IEEE Transactions on Green Communications and Networking最新文献_第9页

6G⁺ Networks Through Enhanced Efficiency and Sustainability With MADDPG-Driven Network Slicing in SoS Environments 在 SoS 环境中通过 MADDPG 驱动的网络切片提高 6G⁺ 网络的效率和可持续性

IF 5.3 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2024-03-23 DOI: 10.1109/TGCN.2024.3404500

Andreas Andreou;Constandinos X. Mavromoustakis

{"title":"6G⁺ Networks Through Enhanced Efficiency and Sustainability With MADDPG-Driven Network Slicing in SoS Environments","authors":"Andreas Andreou;Constandinos X. Mavromoustakis","doi":"10.1109/TGCN.2024.3404500","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3404500","url":null,"abstract":"This study explores the integration of sustainable practices in the advancing domain of sixth-generation and beyond (6G+) network technologies, with a particular focus on enhancing the efficiency of search and rescue operations. It presents a comprehensive strategy for network slicing designed to bolster seamless communication and operational efficacy of emergency response teams in varied and ever-changing conditions. It presents an innovative approach to managing workload fluctuations in network slicing. Also, it introduces a new slice configuration mechanism to prioritize signals for devices within the complex, compelling, hierarchical network systems. Incorporating a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is central to the approach, tackling the complexity of implementing effective communication strategies across multiple network layers. Our findings demonstrate a highly adaptable and real-time slice configuration technique within System of Systems (SoS) environments, offering significant enhancements in systems engineering and emergency communication management. This approach contributes to the robustness and reliability of emergency response communications and underscores the importance of integrating environmental sustainability in developing next-generation network technologies.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1752-1761"},"PeriodicalIF":5.3,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two-Stage Dilated Convolutional Neural Network-Based Detector for OFDM-IM 基于两级稀释卷积神经网络的 OFDM-IM 检测器

IF 5.3 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2024-03-21 DOI: 10.1109/TGCN.2024.3403843

Ruiyan Du;Huifang Wang;Shiyi Wang;Baozhu Shi;Zhuoyao Duan;Fulai Liu

{"title":"Two-Stage Dilated Convolutional Neural Network-Based Detector for OFDM-IM","authors":"Ruiyan Du;Huifang Wang;Shiyi Wang;Baozhu Shi;Zhuoyao Duan;Fulai Liu","doi":"10.1109/TGCN.2024.3403843","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3403843","url":null,"abstract":"As a key emerging green communication technology, signal detection based on deep learning can improve communication performance for orthogonal frequency division multiplexing with index modulation (OFDM-IM). However, it may lead to an increase in the bit error rate (BER) when the index and carrier are detected as a whole. To tackle this problem, a two-stage dilated convolutional neural network based on OFDM-IM (TS-DCNN-IM) is presented to improve signal detection performance in this paper. Through the two-stage design, the index and carrier can be processed separately by different subnetworks, thereby achieving better detection performance. In the first stage, an index subnetwork based on CNN is designed to obtain the index information of the carriers. Specifically, a dilated convolution module is introduced into the index subnetwork to better extract the carrier features, which is achieved by enlarging the receptive field without adding the network parameters. In the second stage, a deep neural network is constructed to predict the transmitted signal bits. Finally, the well-trained TS-DCNN-IM model is used to directly output the transmitted signal bits. Simulation results show that compared to the related algorithms, the TS-DCNN-IM algorithm can achieve better BER performance and higher computational efficiency.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1852-1861"},"PeriodicalIF":5.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UAV-Enabled Mobile RAN and RF-Energy Transfer Protocol for Enabling Sustainable IoT in Energy-Constrained Networks 支持无人机的移动 RAN 和射频能源传输协议，在能源受限网络中实现可持续物联网

IF 5.3 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2024-03-21 DOI: 10.1109/TGCN.2024.3403662

Ankur Jaiswal;Salla Shivateja;Abhishek Hazra;Nabajyoti Mazumdar;Jagpreet Singh;Varun G. Menon

{"title":"UAV-Enabled Mobile RAN and RF-Energy Transfer Protocol for Enabling Sustainable IoT in Energy-Constrained Networks","authors":"Ankur Jaiswal;Salla Shivateja;Abhishek Hazra;Nabajyoti Mazumdar;Jagpreet Singh;Varun G. Menon","doi":"10.1109/TGCN.2024.3403662","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3403662","url":null,"abstract":"This article introduces a novel approach for Unmanned Aerial Vehicles (UAV) assisted wireless power transfer (WPT) within a Radio Access Network (RAN) provisioned Internet of Things (IoT) network. The goal is to efficiently charge scattered IoT Nodes (INs) within their respective energy deadlines. The proposed methodology combines the concepts of Radio Frequency Energy Transfer (RFET) zones, K-means clustering, and Ant Colony Optimization (ACO) to optimize the charging process. Initially, RFET zones are formed around the INs, and K-means clustering is applied to group nodes based on their spatial proximity and energy requirements. Subsequently modified ACO algorithm is employed to construct efficient paths for UAVs to visit these clusters. This is achieved by taking into account several aspects such as node deadlines and UAV capacity, thereby assuring the timely and efficient transmission of energy. After comparative analysis with EUP-ACS and IA-DRL, the proposed algorithm achieves a substantial reduction of 22.22% and 36.36% respectively in UAV usage, while also exhibiting significant improvements in RFET zones, energy efficiency, and survival rate, confirming its effectiveness in enhancing charging performance, reducing energy waste, and meeting deadlines.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 3","pages":"1118-1127"},"PeriodicalIF":5.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly Detection Algorithm of Industrial Internet of Things Data Platform Based on Deep Learning 基于深度学习的工业物联网数据平台异常检测算法

IF 5.3 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2024-03-21 DOI: 10.1109/TGCN.2024.3403102

Xing Li;Chao Xie;Zhijia Zhao;Chunbao Wang;Huajun Yu

{"title":"Anomaly Detection Algorithm of Industrial Internet of Things Data Platform Based on Deep Learning","authors":"Xing Li;Chao Xie;Zhijia Zhao;Chunbao Wang;Huajun Yu","doi":"10.1109/TGCN.2024.3403102","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3403102","url":null,"abstract":"The development of the Internet of Things (IoT) causes most industrial applications to utilize IoT devices to improve their productivity. Applications such as smart cities, energy management, smart homes, smart cars, and supply chain management widely utilize the IoT to manage the industries’ efficiency. Industrial IoT devices are frequently affected by cybercriminals and damage information and productivity. Criminal activities can be overcome by applying various machine-learning techniques. Existing methods can process intermediate attacks; however, traditional machine learning techniques have difficulties predicting adversarial and catastrophic attacks. In addition, most of the AI-based industrial applications have heterogeneous and mixed data, requiring robust intruder detection systems. The research issues are addressed by introducing the Meta-Heuristic Optimized Deep Random Neural Networks (MH-DRNN). The system uses the optimization process in feature selection and classification, reducing the heterogeneous data analysis issues. The optimization method selects the features from the feature set according to the sunflower movement, which minimizes the difficulties in computation. In addition, three MLP and three recurrent layers are incorporated into this system to maximize the prediction rate up to 99.2% accuracy.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 3","pages":"1037-1048"},"PeriodicalIF":5.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital Twin-Driven Trust Management in Open RAN-Based Spatial Crowdsourcing Drone Services 基于开放 RAN 的空间众包无人机服务中的数字双胞胎驱动的信任管理

IF 5.3 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2024-03-21 DOI: 10.1109/TGCN.2024.3403901

Junaid Akram;Ali Anaissi;Rajkumar Singh Rathore;Rutvij H. Jhaveri;Awais Akram

{"title":"Digital Twin-Driven Trust Management in Open RAN-Based Spatial Crowdsourcing Drone Services","authors":"Junaid Akram;Ali Anaissi;Rajkumar Singh Rathore;Rutvij H. Jhaveri;Awais Akram","doi":"10.1109/TGCN.2024.3403901","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3403901","url":null,"abstract":"We introduce “TMIoDT,” a pioneering framework aimed at bolstering communication security in the Internet of Drone Things (IoDT) integrated with Open Radio Access Networks (Open RAN), with a specific focus on bushfire monitoring applications. Our novel contributions include the seamless integration of digital twin technology with blockchain to establish a robust trust management system in the IoDT context. This approach addresses the critical vulnerabilities associated with unsecured wireless networks in IoDT, such as data integrity issues and susceptibility to cyber threats. The TMIoDT framework encompasses a mutual authentication mechanism to secure interactions and key exchanges among IoDT entities, including drones and Unmanned Ground Vehicles (UGVs). Furthermore, it leverages blockchain technology for credible trust management and employs digital twins to model UGV servers accurately, enhancing IoDT relationship modeling. An advanced Intrusion Detection System (IDS), utilizing Stacked Variational Autoencoder (SVA) and Attention-based Bidirectional LSTM (ABL), is implemented for anomaly detection, complemented by a blockchain-based transaction writing scheme for secure data verification. Our comprehensive evaluation, utilizing the ToN-IoT and ICIDS-2017 network intrusion datasets, confirms TMIoDT’s effectiveness in significantly improving communication security and reliability in IoDT.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 3","pages":"1061-1075"},"PeriodicalIF":5.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Optimization of IRS Location and Passive Beamforming for Enhanced Received Power 联合优化 IRS 定位和无源波束成形以增强接收功率

IF 5.3 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2024-03-20 DOI: 10.1109/TGCN.2024.3403527

Jyotsna Rani;Deepak Mishra;Ganesh Prasad;Ashraf Hossain;Swades De;Kuntal Deka

{"title":"Joint Optimization of IRS Location and Passive Beamforming for Enhanced Received Power","authors":"Jyotsna Rani;Deepak Mishra;Ganesh Prasad;Ashraf Hossain;Swades De;Kuntal Deka","doi":"10.1109/TGCN.2024.3403527","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3403527","url":null,"abstract":"Intelligent reflecting surface (IRS) has recently emerged as a promising technology for beyond fifth-generation (B5G) networks conceived from metamaterials that smartly tunes the signal reflections via a large number of low-cost passive reflecting elements. However, the IRS-assisted communication model and the optimization of available resources needs to be improved further for more efficient communications. This paper investigates the enhancement of received power in an IRS-assisted wireless communication by jointly optimizing the phase shifts at the IRS elements and its location. Employing the conventional Friss transmission model, the relationship between the transmitted power and reflected power is established. The expression of the received power incorporates the free space loss, reflection loss factor, physical dimension of the IRS panel, and radiation pattern of the transmit signal. Also, the expression of reflection coefficient of IRS panel is obtained by exploiting the existing data of radar communications. Initially exploring a single IRS element within a two-ray reflection model, we extend it to a more complex multi-ray reflection model with multiple IRS elements in 3D Cartesian space. The expression of the received power in both the cases is derived in a more tractable form, and then, it is maximized by jointly optimizing the underlying variables, i.e., the IRS location and the phase shifts. Further, the optimization of resources are investigated in active IRS, multiple access, and joint active and passive beamforming. Numerical insights and performance comparison reveal that joint optimization leads to a substantial 37% enhancement in received power compared to the closest competitive benchmark.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1970-1984"},"PeriodicalIF":5.3,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Communications Society Information IEEE 通信学会信息

IF 4.8 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2024-03-20 DOI: 10.1109/TGCN.2024.3394073

引用次数: 0

Joint Data Offloading and Energy-Efficient Secure MEC Resource Allocation Method for IoT Device Data in RAN Communication 针对 RAN 通信中物联网设备数据的联合数据卸载和高能效安全 MEC 资源分配方法

IF 5.3 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2024-03-20 DOI: 10.1109/TGCN.2024.3379960

Qiang He;Ji Li;Xiaogang Zhu;Alireza Jolfaei;Zheng Feng;Amr Tolba;Keping Yu;Yukai Fu

引用次数: 0

IEEE Transactions on Green Communications and Networking 电气和电子工程师学会绿色通信与网络论文集

IF 4.8 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2024-03-20 DOI: 10.1109/TGCN.2024.3394071

引用次数: 0

Adaptive Frame Coalescing in Energy Efficient Ethernet With Model Predictive Control and Queuing Theory 利用模型预测控制和队列理论在高能效以太网中实现自适应帧聚合

IF 5.3 2区计算机科学

IEEE Transactions on Green Communications and Networking Pub Date : 2024-03-19 DOI: 10.1109/TGCN.2024.3379289

Omer Gursoy;Nail Akar

{"title":"Adaptive Frame Coalescing in Energy Efficient Ethernet With Model Predictive Control and Queuing Theory","authors":"Omer Gursoy;Nail Akar","doi":"10.1109/TGCN.2024.3379289","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3379289","url":null,"abstract":"Frame coalescing is a well-established technique which manages the low power idle (LPI) mode supported by energy efficient Ethernet (EEE) interfaces. Frame coalescing enables EEE interfaces to remain in the LPI mode for a certain amount of time upon the arrival of the first frame (timer-based coalescing), or until a predefined amount of traffic accumulates in the transmission buffer (size-based coalescing). In this paper, we propose a novel open-loop dynamic coalescing technique that is based on model predictive control (MPC) and queuing theory. In contrast to conventional timer-based coalescing, the proposed method enables the update of the timer parameter repeatedly throughout the duration of the LPI mode of a single coalescing cycle by taking into account the arrival instants and sizes, of the frames waiting in the buffer. Two different methods, namely MPC-mean and MPC-tail, are proposed which attempt to minimize the energy consumption of the Ethernet link, under constraints on mean and tail of the queue waiting time, respectively. The effectiveness of the proposed dynamic MPC-based coalescing algorithms are validated using simulations with synthetic and actual traffic traces.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1574-1585"},"PeriodicalIF":5.3,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0