IEEE Transactions on Sustainable Computing最新文献_第4页

Multi-Stage Damping Control Scheme for VSG-Enabled Inverter-Based Resources to Stabilize Low-Inertia Power Grids 基于vsg的逆变器资源多级阻尼控制方案稳定低惯量电网

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1109/TSUSC.2026.3665981

Rohit Kumar;Soumya R. Mohanty;M. K. Verma

{"title":"Multi-Stage Damping Control Scheme for VSG-Enabled Inverter-Based Resources to Stabilize Low-Inertia Power Grids","authors":"Rohit Kumar;Soumya R. Mohanty;M. K. Verma","doi":"10.1109/TSUSC.2026.3665981","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3665981","url":null,"abstract":"This paper presents the impact of Inverter-Based Resources (IBRs) controlled through the Grid-Forming (GFM) scheme on Low-Frequency Oscillations (LFOs) and power system dynamic behavior. These IBRs are integrated at low-inertia buses, where their effect on stability is critical. The study employs the Virtual Synchronous Generator (VSG) control-based GFM scheme for IBRs. The dynamic interaction between the power system and the active and reactive power control loops of VSG-controlled IBRs has a significant impact on the system’s LFOs. This impact becomes more pronounced as the VSG control loop parameters are increased, particularly the virtual inertia constant <inline-formula><tex-math>$(H)$</tex-math></inline-formula>, the virtual damping coefficient <inline-formula><tex-math>$(D_{p})$</tex-math></inline-formula>, and the virtual voltage gain coefficient <inline-formula><tex-math>$(K_{q})$</tex-math></inline-formula>. These dynamic interactions can introduce new, weakly damped LFOs, negatively impacting the system’s dynamic behavior. To address this challenge, a Supplementary Damping Control (SDC) scheme is proposed for IBRs. This scheme aims to improve LFO damping and mitigate power oscillations of IBRs. The SDC comprises a multi-stage mixed <inline-formula><tex-math>$H_{2}/H_infty$</tex-math></inline-formula> decentralized damping controller integrated with the IBR’s reactive control loop. The parameter variation uncertainty and explicit modelling of disturbance input have been considered in the design process of this SDC scheme. Further, the robustness of the proposed SDC is validated on the IEEE 39-bus system. The system is tested under various operating conditions, such as load increments, changes in network topology, and integration of renewable sources. Eigenvalue analysis is conducted using MATLAB, while dynamic simulations are performed using the Real-Time Digital Simulator (RTDS). Simulation results confirm that the proposed SDC effectively mitigates system LFOs dynamic behavior.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"158-172"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A Collaborative Caching and Offloading Approach for Vehicular Edge Computing 一种用于车辆边缘计算的协同缓存和卸载方法

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-23 DOI: 10.1109/TSUSC.2026.3666125

Zijia Zhao;Liang Zhao;Nan Wu;Lexi Xu;Na Lin;Zhiyuan Tan

{"title":"A Collaborative Caching and Offloading Approach for Vehicular Edge Computing","authors":"Zijia Zhao;Liang Zhao;Nan Wu;Lexi Xu;Na Lin;Zhiyuan Tan","doi":"10.1109/TSUSC.2026.3666125","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3666125","url":null,"abstract":"Vehicular Edge Computing (VEC) leverages promising technologies, namely the vehicle-to-vehicle (V2V) computation offloading approach and edge service caching, to address latency-sensitive tasks. The V2V offloading method efficiently harnesses idle resources from neighboring vehicles. Edge service caching facilitates the offloading task through pre-caching pertinent service data. However, formulating an efficient caching mechanism to support V2V offloading poses significant challenges, given the dynamic vehicle environment, varying computational resources, and limited caching resources of Roadside Units (RSUs). This paper introduces a collaborative caching and offloading (CACO) scheme. First, to mitigate resource wastage caused by inter-vehicle communication interruptions, we employ Generative Adversarial Network (GAN) for trajectory prediction. This process generates a relationship matrix, predicting the stability of inter-vehicle link connections to assist in V2V offloading decisions. Second, to circumvent redundant uploads and computations for recurring offloading tasks, we analyze the popularity of historical offloading tasks using the Page-Hinkley test (PHT) technique, caching frequently offloaded tasks to reduce the processing latency of offloading tasks. Subsequently, a matching scheme for caching and offloading contents is devised. Finally, the Deep Reinforcement Learning (DRL) algorithm is employed to train the offloading strategy. Results from extensive experiments substantiate that CACO attains superior performance in both system computational latency and offloading success rate.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"76-82"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

EnFed: An Energy-Aware Federated Learning in Resource Constrained Environments for Human Activity Recognition EnFed：资源受限环境下的能源感知联合学习，用于人类活动识别

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-04-28 DOI: 10.1109/TSUSC.2026.3688453

Anwesha Mukherjee;Rajkumar Buyya

{"title":"EnFed: An Energy-Aware Federated Learning in Resource Constrained Environments for Human Activity Recognition","authors":"Anwesha Mukherjee;Rajkumar Buyya","doi":"10.1109/TSUSC.2026.3688453","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3688453","url":null,"abstract":"The human activity recognition (HAR) and recommendation applications for mobile users require a privacy-aware and accurate data analysis model with lower time and lower energy consumption. The use of federated learning (FL) to develop a privacy-aware HAR model is an emerging research topic. However, the participating mobile devices in the FL process may slow down due to their limited computational resources, connectivity interruption, and limited battery life. To address these challenges, this paper proposes an energy-efficient FL method referred to as EnFed, with a case study on HAR. In EnFed, a mobile device that needs a model for an application requests its nearby devices with respect to an incentive. The nearby devices, which agree to the offered incentive, send their local model updates, i.e., updated local model parameters for that application, to the requesting device. The device, after receiving local model updates from the contributors, aggregates them to build a global model and then fits the model to its own dataset to build its own local model. The results show that using EnFed a resource-limited device obtains an accurate prediction model at lower time and lower energy consumption. The experimental results show that EnFed achieves above 95% prediction accuracy and outperforms the baselines. EnFed also reduces the response time above 90% than the cloud-only framework. The comparative study shows that EnFed outperforms the existing HAR approaches.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 3","pages":"278-290"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A Framework for Sustainable Management of Autonomous Ground Robot Fleets 自主地面机器人车队可持续管理框架

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1109/TSUSC.2026.3666122

Syeda Tanjila Atik;Daniel Grosu;Marco Brocanelli

{"title":"A Framework for Sustainable Management of Autonomous Ground Robot Fleets","authors":"Syeda Tanjila Atik;Daniel Grosu;Marco Brocanelli","doi":"10.1109/TSUSC.2026.3666122","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3666122","url":null,"abstract":"Ensuring low battery degradation in Autonomous Ground Robot (AGR) fleets operating in online environments (e.g., delivery services) is essential for enhancing their long-term sustainability. However, most existing studies either rely on offline methods—unsuitable for scenarios requiring real-time decisions—or focus solely on maximizing task allocation, resource utilization, or revenue, with limited consideration for battery health. Additionally, maximizing fleet sustainability requires bounded relative revenue losses from unassigned tasks within a user-defined acceptable limit to make it an attractive option for industry. To address these limitations, we propose an online task and charge allocation framework that jointly optimizes revenue generation and battery lifespan, while allowing users to explicitly constrain relative revenue losses. The framework includes three event-driven algorithms: <inline-formula><tex-math>$mathsf{BTC-M}$</tex-math></inline-formula>, which computes optimal decisions at each event, and two computationally efficient greedy variants, <inline-formula><tex-math>$mathsf{BTC-G}$</tex-math></inline-formula> and <inline-formula><tex-math>$mathsf{BTC-WG}$</tex-math></inline-formula>, which provide sub-optimal solutions with reduced overhead. We evaluate the performance of our approach under different task arrival distributions representative of real-world applications. Simulation results based on a real AGR, compared against multiple baselines, demonstrate that our framework can extend battery lifespan by up to 19% with minimal revenue loss.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"83-97"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Information Density as a Quantitative Measure for AI-Enabled Virtual Sensing: Feasibility and Limits 信息密度作为人工智能虚拟感知的定量度量：可行性与局限性

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-04-29 DOI: 10.1109/TSUSC.2026.3688833

Hrishikesh Dutta;Roberto Minerva;Reza Farahbakhsh;Noel Crespi

{"title":"Information Density as a Quantitative Measure for AI-Enabled Virtual Sensing: Feasibility and Limits","authors":"Hrishikesh Dutta;Roberto Minerva;Reza Farahbakhsh;Noel Crespi","doi":"10.1109/TSUSC.2026.3688833","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3688833","url":null,"abstract":"Modern IoT and sensor networks generate vast amounts of data, posing significant challenges for storage, transmission, and real-time processing. Traditional approaches, such as compressive sensing and machine learning-based compression, often suffer from computational inefficiencies and irreversible data loss. This paper introduces Information Density as a quantitative metric to support sensor deployment and enable AI-driven virtual sensing. We propose a framework that leverages spatial, temporal and inter-modal correlations among sensor signals to perform sensing tasks even in the absence of physical sensors. Two complementary measures: (i) Phase in Eigen Space and (ii) Mutual Information, are developed to quantify and assess information density, enabling the selection of optimal sensor configurations across both intra-modality and cross-modality scenarios. Validated using real-world data from Madrid’s smart city infrastructure, this framework demonstrates the feasibility of replacing physical sensors with virtual ones under bounded error conditions (e.g., achieving <inline-formula><tex-math>$< 3.21%$</tex-math></inline-formula> mean error with a single sensor). The results highlight the potential for scalable and energy-efficient sensing systems in smart environments.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 3","pages":"316-329"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Nash Bargaining and Coalition-Based Incentives for Federated Learning in Internet of Vehicles 基于纳什议价和联盟激励的车联网联合学习

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-03-10 DOI: 10.1109/TSUSC.2026.3672703

Qiufen Ni;Lu Jin;Chenhao Wang;Jianxiong Guo;Weili Wu

{"title":"Nash Bargaining and Coalition-Based Incentives for Federated Learning in Internet of Vehicles","authors":"Qiufen Ni;Lu Jin;Chenhao Wang;Jianxiong Guo;Weili Wu","doi":"10.1109/TSUSC.2026.3672703","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3672703","url":null,"abstract":"The dynamic topology, resource constraints, and data heterogeneity inherent in the Internet of Vehicles (IoV) present fundamental challenges to the effective deployment of federated learning (FL) applications. Traditional FL strategies often suffer from suboptimal model performance and excessive communication overhead under such conditions. Existing solutions primarily focus on static resource allocation and overlook the compounded effects of high mobility, diverse device capabilities, and time-varying data quality. Moreover, current incentive mechanisms lack adaptability to dynamic resource-benefit trade-offs, making it challenging to sustain efficient participation from vehicle clients. To address these issues, we propose NBCI-FL, an incentive framework that redefines vehicular collaboration as a multi-objective coalition game. It introduces a mobility-aware coalition-formation mechanism based on heterogeneous coalition games, along with a three-dimensional evaluation-based client-selection mechanism to construct efficient and stable learning clusters. Furthermore, we propose a two-stage Nash bargaining game that dynamically balances the fairness of data contributions with the optimization of resource utilization efficiency. Theoretical analysis proves the existence and effectiveness of the Nash bargaining equilibrium under vehicular dynamic conditions. Extensive simulations on real-world datasets demonstrate that our proposed NBCI-FL substantially outperforms state-of-the-art traditional FL baselines by maintaining high global model accuracy, improving communication efficiency, enhancing network stability, and ensuring fair and rational utility allocation among all participants.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 3","pages":"213-226"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An Adaptive Gradient Recurrent Neural Network for Dynamic Quadratic Programming and its FPGA Implementation 动态二次规划的自适应梯度递归神经网络及其FPGA实现

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-04-30 DOI: 10.1109/TSUSC.2026.3689162

Huang Ouyang;Kaiyuan Yang;Tiantai Deng;Long Jin

{"title":"An Adaptive Gradient Recurrent Neural Network for Dynamic Quadratic Programming and its FPGA Implementation","authors":"Huang Ouyang;Kaiyuan Yang;Tiantai Deng;Long Jin","doi":"10.1109/TSUSC.2026.3689162","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3689162","url":null,"abstract":"Dynamic quadratic programming (DQP) widely exists in many optimization applications. Existing recurrent neural networks (RNNs) exhibit significant limitations in solving DQP, including lagging errors in gradient-based neural networks (GNNs) and fixed scaling factors in zeroing neural networks (ZNNs). Meanwhile, the matrix singularity may cause solution crashes in ZNNs. Additionally, existing RNNs for DQP are primarily implemented on von Neumann architecture-based computers, and this architecture exhibits low efficiency in handling repetitive computational tasks. To address these issues, this paper constructs an adaptive gradient recurrent neural network (AGRNN) to solve DQP. Furthermore, compared to the von Neumann architecture, field programmable gate arrays (FPGAs) have significant advantages in handling repetitive computational tasks, and thus, the AGRNN is implemented on an FPGA. The AGRNN significantly reduces GNNs’ lagging errors, has a higher flexibility than ZNNs in adjusting scaling factors, and effectively mitigates matrix singularity issues. Additionally, the FPGA implementation further improves the AGRNN’s efficiency in solving DQP. Numerical experiments show that the FPGA implementation achieves at least 2.68 times the computational speed of the von Neumann architecture. Meanwhile, experiments are conducted in the dynamic economic dispatch involving two generators, where the FPGA implementation of the AGRNN utilizes 5521 LUTs, 8311 FFs, and 339 DSPs.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 3","pages":"330-341"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A Reinforcement Learning Framework for UAV Path Planning Automation in Complex Fire and Rescue Environments 复杂消防救援环境下无人机路径规划自动化的强化学习框架

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-03-27 DOI: 10.1109/TSUSC.2026.3678502

Yanming Chen;Saeid Pourroostaei Ardakani

引用次数: 0

Energy-Aware DRL-Based Dual-Perception Fountain Codes for Resource-Constrained UASNs 基于能量感知drl的资源受限uasn双感知喷泉码

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-18 DOI: 10.1109/TSUSC.2026.3666121

Rongxin Zhu;Wenjie Li;Azzedine Boukerche;Qiuling Yang

{"title":"Energy-Aware DRL-Based Dual-Perception Fountain Codes for Resource-Constrained UASNs","authors":"Rongxin Zhu;Wenjie Li;Azzedine Boukerche;Qiuling Yang","doi":"10.1109/TSUSC.2026.3666121","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3666121","url":null,"abstract":"Fountain codes with online adaptation (OFCs) are promising for underwater acoustic sensor networks (UASNs), since they exploit limited feedback to reduce transmission overhead. Yet, the performance of conventional OFCs is severely degraded in UASNs due to high error rates, long propagation delays, and sparse feedback, resulting in poor recovery efficiency and high energy cost. To address these challenges, we propose an energy-aware dual-perception OFC framework driven by deep reinforcement learning (DRL-OFC). In this design, a DRL agent jointly perceives channel dynamics and feedback sparsity to learn an optimal degree distribution that suppresses redundant coding and enhances intermediate decoding. In addition, a feedback-aware transmission strategy is developed to cope with the long-delay characteristics of UASNs, further reducing unnecessary retransmissions. Simulation results show that DRL-OFC achieves significant gains over existing OFC schemes in terms of decoding efficiency, transmission overhead, recovery performance, and energy consumption, confirming its suitability for resource-constrained underwater communications.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"111-122"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

High Accuracy Power Aware Chebyshev-Based Hardware Implementation of Tanh Function for RNN Controllers 基于切比舍夫的RNN控制器Tanh函数的高精度功率感知硬件实现

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-03-24 DOI: 10.1109/TSUSC.2026.3677313

Chanakya Hingu;Xingang Fu;Praneeth Vangala;Shuhui Li

{"title":"High Accuracy Power Aware Chebyshev-Based Hardware Implementation of Tanh Function for RNN Controllers","authors":"Chanakya Hingu;Xingang Fu;Praneeth Vangala;Shuhui Li","doi":"10.1109/TSUSC.2026.3677313","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3677313","url":null,"abstract":"This paper presents a distinctive hardware-efficient, high-accuracy, and power-optimized implementation of the hyperbolic tangent activation function based on Chebyshev polynomial approximation, optimized for deployment in RNN controllers for DC–AC inverters. The proposed method overcomes the limitations of conventional approaches by leveraging fixed-point arithmetic and Clenshaw’s recurrence for numerically stable evaluation. Two Verilog-based architectures are developed and synthesized on an Intel FPGA: a compact FSM-based design that evaluates a single high-degree polynomial, and a low-latency interval-based implementation that utilizes multiple lower-degree polynomials. Both designs are validated against MATLAB references and demonstrate high accuracy. Resource utilization and power analysis reveal that the FSM-based implementation consumes significantly fewer resources compared to existing methods. Experimental results confirm that the proposed Chebyshev designs achieve bit-level accuracy, a reduced hardware, and low power consumption, making them ideal for artificial intelligence-enhanced power electronics applications requiring real-time performance in resource-constrained environments.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 3","pages":"342-349"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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