Pub Date : 2025-11-19DOI: 10.1109/TGCN.2025.3635041
Yue Yu;Zheng Zhao;Yuxi Zhao;Yi Zhong;Iztok Humar;Xiaohu Ge
The integration of renewable energy into cellular networks has become a key strategy to reduce carbon emissions. However, the inherent variability of renewable sources and the fluctuating power demands of the network often lead to energy mismatches, thus affecting the carbon efficiency. To address this challenge, this paper proposes a resource-allocation and energy-management framework for cellular networks with a hybrid-energy supply, aiming to maximize the carbon efficiency. The proposed framework adopts an outer-layer BS-sleeping algorithm and an inner iterative optimization that updates user association, battery management, and energy sharing scheme. Specifically, a user-association strategy is developed based on Dinkelbach’s method and convex-concave optimization, ensuring efficient energy utilization, while maintaining the quality of service (QoS). Additionally, a battery-management and energy-sharing scheme is introduced to enhance the photovoltaic energy utilization and reduce the carbon emissions. To further improve the system-wide efficiency, a collaborative, iterative mechanism is designed to dynamically coordinate the network’s operations with hybrid-energy management. The simulation results show that, compared to conventional energy-efficiency-optimization methods, the proposed methods and schemes improve the carbon efficiency by 51.3% annually, while reducing the system-wide carbon emissions by $mathrm {1.9~tCO_{2}e}$ (tonnes of carbon dioxide equivalent).
{"title":"Carbon-Efficiency Optimization in Cellular Networks With a Hybrid-Energy Supply","authors":"Yue Yu;Zheng Zhao;Yuxi Zhao;Yi Zhong;Iztok Humar;Xiaohu Ge","doi":"10.1109/TGCN.2025.3635041","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3635041","url":null,"abstract":"The integration of renewable energy into cellular networks has become a key strategy to reduce carbon emissions. However, the inherent variability of renewable sources and the fluctuating power demands of the network often lead to energy mismatches, thus affecting the carbon efficiency. To address this challenge, this paper proposes a resource-allocation and energy-management framework for cellular networks with a hybrid-energy supply, aiming to maximize the carbon efficiency. The proposed framework adopts an outer-layer BS-sleeping algorithm and an inner iterative optimization that updates user association, battery management, and energy sharing scheme. Specifically, a user-association strategy is developed based on Dinkelbach’s method and convex-concave optimization, ensuring efficient energy utilization, while maintaining the quality of service (QoS). Additionally, a battery-management and energy-sharing scheme is introduced to enhance the photovoltaic energy utilization and reduce the carbon emissions. To further improve the system-wide efficiency, a collaborative, iterative mechanism is designed to dynamically coordinate the network’s operations with hybrid-energy management. The simulation results show that, compared to conventional energy-efficiency-optimization methods, the proposed methods and schemes improve the carbon efficiency by 51.3% annually, while reducing the system-wide carbon emissions by <inline-formula> <tex-math>$mathrm {1.9~tCO_{2}e}$ </tex-math></inline-formula> (tonnes of carbon dioxide equivalent).","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1462-1477"},"PeriodicalIF":6.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802351","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 : 2025-11-17DOI: 10.1109/TGCN.2025.3633457
Haoxin Sun;Mattia Lecci;Javier Rivas;Carlos S. Álvarez-Merino;Hao Qiang Luo-Chen;Emil J. Khatib;Germán Corrales Madueño;Francisco J. Garcia;David Segura Ramos;Raquel Barco
The emergence of cloud-native architectures and Open RAN (O-RAN) principles has revolutionized the deployment and scalability of mobile network infrastructure. However, the energy efficiency of Virtualized Network Functions (VNFs) operating in such environments remains a critical concern, particularly as 5G and 6G networks scale in complexity and resource demands. This study introduces a modular and reproducible testbed for high-fidelity energy profiling of containerized monolithic srsRAN-based gNB implementations running on Commercial Off-The-Shelf (COTS) server-class hardware. The testbed integrates a commercial-grade Power Analyzer (PA) and a full-stack network emulation framework to measure the impact of key parameters, including CPU frequency and core allocation, on the power consumption of a virtualized gNB. A comprehensive configuration dataset is collected across both high-load and low-load scenarios. The results reveal that CPU frequency throttling consistently reduces energy consumption beyond specific performance thresholds, while core limitation is effective only in low-load scenarios; however, it enables VNF co-location, which contributes to reducing overall infrastructure-level energy consumption. These findings validate the applicability of dynamic energy optimization strategies and provide actionable insights for orchestration frameworks aiming to balance energy efficiency with Quality of Service (QoS) requirements.
{"title":"Energy-Efficient Virtualized gNBs for Cloud-Native O-RAN: A Testbed-Based Study of CPU Resource Management in 5G/6G Networks","authors":"Haoxin Sun;Mattia Lecci;Javier Rivas;Carlos S. Álvarez-Merino;Hao Qiang Luo-Chen;Emil J. Khatib;Germán Corrales Madueño;Francisco J. Garcia;David Segura Ramos;Raquel Barco","doi":"10.1109/TGCN.2025.3633457","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3633457","url":null,"abstract":"The emergence of cloud-native architectures and Open RAN (O-RAN) principles has revolutionized the deployment and scalability of mobile network infrastructure. However, the energy efficiency of Virtualized Network Functions (VNFs) operating in such environments remains a critical concern, particularly as 5G and 6G networks scale in complexity and resource demands. This study introduces a modular and reproducible testbed for high-fidelity energy profiling of containerized monolithic srsRAN-based gNB implementations running on Commercial Off-The-Shelf (COTS) server-class hardware. The testbed integrates a commercial-grade Power Analyzer (PA) and a full-stack network emulation framework to measure the impact of key parameters, including CPU frequency and core allocation, on the power consumption of a virtualized gNB. A comprehensive configuration dataset is collected across both high-load and low-load scenarios. The results reveal that CPU frequency throttling consistently reduces energy consumption beyond specific performance thresholds, while core limitation is effective only in low-load scenarios; however, it enables VNF co-location, which contributes to reducing overall infrastructure-level energy consumption. These findings validate the applicability of dynamic energy optimization strategies and provide actionable insights for orchestration frameworks aiming to balance energy efficiency with Quality of Service (QoS) requirements.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1446-1461"},"PeriodicalIF":6.7,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11250702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802356","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}
Pub Date : 2025-11-14DOI: 10.1109/TGCN.2025.3633182
Sambit Mishra;Soumya P. Dash;George C. Alexandropoulos
This paper investigates the performance of one- and two-sided amplitude shift keying (ASK) modulations in noncoherent single-input single-output (SISO) wireless communication systems assisted by a reconfigurable intelligent surface (RIS). Novel noncoherent receiver structures are proposed based on the energy and the sign of the received signal for the detection of the one- and two-sided ASK modulated data symbols, respectively. The system’s performance is assessed in terms of the symbol error rate (SER), and an optimization framework is proposed to determine the most effective one- and two-sided ASKs to minimize the SER while adhering to an average transmit power constraint. Two scenarios based on the availability of the statistical characteristics of the wireless channel are explored: a) the transceiver pair has complete knowledge of the channel statistics, and b) both end nodes possess knowledge of the statistics of the channel gain up to its fourth moment, and novel algorithms are developed to obtain SER-optimized ASKs for both of them. Extensive numerical evaluations are presented, showcasing that a threshold signal-to-noise ratio (SNR) exists above which the SER-optimized ASKs outperform the traditional equispaced ASKs. The dependencies of the SER performance and the SNR threshold on various system parameters are assessed, providing design guidelines for hardware- and energy-efficient RIS-assisted noncoherent wireless communication systems with multi-level ASK modulations.
{"title":"SER-Optimized Multi-Level ASK Modulations for RIS-Assisted Communications With Energy- and Sign-Based Noncoherent Reception","authors":"Sambit Mishra;Soumya P. Dash;George C. Alexandropoulos","doi":"10.1109/TGCN.2025.3633182","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3633182","url":null,"abstract":"This paper investigates the performance of one- and two-sided amplitude shift keying (ASK) modulations in noncoherent single-input single-output (SISO) wireless communication systems assisted by a reconfigurable intelligent surface (RIS). Novel noncoherent receiver structures are proposed based on the energy and the sign of the received signal for the detection of the one- and two-sided ASK modulated data symbols, respectively. The system’s performance is assessed in terms of the symbol error rate (SER), and an optimization framework is proposed to determine the most effective one- and two-sided ASKs to minimize the SER while adhering to an average transmit power constraint. Two scenarios based on the availability of the statistical characteristics of the wireless channel are explored: a) the transceiver pair has complete knowledge of the channel statistics, and b) both end nodes possess knowledge of the statistics of the channel gain up to its fourth moment, and novel algorithms are developed to obtain SER-optimized ASKs for both of them. Extensive numerical evaluations are presented, showcasing that a threshold signal-to-noise ratio (SNR) exists above which the SER-optimized ASKs outperform the traditional equispaced ASKs. The dependencies of the SER performance and the SNR threshold on various system parameters are assessed, providing design guidelines for hardware- and energy-efficient RIS-assisted noncoherent wireless communication systems with multi-level ASK modulations.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1433-1445"},"PeriodicalIF":6.7,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802341","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 : 2025-11-13DOI: 10.1109/TGCN.2025.3632392
Debabrata Roy;Avirup Das;Dibakar Saha
This paper introduces a novel network selection approach for heterogeneous networks (HetNets) that aims to minimize the energy consumption of user equipment (UE) while ensuring the Quality of Service (QoS) for various applications, including voice communication, video streaming, and data transmission. Key QoS parameters, such as throughput, latency, and reliability, are dynamically considered to ensure optimal network performance. We propose a network selection mechanism based on the Fuzzy Analytical Hierarchy Process (FAHP), which utilizes a Dynamic Pairwise Comparison Matrix (D-PCM) to intelligently assign weights to network criteria, facilitating the selection of the most energy-efficient network in a HetNet. FAHP offers greater flexibility through fuzzy evaluation, enabling more accurate and adaptive decision-making compared to the traditional Analytical Hierarchy Process (AHP), which relies on precise values in the comparison matrix. By incorporating the inherent uncertainty of network conditions and fluctuations in user QoS demands, our FAHP method accounts for user satisfaction levels across different QoS parameters when selecting the most energy-efficient network. To validate our FAHP-based proposed mechanism, we develop a criteria-based network simulator that evaluates the available QoS across all networks in HetNets. We evaluate our proposed mechanism against two AHP-based algorithms: one designed to optimize QoS and the other focused on enhancing energy efficiency, as well as the conventional Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIekriterijumsko KOmpromisno Rangiranje (VIKOR) algorithms. The experimental results demonstrate the effectiveness of our approach, achieving an 11% reduction in energy consumption for video communication, a 33%reduction for image transmission using a sigmoid function-based satisfaction scale, and a 54% reduction for audio transmission with an exponential function-based satisfaction scale.
{"title":"Fuzzy-AHP-Based Network Selection in HetNet: An Energy-Efficient and QoS-Aware Approach","authors":"Debabrata Roy;Avirup Das;Dibakar Saha","doi":"10.1109/TGCN.2025.3632392","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3632392","url":null,"abstract":"This paper introduces a novel network selection approach for heterogeneous networks (HetNets) that aims to minimize the energy consumption of user equipment (UE) while ensuring the Quality of Service (QoS) for various applications, including voice communication, video streaming, and data transmission. Key QoS parameters, such as throughput, latency, and reliability, are dynamically considered to ensure optimal network performance. We propose a network selection mechanism based on the Fuzzy Analytical Hierarchy Process (<italic>FAHP</i>), which utilizes a <italic>Dynamic Pairwise Comparison Matrix (D-PCM)</i> to intelligently assign weights to network criteria, facilitating the selection of the most energy-efficient network in a HetNet. <italic>FAHP</i> offers greater flexibility through fuzzy evaluation, enabling more accurate and adaptive decision-making compared to the traditional Analytical Hierarchy Process (<italic>AHP</i>), which relies on precise values in the comparison matrix. By incorporating the inherent uncertainty of network conditions and fluctuations in user QoS demands, our <italic>FAHP</i> method accounts for user satisfaction levels across different QoS parameters when selecting the most energy-efficient network. To validate our <italic>FAHP</i>-based proposed mechanism, we develop a criteria-based network simulator that evaluates the available QoS across all networks in HetNets. We evaluate our proposed mechanism against two <italic>AHP</i>-based algorithms: one designed to optimize QoS and the other focused on enhancing energy efficiency, as well as the conventional <italic>Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)</i> and <italic>VIekriterijumsko KOmpromisno Rangiranje (VIKOR)</i> algorithms. The experimental results demonstrate the effectiveness of our approach, achieving an 11% reduction in energy consumption for video communication, a 33%reduction for image transmission using a sigmoid function-based satisfaction scale, and a 54% reduction for audio transmission with an exponential function-based satisfaction scale.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1404-1418"},"PeriodicalIF":6.7,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802358","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 : 2025-11-12DOI: 10.1109/TGCN.2025.3632067
Zhenlong Man;Shuping Li;Fan Zhang
In the Industrial Internet of Things (IIoT), numerous devices are connected to the Internet, forming a huge data interaction network that can monitor industrial production processes in real time and achieve precise energy control. However, data transmission faces challenges such as privacy and security threats and communication overhead. For this reason, this paper puts forward an image security protection framework built on the Green Industrial Internet of Things (GIIoT) communication. In the image data encryption stage, the non-determinism of a chaotic system is utilized to design a dynamic spiral scrambling method to rearrange pixels. The orthogonal matrix is permuted according to the singular value decomposition theory, and the chaotic sequence generation mechanism is introduced for the purpose of performing random bit XOR operations. Subsequently, through the collaborative embedding method of adaptive histogram shift and random bit mapping transformation, the embedding strategy is dynamically adjusted based on the statistical characteristics of the image, and the cipher-image is covertly embedded in the carrier image to ensure secure transmission. Security analysis and performance assessments demonstrate that the proposed scheme achieves strong security, high efficiency, and low communication overhead, meeting the needs for secure and sustainable green communication in the IIoT.
{"title":"Image Privacy Protection for Green Industrial IoT: Application of Dynamic Collaborative Encryption and Adaptive Embedding Algorithm","authors":"Zhenlong Man;Shuping Li;Fan Zhang","doi":"10.1109/TGCN.2025.3632067","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3632067","url":null,"abstract":"In the Industrial Internet of Things (IIoT), numerous devices are connected to the Internet, forming a huge data interaction network that can monitor industrial production processes in real time and achieve precise energy control. However, data transmission faces challenges such as privacy and security threats and communication overhead. For this reason, this paper puts forward an image security protection framework built on the Green Industrial Internet of Things (GIIoT) communication. In the image data encryption stage, the non-determinism of a chaotic system is utilized to design a dynamic spiral scrambling method to rearrange pixels. The orthogonal matrix is permuted according to the singular value decomposition theory, and the chaotic sequence generation mechanism is introduced for the purpose of performing random bit XOR operations. Subsequently, through the collaborative embedding method of adaptive histogram shift and random bit mapping transformation, the embedding strategy is dynamically adjusted based on the statistical characteristics of the image, and the cipher-image is covertly embedded in the carrier image to ensure secure transmission. Security analysis and performance assessments demonstrate that the proposed scheme achieves strong security, high efficiency, and low communication overhead, meeting the needs for secure and sustainable green communication in the IIoT.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1391-1403"},"PeriodicalIF":6.7,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802336","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 : 2025-11-12DOI: 10.1109/TGCN.2025.3631854
Jianfeng Shi;Yujie Kang;Yue Li;Baolong Li
Integrated Satellite-Terrestrial Networks (ISTN) are considered a promising solution for next-generation wireless communications due to their wide coverage and resource complementarity. However, ISTNs still face critical challenges, especially when integrating passive RIS into ISTNs. In this case, passive RIS may result in significant signal attenuation and limited flexibility in multi-user access. To address these issues, this paper proposes the joint use application of active Reconfigurable Intelligent Surfaces (RIS) and Rate-Splitting Multiple Access (RSMA) in ISTN. Active RIS can not only reconfigure signal reflections but also amplify signal strength, thereby mitigating the double fading issue. RSMA enhances spectrum and energy efficiency (EE) through flexible interference management. By combining both technologies, a novel energy-efficient transmission framework for ISTN is constructed. Within the framework, the EE maximization problem is investigated to further enhance the EE performance of ISTN by combining the advantages of both technologies, considering the total power constraints of the base station (BS), low earth orbit (LEO) satellite, and active RIS, as well as the quality of service (QoS) constraints for all users. To address the joint optimization of BS and LEO satellite beamforming, common rate allocation, and active RIS precoding matrix, the Dinkelbach method is first applied to handle the fractional objective function. Then, an EE maximization algorithm based on alternating optimization (AO), successive convex approximation (SCA), semi-definite relaxation (SDR), fractional programming (FP), and quadratic constrained quadratic programming (QCQP) is proposed. The simulation results show that active RIS effectively mitigates the “double fading” effect, with a 24.75% improvement in EE compared to passive RIS. Furthermore, the proposed algorithm significantly outperforms the space division multiple access (SDMA) scheme in terms of EE, achieving a 10.59% improvement. The above results demonstrate the critical role of active RIS and RSMA in building sustainable 6G networks with minimized ecological footprint.
{"title":"Energy Efficient Design for Active RIS-Assisted Integrated Satellite-Terrestrial Networks With RSMA","authors":"Jianfeng Shi;Yujie Kang;Yue Li;Baolong Li","doi":"10.1109/TGCN.2025.3631854","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3631854","url":null,"abstract":"Integrated Satellite-Terrestrial Networks (ISTN) are considered a promising solution for next-generation wireless communications due to their wide coverage and resource complementarity. However, ISTNs still face critical challenges, especially when integrating passive RIS into ISTNs. In this case, passive RIS may result in significant signal attenuation and limited flexibility in multi-user access. To address these issues, this paper proposes the joint use application of active Reconfigurable Intelligent Surfaces (RIS) and Rate-Splitting Multiple Access (RSMA) in ISTN. Active RIS can not only reconfigure signal reflections but also amplify signal strength, thereby mitigating the double fading issue. RSMA enhances spectrum and energy efficiency (EE) through flexible interference management. By combining both technologies, a novel energy-efficient transmission framework for ISTN is constructed. Within the framework, the EE maximization problem is investigated to further enhance the EE performance of ISTN by combining the advantages of both technologies, considering the total power constraints of the base station (BS), low earth orbit (LEO) satellite, and active RIS, as well as the quality of service (QoS) constraints for all users. To address the joint optimization of BS and LEO satellite beamforming, common rate allocation, and active RIS precoding matrix, the Dinkelbach method is first applied to handle the fractional objective function. Then, an EE maximization algorithm based on alternating optimization (AO), successive convex approximation (SCA), semi-definite relaxation (SDR), fractional programming (FP), and quadratic constrained quadratic programming (QCQP) is proposed. The simulation results show that active RIS effectively mitigates the “double fading” effect, with a 24.75% improvement in EE compared to passive RIS. Furthermore, the proposed algorithm significantly outperforms the space division multiple access (SDMA) scheme in terms of EE, achieving a 10.59% improvement. The above results demonstrate the critical role of active RIS and RSMA in building sustainable 6G networks with minimized ecological footprint.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1378-1390"},"PeriodicalIF":6.7,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802346","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}
Processing graph-structured data in Green Internet of Things (IoT) applications demands a dual focus on analytical accuracy and energy efficiency. While Quantum Graph Neural Networks (QGNNs) present a promising computational paradigm, the parameterized quantum circuits (PQC) they rely on often require excessive depth and lack robustness, hindering their use in resource-constrained environments. The core problem addressed in this paper is how to design a quantum-inspired GCN that balances expressive power with shallow, energy-efficient circuit design, making it viable for sustainable Green IoT deployment. Our main contributions are threefold. First, we design the Intelligent Parameterized Quantum Circuit (IPQC) as a compact, 15-parameter bidirectional-control quantum convolutional block that enhances expressive power while maintaining parameter efficiency and noise tolerance. Second, building on this block, we develop the Quantum Graph Convolutional Network with Residual Injection (QGCN-RI), a residual-injection-driven network that integrates two-stage normalization and amplitude encoding to significantly improve optimization stability. Third, we conduct comprehensive experiments on citation network benchmarks. Results demonstrate that QGCN-RI achieves performance competitive with strong classical baselines like GAT, reaching 83.3% accuracy on Cora. More critically, our quantitative analysis indicates that the model’s shallow circuit design allows it to achieve this competitive accuracy with a lower estimated energy consumption. By showing that a compact QGCN can approximate the performance of its classical counterparts with reduced resource costs, our work validates the feasibility of quantum graph learning for developing sustainable and resource-aware Green IoT solutions.
{"title":"IPQC: An Intelligent Quantum Graph Convolutional Network for Topological Data Processing on Green IoT","authors":"Naixue Xiong;Silong Li;Linshu Chen;Wei Liang;Yuxiang Chen","doi":"10.1109/TGCN.2025.3626366","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3626366","url":null,"abstract":"Processing graph-structured data in Green Internet of Things (IoT) applications demands a dual focus on analytical accuracy and energy efficiency. While Quantum Graph Neural Networks (QGNNs) present a promising computational paradigm, the parameterized quantum circuits (PQC) they rely on often require excessive depth and lack robustness, hindering their use in resource-constrained environments. The core problem addressed in this paper is how to design a quantum-inspired GCN that balances expressive power with shallow, energy-efficient circuit design, making it viable for sustainable Green IoT deployment. Our main contributions are threefold. First, we design the Intelligent Parameterized Quantum Circuit (IPQC) as a compact, 15-parameter bidirectional-control quantum convolutional block that enhances expressive power while maintaining parameter efficiency and noise tolerance. Second, building on this block, we develop the Quantum Graph Convolutional Network with Residual Injection (QGCN-RI), a residual-injection-driven network that integrates two-stage normalization and amplitude encoding to significantly improve optimization stability. Third, we conduct comprehensive experiments on citation network benchmarks. Results demonstrate that QGCN-RI achieves performance competitive with strong classical baselines like GAT, reaching 83.3% accuracy on Cora. More critically, our quantitative analysis indicates that the model’s shallow circuit design allows it to achieve this competitive accuracy with a lower estimated energy consumption. By showing that a compact QGCN can approximate the performance of its classical counterparts with reduced resource costs, our work validates the feasibility of quantum graph learning for developing sustainable and resource-aware Green IoT solutions.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1352-1363"},"PeriodicalIF":6.7,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802337","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}
Collaborative multiple uncrewed aerial vehicles (UAVs) demonstrate significant potential for real-time video analytics applications. Current multi-UAV systems face challenges such as inference latency and endurance. These problems primarily stem from limited computational resource and energy constraints of UAVs. The scale of UAV deployment is a crucial factor, as it imposes varying degrees of limitations on both inference latency and UAV endurance. This paper proposes a scalable cooperative UAV architecture for video analytics, which is optimized for different UAV scales and suitable for both centralized and distributed control modes. To minimize inference latency and enhance energy efficiency, we develop mathematical models and optimization algorithms for UAV collaboration-based video analytics, addressing both centralized and distributed scenarios. The centralized method uses a two-layer optimization algorithm to jointly optimize UAV deployment and task scheduling (JDTSO), while the distributed method integrates multi-agent proximal policy optimization (MAPPO) with a directed acyclic graph (DAG) partition strategy (MAPDP). Extensive analysis and numerical results demonstrate the superior performance of the proposed architecture.
{"title":"Toward Inference Latency Optimization for Scalable Collaborative Multi-UAV Analytics","authors":"Ying Wang;Jingling Yuan;Wenbo Wu;Quanfeng Yao;Donglei Xu;Zhishu Shen","doi":"10.1109/TGCN.2025.3625726","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3625726","url":null,"abstract":"Collaborative multiple uncrewed aerial vehicles (UAVs) demonstrate significant potential for real-time video analytics applications. Current multi-UAV systems face challenges such as inference latency and endurance. These problems primarily stem from limited computational resource and energy constraints of UAVs. The scale of UAV deployment is a crucial factor, as it imposes varying degrees of limitations on both inference latency and UAV endurance. This paper proposes a scalable cooperative UAV architecture for video analytics, which is optimized for different UAV scales and suitable for both centralized and distributed control modes. To minimize inference latency and enhance energy efficiency, we develop mathematical models and optimization algorithms for UAV collaboration-based video analytics, addressing both centralized and distributed scenarios. The centralized method uses a two-layer optimization algorithm to jointly optimize UAV deployment and task scheduling (JDTSO), while the distributed method integrates multi-agent proximal policy optimization (MAPPO) with a directed acyclic graph (DAG) partition strategy (MAPDP). Extensive analysis and numerical results demonstrate the superior performance of the proposed architecture.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1364-1377"},"PeriodicalIF":6.7,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802323","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 : 2025-08-20DOI: 10.1109/TGCN.2025.3598657
{"title":"IEEE Transactions on Green Communications and Networking","authors":"","doi":"10.1109/TGCN.2025.3598657","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3598657","url":null,"abstract":"","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"C2-C2"},"PeriodicalIF":6.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11131456","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887754","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}
Pub Date : 2025-08-20DOI: 10.1109/TGCN.2025.3598659
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/TGCN.2025.3598659","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3598659","url":null,"abstract":"","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"C3-C3"},"PeriodicalIF":6.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11131449","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887853","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}
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