海洋物联网中的 NOMA 辅助安全计算卸载和资源分配

IF 7 1区 计算机科学 Q1 TELECOMMUNICATIONS IEEE Transactions on Cognitive Communications and Networking Pub Date : 2024-07-08 DOI:10.1109/TCCN.2024.3424845
Wei Jiang;Xiao Yuan;Caishi Huang;Liping Qian
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引用次数: 0

摘要

在本文中,我们研究了一个具有高空平台(HAP)安全计算卸载风险的海上物联网(M-IoT)网络。为了保证HAP信息传输的安全性,我们利用一组无人水面车辆(usv)与一个HAP组成一个非正交多址(NOMA)传输组,提供同信道干扰。我们的目标是在满足安全性和时延要求的同时,共同优化HAP的计算卸载工作量、HAP的传输功率、数据传输时间和USV的传输功率,使总能耗最小化。虽然该问题是严格的非凸优化,但我们使用问题变换和垂直分解方法将问题分解为底层问题和顶层问题。底层问题是确定HAP的传输功率和HAP的计算卸载工作量,顶层问题是确定数据传输时间,并利用PPO (proximal policy optimization)进行解决。这两个子问题相互迭代求解,以获得最小的总能耗。仿真结果表明,该算法与异步优势actor- critical算法相比收敛速度更快,能耗降低30.34%。
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NOMA-Assisted Secure Computation Offloading and Resource Allocation in Marine Internet of Things
In this paper, we investigate a marine Internet of Things (M-IoT) network with high altitude platform (HAP) secure computation offloading at risk of eavesdropping. To ensure the security of HAP’s information transmission, we utilize a group of unmanned surface vehicles (USVs) with an HAP to form a non-orthogonal multiple access (NOMA) transmission group to provide co-channel interference. Our goal is to minimize the total energy consumption by jointly optimizing HAP’s computation offloading workload, HAP’s transmission power, data transmission time, and USV’s transmission power while meeting the security and delay requirements. Although this problem is strictly non-convex optimization, we use problem transformation and vertical decomposition methods to decompose the problem into an underlying problem and a top-level problem. The underlying problem is to determine the HAP’s transmission power and HAP’s computation offloading workload, and the top-level problem is to determine the data transmission time, which is solved by using proximal policy optimization (PPO). The two subproblems are solved iteratively over each other to obtain the minimum total energy consumption. Simulation results show that the proposed algorithm converges faster and reduces the energy consumption of 30.34% compared to asynchronous advantage actor-critic (A3C).
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来源期刊
IEEE Transactions on Cognitive Communications and Networking
IEEE Transactions on Cognitive Communications and Networking Computer Science-Artificial Intelligence
CiteScore
15.50
自引率
7.00%
发文量
108
期刊介绍: The IEEE Transactions on Cognitive Communications and Networking (TCCN) aims to publish high-quality manuscripts that push the boundaries of cognitive communications and networking research. Cognitive, in this context, refers to the application of perception, learning, reasoning, memory, and adaptive approaches in communication system design. The transactions welcome submissions that explore various aspects of cognitive communications and networks, focusing on innovative and holistic approaches to complex system design. Key topics covered include architecture, protocols, cross-layer design, and cognition cycle design for cognitive networks. Additionally, research on machine learning, artificial intelligence, end-to-end and distributed intelligence, software-defined networking, cognitive radios, spectrum sharing, and security and privacy issues in cognitive networks are of interest. The publication also encourages papers addressing novel services and applications enabled by these cognitive concepts.
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