IEEE Transactions on Sustainable Computing最新文献

Importance Aware Undervolting for Robust Neural Network Training 感知欠电压对鲁棒神经网络训练的重要性

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-01-05 DOI: 10.1109/TSUSC.2025.3650602

Chen Zhang;Lening Wang;Xin Fu

{"title":"Importance Aware Undervolting for Robust Neural Network Training","authors":"Chen Zhang;Lening Wang;Xin Fu","doi":"10.1109/TSUSC.2025.3650602","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3650602","url":null,"abstract":"Convolutional Neural Network (CNN) is a powerful tool that has been extensively applied to many different applications. However, recent developments at CNN have revealed its vulnerability against adversarial example attacks. By introducing visually undetectable noise to the input image, an adversarial example attack can cause the CNN classifier to make false predictions. Multiple approaches have been proposed to defend against adversarial samples, one of which focuses on injecting noise into CNN during training. However, the existing method cannot generate noise efficiently and introduces extra time and energy overhead. In this paper, we propose an Importance-Aware undervolting training framework to improve CNN robustness. The undervolting technique is employed during training for noise generation at negligible overhead. Meanwhile, we observe that the neuron importance and bit importance in hardware can be leveraged during undervolting CNN training for controllable and flexible noise injection, which improves robustness. We design a position-aware bit mapping method in the memory unit by allocating data bits based on significance. And an importance-aware processing element (PE) mapping is also proposed at the computation unit for noise restriction. Our approach regularizes the noise injected into CNN and serves as an efficient method to defend against adversarial example attacks with significant energy savings. The proposed framework is evaluated through both FPGA implementation and software simulation. The experiment results show that our importance-aware undervolting CNN training achieves 47.8% adversarial accuracy at PGD-10 attack and 47.0% training energy savings.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"147-157"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606299","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

Energy-Aware VM Consolidation Using Similarity-Driven Intelligence in Green Cloud Environments 绿色云环境中使用相似驱动智能的能源感知VM整合

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1109/TSUSC.2026.3660014

Nirmal Kr Biswas;Debashis Das;Sourav Banerjee;Utpal Biswas

{"title":"Energy-Aware VM Consolidation Using Similarity-Driven Intelligence in Green Cloud Environments","authors":"Nirmal Kr Biswas;Debashis Das;Sourav Banerjee;Utpal Biswas","doi":"10.1109/TSUSC.2026.3660014","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3660014","url":null,"abstract":"Cloud computing is currently playing an essential role in supporting emerging sectors such as smart energy, intelligent transportation, and large-scale distributed systems. The scalability and adaptability features of cloud computing are efficiently enable resource utilization and continuous data exchange in dynamic and heterogeneous environments. However, the increasing demand for cloud services has raised the energy consumption of cloud data centers, which has a critical environmental impact. Therefore, sustainable resource management tactics have become crucial in today’s world. Dynamic Virtual Machine (VM) consolidation is one of the major tactics for sustainable resource management in the green cloud computing environment. Herein, a novel dynamic Energy-Aware Cosine Similarity Learning Network (ECSLN) is proposed to predict the overutilized host. Further, an Impact Factor-Based VM Selection (IFBVMS) method is proposed to select VMs for migration, and an ECSLN-Packed Placement method for VM placement into hosts. The main aim of the proposed VM consolidation is to maximize energy efficiency while preserving compliance with Service Level Agreements (SLAs) for sustainable environments. The experimental evaluation using real-world traces like PlanetLab, Bitbrains, and Alibaba Cluster 2020 workload validates that the proposed VM consolidation methods significantly reduce energy consumption and SLA violation compared to the existing methods for sustainable environments. The evaluation shows that the proposed ECSLN-based consolidation framework enables green cloud infrastructure by intelligently balancing energy consumption, SLA violation, and performance of cloud data centers.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"123-134"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606290","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

Toward a More Effective and Comprehensive Assessment of Data Center Environmental Impact 迈向更有效和全面的数据中心环境影响评估

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-20 DOI: 10.1109/TSUSC.2026.3666670

Dina Nassar;Rabih Bashroush

{"title":"Toward a More Effective and Comprehensive Assessment of Data Center Environmental Impact","authors":"Dina Nassar;Rabih Bashroush","doi":"10.1109/TSUSC.2026.3666670","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3666670","url":null,"abstract":"The escalating demand for data and computational power has led to the rapid expansion of data centres, significantly increasing their environmental footprint. Data centres now account for an estimated 2% to 4% of global electricity consumption. Although various best practices and key performance indicators (KPIs) have been developed to mitigate this impact, creating a comprehensive framework for evaluating the environmental sustainability of data centres remains challenging. Existing certification schemes, such as Leadership in Energy and Environmental Design (LEED) and the Building Research Establishment Environmental Assessment Method (BREEAM), were originally designed for traditional building infrastructure and are increasingly applied to data centres. However, these frameworks primarily focus on building-related aspects, inadequately addressing the unique mechanical, electrical, and IT dimensions critical to data centres. This study introduces a comprehensive evaluation model applied to the LEED framework, identifying significant gaps and proposing enhancements. The findings highlight notable discrepancies and limitations within the current LEED version, emphasising the need for tailored approaches that accurately reflect the distinct characteristics and operational demands of data centres.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"187-198"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11404237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}

引用次数: 0

BEExformer: A Fast Inferencing Binarized Transformer With Early Exits BEExformer：一种具有早期出口的快速推理二值化变压器

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-02-19 DOI: 10.1109/TSUSC.2026.3666456

Wazib Ansar;Saptarsi Goswami;Amlan Chakrabarti

{"title":"BEExformer: A Fast Inferencing Binarized Transformer With Early Exits","authors":"Wazib Ansar;Saptarsi Goswami;Amlan Chakrabarti","doi":"10.1109/TSUSC.2026.3666456","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3666456","url":null,"abstract":"Large Language Models (LLMs) based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and processing requirements hinder deployment on constrained resources. To enhance efficiency, binarization and Early Exit (EE) have proved to be effective solutions. However, binarization may lead to performance loss as reduced precision affects gradient estimation and parameter updates. Besides, research on EE mechanisms is still in its early stages. To address these challenges, we introduce Binarized Early Exit Transformer (BEExformer), a first-of-its-kind selective learning-based transformer integrating Binarization-Aware Training (BAT) with EE for efficient and fast textual inference. Each transformer block has an integrated Selective-Learn Forget Network (SLFN) to enhance contextual retention while eliminating irrelevant information. The BAT employs a differentiable second-order approximation to the sign function, enabling gradient computation that captures both the sign and magnitude of the weights. This aids in 21.30 × reduction in model size. The EE mechanism hinges on fractional reduction in entropy among intermediate transformer blocks with soft-routing loss estimation. This accelerates inference by reducing FLOPs by 52.27% and even improves accuracy by 3.22% by resolving the “overthinking” problem inherent in deep networks. Extensive evaluation through comparison with the SOTA methods and various ablations across nine datasets covering multiple NLP tasks demonstrates its Pareto-optimal performance-efficiency trade-off.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"98-110"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606312","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

Veri-SFL: Privacy-Preserving Verification of Resource Allocation and Data Trustworthiness in Sustainable Federated Learning Veri-SFL：可持续联邦学习中资源分配和数据可信度的隐私保护验证

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-01-26 DOI: 10.1109/TSUSC.2026.3653218

Yu-Chi Chen;You-Siang Liao;Zong-Sian Lai

{"title":"Veri-SFL: Privacy-Preserving Verification of Resource Allocation and Data Trustworthiness in Sustainable Federated Learning","authors":"Yu-Chi Chen;You-Siang Liao;Zong-Sian Lai","doi":"10.1109/TSUSC.2026.3653218","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3653218","url":null,"abstract":"Federated Learning (FL) is currently referred to as one of the privacy-enhancing technologies because of its service architecture. However, recent advancements in FL have highlighted its potential not only as a new framework of privacy but also as a key enabler of sustainable computing, which is expected to minimize the impact of an individual party to further improve the capacity of the machine learning model, energy efficiency, and reliability. For the above requirement of sustainability, resource allocation and trust management in FL are very infrastructural tasks of energy efficiency and reliability. In this paper, we present a framework, called Veri-SFL, to indicate verification for resource allocation and trust measurement in FL. We use trust scores to represent the credibility of each dataset without leaking any privacy, and utilize collaborative zk-SNARKs to verify the trust scores of each local dataset. Then, after verifying the correctness of trust levels, we present a solution to verify whether workers (model owners) are training according to the required distribution ratio by using collaborative zk-SNARKs.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"199-212"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606274","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

iClean: An Intelligent Industrial IoT Framework for Automatic Sustainable Air Quality Monitoring iClean：用于自动可持续空气质量监测的智能工业物联网框架

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1109/TSUSC.2026.3653174

Lopamudra Samal;Aryan Kumar Samal;Kamalakanta Mahapatra;Ayas Kanta Swain;Saraju P. Mohanty

{"title":"iClean: An Intelligent Industrial IoT Framework for Automatic Sustainable Air Quality Monitoring","authors":"Lopamudra Samal;Aryan Kumar Samal;Kamalakanta Mahapatra;Ayas Kanta Swain;Saraju P. Mohanty","doi":"10.1109/TSUSC.2026.3653174","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3653174","url":null,"abstract":"Air pollution monitoring systems are essential for evaluating rural and industrial environmental quality for safeguarding public health. This study presents a comprehensive IoT-based framework that uses low-cost sensors and machine learning algorithms for real-time monitoring of various pollutants, including LPG, methane, CO, alcohol, PM2.5, PM10, temperature, and humidity. The system gathers sensor data from a gateway node, which is then processed using Support Vector Machines (SVM) and Random Forest Regression (RFR) models to predict pollutant concentrations. Our approach features innovative methodologies for data validation, anomaly detection, and predictive modeling, employing Root Mean Squared Error (RMSE) as the performance metric. The model achieved a remarkably low RMSE value of 0.022, significantly improving the accuracy and reliability of air quality assessments. Experimental results highlight the system’s capability to capture complex environmental patterns and predict pollutant levels with high precision. This research intends air pollution monitoring from cost-effective Internet of Things (IoT) solutions and machine learning techniques. Additionally, the user interface is designed for mobile applications, offering real-time data access, alerts, and notifications, thereby enabling personalized environmental health management and targeted pollution control strategies in industrial areas.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 2","pages":"135-146"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606317","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

Robust Spatiotemporal Optimal Scheduling for Distributed Data Centers With Multiple Uncertainties 多不确定性分布式数据中心鲁棒时空最优调度

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.2026.3665882

Jianqiang Hu;Jiuan Lu;Josep M. Guerrero;Jinde Cao

引用次数: 0

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

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