Multipath transmission technology has recently emerged as a crucial solution to address bandwidth resource constraints and uneven load distribution across access points caused by the surge in data-intensive applications. A well-designed multipath scheduler can improve the quality of service and balance the power consumption in evolving Open Radio Access Networks (O-RANs). However, wireless channel instability and RAN heterogeneity challenge the scheduler’s bandwidth aggregation capability. This paper introduces a Neural Aggregation Bandwidth Optimization (NABO) scheduler for O-RAN, combining bandwidth prediction with scheduling policy optimization. NABO employs an innovative approach by first constructing a Transformer-optimized Throughput (ToT) prediction model based on historical path characteristics. To train the model, we design a system to simulate various network conditions and collect datasets. This model is then integrated into a dual-network collaborative learning framework that combines ToT predictions with heterogeneity levels to guide the scheduler’s optimization process. The ToT model achieves a throughput prediction error of less than 2%. In numerous heterogeneous simulation scenarios and real-world wireless environments, NABO significantly outperforms state-of-the-art multipath transmission methods, with bandwidth aggregation improvements of approximately 51% and 30% over existing benchmarks, respectively. These findings demonstrate NABO’s superior efficacy and potential in enhancing the performance and energy efficiency of O-RANs.
近来，多路径传输技术已成为解决带宽资源紧张和数据密集型应用激增造成的接入点负载分布不均问题的重要解决方案。精心设计的多路径调度器可以在不断发展的开放式无线接入网（O-RAN）中提高服务质量并平衡功耗。然而，无线信道的不稳定性和 RAN 的异构性对调度器的带宽聚合能力提出了挑战。本文介绍了用于 O-RAN 的神经聚合带宽优化（NABO）调度器，它将带宽预测与调度策略优化相结合。NABO 采用了一种创新方法，首先根据历史路径特征构建一个变压器优化吞吐量（ToT）预测模型。为了训练该模型，我们设计了一个系统来模拟各种网络条件并收集数据集。然后将该模型集成到双网络协同学习框架中，该框架将 ToT 预测与异构水平相结合，以指导调度器的优化过程。ToT 模型的吞吐量预测误差小于 2%。在众多异构模拟场景和真实无线环境中，NABO 的性能明显优于最先进的多径传输方法，带宽聚合分别比现有基准提高了约 51% 和 30%。这些发现证明了NABO在提高O-RAN性能和能效方面的卓越功效和潜力。
{"title":"An AI-Enhanced Multipath TCP Scheduler for Open Radio Access Networks","authors":"Wenxuan Qiao;Yuyang Zhang;Ping Dong;Xiaojiang Du;Hongke Zhang;Mohsen Guizani","doi":"10.1109/TGCN.2024.3424202","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3424202","url":null,"abstract":"Multipath transmission technology has recently emerged as a crucial solution to address bandwidth resource constraints and uneven load distribution across access points caused by the surge in data-intensive applications. A well-designed multipath scheduler can improve the quality of service and balance the power consumption in evolving Open Radio Access Networks (O-RANs). However, wireless channel instability and RAN heterogeneity challenge the scheduler’s bandwidth aggregation capability. This paper introduces a Neural Aggregation Bandwidth Optimization (NABO) scheduler for O-RAN, combining bandwidth prediction with scheduling policy optimization. NABO employs an innovative approach by first constructing a Transformer-optimized Throughput (ToT) prediction model based on historical path characteristics. To train the model, we design a system to simulate various network conditions and collect datasets. This model is then integrated into a dual-network collaborative learning framework that combines ToT predictions with heterogeneity levels to guide the scheduler’s optimization process. The ToT model achieves a throughput prediction error of less than 2%. In numerous heterogeneous simulation scenarios and real-world wireless environments, NABO significantly outperforms state-of-the-art multipath transmission methods, with bandwidth aggregation improvements of approximately 51% and 30% over existing benchmarks, respectively. These findings demonstrate NABO’s superior efficacy and potential in enhancing the performance and energy efficiency of O-RANs.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090990","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}
Open Radio Access Network (O-RAN) has brought a significant transformation in the field of communication networks. Its openness propels communication networks towards a more open, flexible, and efficient direction. Meanwhile, Unmanned Aerial Vehicle (UAV) communication, as a key technology in the sixth generation mobile communication network, offers more flexible and efficient solutions to address diverse environments and requirements. On this basis, we investigate the O-RAN-enabled UAV-assisted network architecture, in which the UAV assists the terrestrial network to enhance the wireless coverage performance. To further explore the advantages of the proposed architecture, we propose a joint problem involving radio unit association, aerial radio unit deployment, and resource allocation, with the objective of maximizing network energy efficiency. To tackle this problem, we design a double-loop-based algorithm. Specifically, we employ the Dinkelbach method in the outer loop to handle the fractional form of the objective function and devise an iterative algorithm based on Block Coordinate Descent architecture in the inner loop to optimize the decoupled sub-problems. Comprehensive simulation results are provided to verify the effectiveness of the proposal.
{"title":"Energy-Efficient Deployment and Resource Allocation for O-RAN-Enabled UAV-Assisted Communication","authors":"Huan Li;Xiao Tang;Daosen Zhai;Ruonan Zhang;Bin Li;Haotong Cao;Neeraj Kumar;Ahmad Almogren","doi":"10.1109/TGCN.2024.3422393","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3422393","url":null,"abstract":"Open Radio Access Network (O-RAN) has brought a significant transformation in the field of communication networks. Its openness propels communication networks towards a more open, flexible, and efficient direction. Meanwhile, Unmanned Aerial Vehicle (UAV) communication, as a key technology in the sixth generation mobile communication network, offers more flexible and efficient solutions to address diverse environments and requirements. On this basis, we investigate the O-RAN-enabled UAV-assisted network architecture, in which the UAV assists the terrestrial network to enhance the wireless coverage performance. To further explore the advantages of the proposed architecture, we propose a joint problem involving radio unit association, aerial radio unit deployment, and resource allocation, with the objective of maximizing network energy efficiency. To tackle this problem, we design a double-loop-based algorithm. Specifically, we employ the Dinkelbach method in the outer loop to handle the fractional form of the objective function and devise an iterative algorithm based on Block Coordinate Descent architecture in the inner loop to optimize the decoupled sub-problems. Comprehensive simulation results are provided to verify the effectiveness of the proposal.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090830","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}
Pub Date : 2024-06-25DOI: 10.1109/TGCN.2024.3418948
Anderson Augusto Simiscuka;Mohammed Amine Togou;Mikel Zorrilla;Gabriel-Miro Muntean
There is increasing viewer interest and technological support for streaming immersive clips over the Internet. There are, however, challenges in supporting high quality of viewer experience, mostly due to the large amounts of the data associated with immersive video and spatial audio (Ambisonics). In situations where there are limited network resources, the streamed 360° content needs to be adjusted dynamically to meet the network constraints. Dynamic Adaptive Streaming over HTTP (DASH) adaptation is a key technology for delivering high-quality video over open radio access networks (RANs). DASH allows for efficient adaptation of video streams to the available network conditions. This paper introduces 360-ADAPT, a DASH-based adaptation solution on an Open-RAN architecture for increased quality remote 360° opera experiences. Unlike existing schemes, 360-ADAPT gives precedence to audio over the video when selecting bitrates, increasing the overall quality of the artistic act and improving use of resources and energy. The proposed 360-ADAPT was tested with real opera viewers in the context of an artistic-oriented platform for opera delivery, part of the Horizon2020 TRACTION project. Results indicate that 360-ADAPT achieves higher perceived quality levels than alternative solutions both in QoS and QoE metrics.
{"title":"360-ADAPT: An Open-RAN-Based Adaptive Scheme for Quality Enhancement of Opera 360° Content Distribution","authors":"Anderson Augusto Simiscuka;Mohammed Amine Togou;Mikel Zorrilla;Gabriel-Miro Muntean","doi":"10.1109/TGCN.2024.3418948","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3418948","url":null,"abstract":"There is increasing viewer interest and technological support for streaming immersive clips over the Internet. There are, however, challenges in supporting high quality of viewer experience, mostly due to the large amounts of the data associated with immersive video and spatial audio (Ambisonics). In situations where there are limited network resources, the streamed 360° content needs to be adjusted dynamically to meet the network constraints. Dynamic Adaptive Streaming over HTTP (DASH) adaptation is a key technology for delivering high-quality video over open radio access networks (RANs). DASH allows for efficient adaptation of video streams to the available network conditions. This paper introduces 360-ADAPT, a DASH-based adaptation solution on an Open-RAN architecture for increased quality remote 360° opera experiences. Unlike existing schemes, 360-ADAPT gives precedence to audio over the video when selecting bitrates, increasing the overall quality of the artistic act and improving use of resources and energy. The proposed 360-ADAPT was tested with real opera viewers in the context of an artistic-oriented platform for opera delivery, part of the Horizon2020 TRACTION project. Results indicate that 360-ADAPT achieves higher perceived quality levels than alternative solutions both in QoS and QoE metrics.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10571391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The low-carbon and efficient operation of smart parks requires high-precision and real-time energy management model training. Multi-mode power Internet of Things (PIoT) consisting of open radio access networks (O-RAN) and power line communications (PLC) can effectively improve the model training performance. However, the negative effects of network threats, such as model inversion attacks, cannot be neglected. To solve this problem, we propose a diFferential pRivacy-aware gEnErative aDversarial netwOrk-assisted resource scheduling algorithM (FREEDOM). Firstly, we integrate a differential privacy mechanism with the energy management model training process and the related system model. Then, a joint resource scheduling optimization problem is constructed, the goal of which is to minimize the weighted sum of the global loss function, total energy consumption, and differential privacy cost under the long-term differential privacy constraint. The original problem is converted based on virtual queue theory and addressed by the FREEDOM. FREEDOM leverages a deep Q-learning network (DQN) to learn the resource scheduling strategy via differential privacy awareness. It improves optimization and convergence performances with the assistance of generative adversarial network (GAN). Simulation results show that FREEDOM can achieve excellent performances of global loss function, total energy consumption, differential privacy cost, and privacy preservation.
{"title":"Differential Privacy-Aware Generative Adversarial Network-Assisted Resource Scheduling for Green Multi-Mode Power IoT","authors":"Sunxuan Zhang;Jiapeng Xue;Jiayi Liu;Zhenyu Zhou;Xiaomei Chen;Shahid Mumtaz","doi":"10.1109/TGCN.2024.3417379","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3417379","url":null,"abstract":"The low-carbon and efficient operation of smart parks requires high-precision and real-time energy management model training. Multi-mode power Internet of Things (PIoT) consisting of open radio access networks (O-RAN) and power line communications (PLC) can effectively improve the model training performance. However, the negative effects of network threats, such as model inversion attacks, cannot be neglected. To solve this problem, we propose a diFferential pRivacy-aware gEnErative aDversarial netwOrk-assisted resource scheduling algorithM (FREEDOM). Firstly, we integrate a differential privacy mechanism with the energy management model training process and the related system model. Then, a joint resource scheduling optimization problem is constructed, the goal of which is to minimize the weighted sum of the global loss function, total energy consumption, and differential privacy cost under the long-term differential privacy constraint. The original problem is converted based on virtual queue theory and addressed by the FREEDOM. FREEDOM leverages a deep Q-learning network (DQN) to learn the resource scheduling strategy via differential privacy awareness. It improves optimization and convergence performances with the assistance of generative adversarial network (GAN). Simulation results show that FREEDOM can achieve excellent performances of global loss function, total energy consumption, differential privacy cost, and privacy preservation.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090699","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}
Pub Date : 2024-06-19DOI: 10.1109/TGCN.2024.3417298
Suleman Khan;Gurjot Singh Gaba;Andrei Gurtov;Leonardus J. A. Jansen;Nils Mäurer;Corinna Schmitt
The L-band Digital Aeronautical Communications System (LDACS) is a key advancement for next-generation aviation networks, enhancing Communication, Navigation, and Surveillance (CNS) capabilities. It operates with VHF Datalink mode 2 (VDLm2) and features a seamless handover mechanism to maintain uninterrupted communication between aircraft and ground stations (GSs), improving safety and efficiency in air traffic management (ATM). However, LDACS’ handover process encounters significant security risks due to inadequate authentication and key agreement between aircraft and ground station controllers (GSCs) during handovers. This vulnerability threatens communications’ confidentiality, integrity, and authenticity, posing risks to flight safety and sensitive data. Therefore, developing and implementing a robust security framework to protect aviation communications is essential. In response, we have proposed a security solution specifically designed to protect LDACS handovers. Our solution uses a mutual authentication and key agreement mechanism tailored for LDACS handovers, ensuring robust security for all types of handovers, including Intra GSC - Intra Aeronautical Telecommunication Network (ATN), Inter GSC - Intra ATN, and Inter GSC - Inter ATN. Our approach utilizes post-quantum cryptography to protect aviation communication systems against potential post-quantum threats, such as unauthorized access to flight data, interception of communication, and spoofing of aircraft identity. Furthermore, our proposed solution has undergone a thorough informal security analysis to ensure its effectiveness in addressing handover challenges and offering robust protection against various threats. It seamlessly integrates with the LDACS framework, delivering low Bit Error Rate (BER) and latency levels, making it a highly reliable approach in practice.
{"title":"Post-Quantum Secure Handover Mechanism for Next-Generation Aviation Communication Networks","authors":"Suleman Khan;Gurjot Singh Gaba;Andrei Gurtov;Leonardus J. A. Jansen;Nils Mäurer;Corinna Schmitt","doi":"10.1109/TGCN.2024.3417298","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3417298","url":null,"abstract":"The L-band Digital Aeronautical Communications System (LDACS) is a key advancement for next-generation aviation networks, enhancing Communication, Navigation, and Surveillance (CNS) capabilities. It operates with VHF Datalink mode 2 (VDLm2) and features a seamless handover mechanism to maintain uninterrupted communication between aircraft and ground stations (GSs), improving safety and efficiency in air traffic management (ATM). However, LDACS’ handover process encounters significant security risks due to inadequate authentication and key agreement between aircraft and ground station controllers (GSCs) during handovers. This vulnerability threatens communications’ confidentiality, integrity, and authenticity, posing risks to flight safety and sensitive data. Therefore, developing and implementing a robust security framework to protect aviation communications is essential. In response, we have proposed a security solution specifically designed to protect LDACS handovers. Our solution uses a mutual authentication and key agreement mechanism tailored for LDACS handovers, ensuring robust security for all types of handovers, including Intra GSC - Intra Aeronautical Telecommunication Network (ATN), Inter GSC - Intra ATN, and Inter GSC - Inter ATN. Our approach utilizes post-quantum cryptography to protect aviation communication systems against potential post-quantum threats, such as unauthorized access to flight data, interception of communication, and spoofing of aircraft identity. Furthermore, our proposed solution has undergone a thorough informal security analysis to ensure its effectiveness in addressing handover challenges and offering robust protection against various threats. It seamlessly integrates with the LDACS framework, delivering low Bit Error Rate (BER) and latency levels, making it a highly reliable approach in practice.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090992","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}
The growing demand for advanced indoor communication capabilities in sixth-generation (6G) networks has led to extensive research into integrating Cognitive Radio (CR) and multiple-input multiple-output (MIMO) technologies with Unmanned Aerial Vehicles (UAVs). The integration of CR and MIMO with UAVs contributes to the green Open Radio Access Network (O-RAN) paradigm by leveraging CR’s advantages in interoperability, adaptability, and software-defined nature, along with UAVs’ flexible 3D movement and MIMO’s energy-efficient attributes. However, securing communication in CR-enabled MIMO-equipped UAVs in O-RAN networks against jamming attacks presents significant challenges, particularly in designing resource allocation algorithms that are both secure and energy-efficient in the presence of jamming attacks. This paper presents a secure and jamming-resistant green channel-assignment algorithm designed for indoor uplink communication in MIMO- and CR-enabled O-RAN-supported UAV networks. The proposed algorithm aims to maximize served transmissions with minimal total transmission power, exploiting MIMO, CR adaptability, and jamming awareness. Leveraging the Lagrangian technique, a closed-form formula for per-antenna power allocation is derived to solve the power minimization problem for each UAV over the available channels. Using the obtained per-UAV powers on idle channels, a power-efficient batch-based channel-assignment problem is formulated, presented as unimodular binary-linear programming solvable through polynomial-time linear programming. Compared to CR MIMO-based algorithms, the proposed algorithm significantly improves overall network performance under jamming attacks by employing user-batching with jamming awareness.
第六代（6G）网络对先进室内通信能力的需求日益增长，这促使人们开始广泛研究如何将认知无线电（CR）和多输入多输出（MIMO）技术与无人机（UAV）相结合。通过利用 CR 在互操作性、适应性和软件定义特性方面的优势，以及无人机灵活的 3D 移动和 MIMO 的高能效属性，CR 和 MIMO 与无人机的集成为绿色开放式无线接入网（O-RAN）范例做出了贡献。然而，如何在 O-RAN 网络中确保支持 CR 的配备 MIMO 的无人机的通信安全免受干扰攻击是一项重大挑战，特别是在设计资源分配算法时要考虑到干扰攻击的安全性和能效。本文针对支持 MIMO 和 CR 的 O-RAN 无人机网络中的室内上行链路通信，提出了一种安全且抗干扰的绿色信道分配算法。所提出的算法旨在利用 MIMO、CR 适应性和干扰意识，以最小的总传输功率实现最大的服务传输。利用拉格朗日技术，得出了每个天线功率分配的闭式公式，以解决每个无人机在可用信道上的功率最小化问题。利用在空闲信道上获得的每架无人机功率，制定了一个基于批量的功率效率信道分配问题，并将其表述为可通过多项式时间线性规划求解的单模态二元线性规划。与基于多输入多输出（CR MIMO）的算法相比，所提出的算法通过采用具有干扰意识的用户批处理，显著提高了干扰攻击下的整体网络性能。
{"title":"Secure Batch-Based Resource Allocation for Green Cognitive MIMO Indoor Flying Networks","authors":"Haythem Bany Salameh;Haitham Al-Obiedollah;Moayad Aloqaily","doi":"10.1109/TGCN.2024.3387899","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3387899","url":null,"abstract":"The growing demand for advanced indoor communication capabilities in sixth-generation (6G) networks has led to extensive research into integrating Cognitive Radio (CR) and multiple-input multiple-output (MIMO) technologies with Unmanned Aerial Vehicles (UAVs). The integration of CR and MIMO with UAVs contributes to the green Open Radio Access Network (O-RAN) paradigm by leveraging CR’s advantages in interoperability, adaptability, and software-defined nature, along with UAVs’ flexible 3D movement and MIMO’s energy-efficient attributes. However, securing communication in CR-enabled MIMO-equipped UAVs in O-RAN networks against jamming attacks presents significant challenges, particularly in designing resource allocation algorithms that are both secure and energy-efficient in the presence of jamming attacks. This paper presents a secure and jamming-resistant green channel-assignment algorithm designed for indoor uplink communication in MIMO- and CR-enabled O-RAN-supported UAV networks. The proposed algorithm aims to maximize served transmissions with minimal total transmission power, exploiting MIMO, CR adaptability, and jamming awareness. Leveraging the Lagrangian technique, a closed-form formula for per-antenna power allocation is derived to solve the power minimization problem for each UAV over the available channels. Using the obtained per-UAV powers on idle channels, a power-efficient batch-based channel-assignment problem is formulated, presented as unimodular binary-linear programming solvable through polynomial-time linear programming. Compared to CR MIMO-based algorithms, the proposed algorithm significantly improves overall network performance under jamming attacks by employing user-batching with jamming awareness.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090867","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}
With the proliferation of mobile devices and connected terminals, the mobile data traffic has witnessed an unprecedented upsurge. The increasing energy consumption owing to the massive machine type communication is the main challenge in radio access networks (RANs). Thus, energy optimized mobile networks are very important for sustainable future green communication. This paper presents an efficient approach for improving the efficiency of RAN by proposing an active-IRS aided framework. The multiple active IRSs assist the user communication by amplifying the incident signals before transmission. The system power usage is determined through a proposed power consumption model with minimum energy overhead. Further, resource management is enabled in the network through a proposed algorithm. The system rate and energy performance is obtained for different values of IRS power budget, output power and amplitude gain subject to the constraint of maximum amplification power. It is observed that maximum amplification power $P_{max}$ of 20 dBm yields maximum achievable rate of 16.2 bits/s/Hz. Also, the gain in energy efficiency is 20.79% when $P_{max}$ is changed from 0 dBm to 10 dBm. In the end, the comparison of active IRS system and passive IRS system with resource control is also carried out.
{"title":"GCIRM: Toward Green Communication With Intelligent Resource Management Scheme for Radio Access Networks","authors":"Ashu Taneja;Shalli Rani;Rajesh Kumar Dhanaraj;Lewis Nkenyereye","doi":"10.1109/TGCN.2024.3384542","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3384542","url":null,"abstract":"With the proliferation of mobile devices and connected terminals, the mobile data traffic has witnessed an unprecedented upsurge. The increasing energy consumption owing to the massive machine type communication is the main challenge in radio access networks (RANs). Thus, energy optimized mobile networks are very important for sustainable future green communication. This paper presents an efficient approach for improving the efficiency of RAN by proposing an active-IRS aided framework. The multiple active IRSs assist the user communication by amplifying the incident signals before transmission. The system power usage is determined through a proposed power consumption model with minimum energy overhead. Further, resource management is enabled in the network through a proposed algorithm. The system rate and energy performance is obtained for different values of IRS power budget, output power and amplitude gain subject to the constraint of maximum amplification power. It is observed that maximum amplification power \u0000<inline-formula> <tex-math>$P_{max}$ </tex-math></inline-formula>\u0000 of 20 dBm yields maximum achievable rate of 16.2 bits/s/Hz. Also, the gain in energy efficiency is 20.79% when \u0000<inline-formula> <tex-math>$P_{max}$ </tex-math></inline-formula>\u0000 is changed from 0 dBm to 10 dBm. In the end, the comparison of active IRS system and passive IRS system with resource control is also carried out.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090991","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}
Pub Date : 2024-03-21DOI: 10.1109/TGCN.2024.3403662
Ankur Jaiswal;Salla Shivateja;Abhishek Hazra;Nabajyoti Mazumdar;Jagpreet Singh;Varun G. Menon
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.
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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.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-21DOI: 10.1109/TGCN.2024.3403901
Junaid Akram;Ali Anaissi;Rajkumar Singh Rathore;Rutvij H. Jhaveri;Awais Akram
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.
{"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":null,"pages":null},"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}
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