{"title":"海洋物联网中的 NOMA 辅助安全计算卸载和资源分配","authors":"Wei Jiang;Xiao Yuan;Caishi Huang;Liping Qian","doi":"10.1109/TCCN.2024.3424845","DOIUrl":null,"url":null,"abstract":"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).","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"11 1","pages":"534-545"},"PeriodicalIF":7.0000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NOMA-Assisted Secure Computation Offloading and Resource Allocation in Marine Internet of Things\",\"authors\":\"Wei Jiang;Xiao Yuan;Caishi Huang;Liping Qian\",\"doi\":\"10.1109/TCCN.2024.3424845\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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).\",\"PeriodicalId\":13069,\"journal\":{\"name\":\"IEEE Transactions on Cognitive Communications and Networking\",\"volume\":\"11 1\",\"pages\":\"534-545\"},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Cognitive Communications and Networking\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10589463/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cognitive Communications and Networking","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10589463/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
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).
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.