IEEE Transactions on Sustainable Computing最新文献_第7页

Energy-Efficient Computation With DVFS Using Deep Reinforcement Learning for Multi-Task Systems in Edge Computing 边缘计算中基于深度强化学习的DVFS节能计算

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-07-31 DOI: 10.1109/TSUSC.2025.3593971

Xinyi Li;Ti Zhou;Haoyu Wang;Man Lin

{"title":"Energy-Efficient Computation With DVFS Using Deep Reinforcement Learning for Multi-Task Systems in Edge Computing","authors":"Xinyi Li;Ti Zhou;Haoyu Wang;Man Lin","doi":"10.1109/TSUSC.2025.3593971","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3593971","url":null,"abstract":"Finding an optimal energy-efficient policy that is adaptable to underlying edge devices while meeting deadlines for tasks has always been challenging. This research studies generalized systems with multitask, multi-deadline scenarios with reinforcement learning-based DVFS for energy saving for periodic soft real-time applications on edge devices. This work addresses the limitation of previous work that models a periodic system as a single task and single-deadline scenario, which is too simplified to cope with complex situations. The method encodes time series data in the Linux kernel into information that is easy to interpret for reinforcement learning, allowing the system to generate DVFS policies to adapt system patterns based on the general workload. For encoding, we present two different methods for comparison. Both methods use only one performance counter: system utilization, and the kernel only needs minimal information from the userspace. Our method is implemented on Jetson Nano Board (2 GB) and is tested with three fixed multitask workloads, which are three, five, and eight tasks in the workload, respectively. For randomness and generalization, we also designed a random workload generator to build different multitask workloads to test. Based on the test results, our method could save 3%-10% power compared to Linux built-in governors.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1116-1127"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480126","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

Enhancing Sustainability in LLM Training: Leveraging Federated Learning and Parameter-Efficient Fine-Tuning 增强法学硕士培训的可持续性：利用联邦学习和参数有效微调

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-07-23 DOI: 10.1109/TSUSC.2025.3592043

Sunbal Iftikhar;Saeed Hamood Alsamhi;Steven Davy

{"title":"Enhancing Sustainability in LLM Training: Leveraging Federated Learning and Parameter-Efficient Fine-Tuning","authors":"Sunbal Iftikhar;Saeed Hamood Alsamhi;Steven Davy","doi":"10.1109/TSUSC.2025.3592043","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3592043","url":null,"abstract":"Large language models deliver strong performance but at the cost of high energy use and carbon emissions, limiting their accessibility. Federated Learning (FL) addresses energy efficiency by utilizing distributed data sources, coordinating learning from diverse, energy-constrained clients, and allowing the training process to occur without the need for large centralized datasets. This paper evaluates LLM training using various FL client selection strategies and a centralized approach, integrating Parameter-Efficient Fine-Tuning (PEFT). The assessment focuses on carbon emissions, energy consumption, and model performance, providing insights into the environmental impact of different training methods and identifying sustainable practices. Results show that centralized training achieves the highest accuracy but lacks privacy and scalability, whereas FL methods like FedFull+LoRa provide comparable accuracy but incur higher energy consumption and emissions. FedProx+LoRa and FedNorm+LoRa balance energy efficiency and accuracy. We also extended our FedSustain client selection strategy by incorporating real-time renewable-energy signals from the Electricity Maps API alongside on-device energy buffers. On an IMDB/DistilBERT benchmark, FedSustain matches leading FL baselines in both accuracy and convergence speed while reducing total energy use by up to 38% and CO<inline-formula><tex-math>$_{2}$</tex-math></inline-formula> emissions by up to 46% . Furthermore, to address the limitations of the current client selection strategy, we present an enhanced strategy for future evaluation on more complex tasks and larger models. These results highlight the potential of energy-aware client selection to make large-model training more sustainable.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1158-1172"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11091528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479776","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

FedCAD: Federated Cyberattack Attribution Detection for Internet of Things Forensics 面向物联网取证的联邦网络攻击归因检测

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-10-08 DOI: 10.1109/TSUSC.2025.3619037

Hania Mohamed;Nour Moustafa;Nickolaos Koroniotis;Zahir Tari;Albert Zomaya;Francesco Schiliro

{"title":"FedCAD: Federated Cyberattack Attribution Detection for Internet of Things Forensics","authors":"Hania Mohamed;Nour Moustafa;Nickolaos Koroniotis;Zahir Tari;Albert Zomaya;Francesco Schiliro","doi":"10.1109/TSUSC.2025.3619037","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3619037","url":null,"abstract":"Attributing cyberattacks in Internet of Things (IoT) environments is challenging due to their distributed, heterogeneous natures and the limitations of traditional Digital Forensics (DF) tools for preserving privacy and scalability. This paper presents FedCAD, a novel Federated Learning (FL)-based forensic method for multi-attribute cyberattack attribution that operates directly on IoT devices. It leverages a lightweight 1D Convolutional Neural Network (CNN) architecture with a shared feature extractor and three parallel sub-networks, each of which targets a distinct attribute, i.e., attack types, tactics/tools, and motives. Its architecture consists of three layers: an IoT device one collecting traces and local training; an FL one for privacy-preserving model aggregation via FedAvg; and a DF analysis one for multi-attribute inference and reporting. Also, FedCAD integrates the MITRE ATT&CK framework to enrich training data with real-world adversarial knowledge. Evaluations on the three public datasets TON-IoT, Bot-IoT, and UNSW-NB15 show that FedCAD outperforms centralized models, with gains of 2.1% accuracy, 1.0% precision, 1.5% recall, and 1.3% f1-score on TON-IoT. Experimental results demonstrate FedCAD’s effectiveness as a scalable, privacy-preserving solution for cyberattack attribution in dynamic IoT ecosystems.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1366-1377"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479533","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

Sustainable AI: Emerging Trends, Impacts, and Future Challenges 可持续人工智能：新兴趋势、影响和未来挑战

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-09-17 DOI: 10.1109/TSUSC.2025.3611272

Adamu Gaston Philipo;Huansheng Ning;Doreen Sebastian Sarwatt;Jumanne Ally Mohamed;Afidhu Swaibu Yusufu;Feifei Shi;Shepherd Urenje;Jianguo Ding

引用次数: 0

Multi-User Oriented Data Sharing Scheme for Internet of Medical Things Based on Dual Cryptography Mechanism 基于双密码机制的面向多用户的医疗物联网数据共享方案

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-10-08 DOI: 10.1109/TSUSC.2025.3619389

Guiping Zheng;Bei Gong;Muhammad Waqas;Iftekhar Ahmad;Hisham Alasmary;Sheng Chen

{"title":"Multi-User Oriented Data Sharing Scheme for Internet of Medical Things Based on Dual Cryptography Mechanism","authors":"Guiping Zheng;Bei Gong;Muhammad Waqas;Iftekhar Ahmad;Hisham Alasmary;Sheng Chen","doi":"10.1109/TSUSC.2025.3619389","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3619389","url":null,"abstract":"Encrypted sharing of Internet of Medical Things (IoMT) data is essential for facilitating collaboration, safeguarding patient privacy, and advancing clinical research. However, existing encryption schemes face numerous challenges in multi-user environments. Traditional proxy re-encryption requires exclusive ciphertext for each user, which is evidently unsuitable for IoMT’s multi-user scenarios. Meanwhile, attribute-based encryption provides flexible data access control, but its complex computations and high resource demands limit its use in large-scale IoMT environments. Additionally, challenges like single-point failure and redundant backups emerge in ciphertext storage. To address these challenges, we propose a dual-cryptography mechanism integrating enhanced proxy re-encryption and attribute-based encryption. Our scheme enables unified ciphertext access for authorized users while applying attribute encryption exclusively to small data keys. To mitigate potential data loss from storage server failures, we propose a decentralized ciphertext storage and recovery mechanism with verifiable secret sharing. Furthermore, we implement decentralized ciphertext storage using verifiable secret sharing, ensuring recoverability from server failures. Formal analysis proves confidentiality under the random oracle model. Experimental results demonstrate high security strength, computational efficiency, and robustness. The solution prevents single-point failures, resists collusion attacks, and maintains traceability through blockchain-integrated audit trails.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1393-1408"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478865","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

RLA: A Low-Latency and High-Smoothness Path Planning System Based on Interpolation and Velocity Control 基于插值和速度控制的低延迟、高平滑路径规划系统

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-09-03 DOI: 10.1109/TSUSC.2025.3605493

Xupeng Zhu;Xin Niu;Wang Chen;Chen Yu

引用次数: 0

Leveraging Customized Heterogeneous Batteries to Alleviate Low Battery Experience for Mobile Users 利用定制的异构电池来缓解移动用户的低电量体验

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-09-17 DOI: 10.1109/TSUSC.2025.3611159

Jaeheon Kwak;Sunjae Lee;Dae R. Jeong;Arjun Kumar;Dongjae Shin;Ilju Kim;Donghwa Shin;Kilho Lee;Jinkyu Lee;Insik Shin

{"title":"Leveraging Customized Heterogeneous Batteries to Alleviate Low Battery Experience for Mobile Users","authors":"Jaeheon Kwak;Sunjae Lee;Dae R. Jeong;Arjun Kumar;Dongjae Shin;Ilju Kim;Donghwa Shin;Kilho Lee;Jinkyu Lee;Insik Shin","doi":"10.1109/TSUSC.2025.3611159","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3611159","url":null,"abstract":"Even with advances in single-cell batteries, mobile users still experience low battery anxiety. By analyzing 19,855 hours of user behavior, we propose <inline-formula><tex-math>$mathsf{MixMax}$</tex-math></inline-formula>, a heterogeneous battery system consisting of three complementary battery types tailored to minimizing low battery time. While the heterogeneous battery system offers an opportunity to simultaneously improve capacity and charging speed, one must face non-trivial challenges to design charge/discharge policies during runtime and determine the ratio of enclosed batteries. They are highly dependent on each other, which entails almost infinite candidates for the choice. <inline-formula><tex-math>$mathsf{MixMax}$</tex-math></inline-formula> simplifies this by reformulating the problem as an optimization problem, breaking it down into manageable sub-problems. However, <inline-formula><tex-math>$mathsf{MixMax}$</tex-math></inline-formula> still faces the challenge of catering to all users due to their diverse battery usage patterns. To address this, we introduce a customized <inline-formula><tex-math>$mathsf{MixMax}$</tex-math></inline-formula> that groups users based on their usage patterns and provides tailored battery solutions. In evaluating <inline-formula><tex-math>$mathsf{MixMax}$</tex-math></inline-formula>, we fabricate coin-cell batteries, develop a precise battery emulator using the fabricated batteries, and prototype <inline-formula><tex-math>$mathsf{MixMax}$</tex-math></inline-formula> on a real-world smartphone. Our evaluation shows that <inline-formula><tex-math>$mathsf{MixMax}$</tex-math></inline-formula> reduces low battery time by up to 24.6% without compromising capacity, volume, weight, or user behavior, and its customized version can further reduce it by up to 46.2%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1292-1309"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479946","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

Advancing Sustainability Through Social Media: A Comprehensive Survey 通过社会媒体推进可持续发展：一项综合调查

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-07-22 DOI: 10.1109/TSUSC.2025.3591527

Shashank Sheshar Singh;Sumit Kumar;Avadh Kishor;Albert Y. Zomaya

{"title":"Advancing Sustainability Through Social Media: A Comprehensive Survey","authors":"Shashank Sheshar Singh;Sumit Kumar;Avadh Kishor;Albert Y. Zomaya","doi":"10.1109/TSUSC.2025.3591527","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3591527","url":null,"abstract":"In recent years, social media has emerged as a powerful tool for sustainability marketing. It leverages its extensive reach and interactive characteristics to raise awareness of environmental issues, promote community engagement, and influence policy development. This paper comprehensively analyzes different strategies used on social media platforms to support sustainability goals. We have classified these strategies into six broad categories: campaigns and education to increase awareness; activities that engage and build communities; efforts to advocate and influence policies; initiatives related to corporate social responsibility and branding; projects that use crowdsourcing and collaboration; and behavior change campaigns. We assess the effectiveness of these strategies based on case studies and key performance metrics for sustainable practices, public behaviors, and corporate marketing. Additionally, the presented survey highlights issues such as misinformation, engagement fatigue, and authenticity, necessitating solutions to address these concerns. This study highlights the vital role of social media in achieving sustainability objectives and outlines future research directions to improve its effectiveness in supporting these goals. Therefore, this research offers valuable insights for practitioners, policymakers, and scholars aiming to leverage social media platforms to advance sustainability goals.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1128-1146"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480609","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

NAAFL: A Non-Authoritative Anarchic Federated Learning for Defending Against Malicious Attacks NAAFL：用于防御恶意攻击的非权威无政府联邦学习

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-09-02 DOI: 10.1109/TSUSC.2025.3605384

Ruihong Xiu;Junqing Le;Di Zhang;Qingguo Lü;Tao Xiang;Xiaofeng Liao

{"title":"NAAFL: A Non-Authoritative Anarchic Federated Learning for Defending Against Malicious Attacks","authors":"Ruihong Xiu;Junqing Le;Di Zhang;Qingguo Lü;Tao Xiang;Xiaofeng Liao","doi":"10.1109/TSUSC.2025.3605384","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3605384","url":null,"abstract":"The centralized server in traditional federated learning (FL) is authoritative (i.e. decisive control), which may cause immeasurable damage to the system’s security in the event of decision failure or attack. To weaken the authority of the central server, existing studies have proposed blockchain-based federated learning (BFL) approaches. However, existing BFL still suffers from high resource overhead and difficulty in resisting high malicious ratio (more than 50%) attacks. To address the above challenge, this paper proposes an efficient and secure non-authoritative (i.e. highly decentralized) anarchic (i.e. distributed self-governance) federated learning framework which is named NAAFL. During the local process of NAAFL, an area credit-based screening mechanism for participating devices is proposed to ensure that participating devices are always highly trusted devices with higher total credit values. Then, to effectively exclude a high percentage of malicious training gradients, a multi-device validation voting mechanism based on historical information is designed to construct the global gradient. Subsequently, to weaken the central server authority and reduce the resource overhead while guaranteeing security, a secure and low-consumption consensus mechanism based on the federation chain is proposed, and the overhead is further reduced by a momentum acceleration algorithm. Finally, the theoretical analysis and experimental simulation are conducted on the proposed NAAFL. The results further show that the proposed NAAFL outperforms existing studies and can defend against attacks with up to 80% malicious ratio, which exceeds the common threshold (50%) of existing studies. Meanwhile, the overhead of NAAFL is reduced by about 77.51% compared to BFL.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1202-1215"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479052","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

Carbon Emission Quantification of Machine Learning: A Review 机器学习的碳排放量化研究综述

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-06-11 DOI: 10.1109/TSUSC.2025.3578834

Syed Mhamudul Hasan;Taminul Islam;Munshi Saifuzzaman;Khaled R. Ahmed;Chun-Hsi Huang;Abdur R. Shahid

{"title":"Carbon Emission Quantification of Machine Learning: A Review","authors":"Syed Mhamudul Hasan;Taminul Islam;Munshi Saifuzzaman;Khaled R. Ahmed;Chun-Hsi Huang;Abdur R. Shahid","doi":"10.1109/TSUSC.2025.3578834","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3578834","url":null,"abstract":"The rapid growth of machine learning (ML) technologies has raised significant concerns about their environmental impact, particularly regarding energy consumption and carbon emissions. This comprehensive review examines the intersection of ML and sustainability, synthesizing research from 2014 to 2024 to provide a holistic view of sustainable ML practices. This systematic review, encompassing over 200 peer-reviewed publications, reveals a growing emphasis on quantifying and mitigating the environmental footprint of ML systems. Key findings include: (1) a 300% increase in sustainable ML research since 2020; (2) the emergence of specialized carbon footprint quantification tools for ML; and (3) promising advancements in energy-efficient algorithms and green computing infrastructure. This research identifies critical challenges, including the lack of standardized sustainability metrics and the need for more robust life-cycle assessments of ML systems. The review also highlights the potential of transfer learning, federated learning, and hardware innovations in reducing ML’s environmental impact. The analysis culminates in a novel framework for implementing sustainable practices in ML projects and a detailed roadmap for future research. This work provides researchers, practitioners, and policymakers with crucial insights to drive the development of more environmentally responsible ML technologies, ultimately contributing to global sustainability goals.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1085-1102"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479055","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