless channels, thereby computational resources allocation with a secrecy rate measured before data uploading is inappropriate. In this paper, we design a Gas-oriented computation offloading scheme that guarantees a low degree of dissatisfaction of sensors, while reducing energy consumption. Also, we deduce the ergodic secrecy rate of IRS-assisted PLS transmission that can represent the global secrecy performance to allocate computational resources. The simulations show that the proposed scheme has lower energy consumption compared to existing schemes, and ensures that the node paying higher Gas gets stronger computational resources.

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