IEEE Transactions on Sustainable Computing最新文献_第5页

Leveraging Blockchain and AI for Sustainable Recycling and Traceability in the Vehicle Industry 利用区块链和人工智能实现汽车行业的可持续回收和可追溯性

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.2026.3672831

Istiaque Ahmed;Kowshik Chowdhury;Kentaroh Toyoda;Tadashi Nakano;Thi Hong Tran

{"title":"Leveraging Blockchain and AI for Sustainable Recycling and Traceability in the Vehicle Industry","authors":"Istiaque Ahmed;Kowshik Chowdhury;Kentaroh Toyoda;Tadashi Nakano;Thi Hong Tran","doi":"10.1109/TSUSC.2026.3672831","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3672831","url":null,"abstract":"The increasing use of electric vehicles (EVs) has highlighted the need for sustainable recycling and traceability of essential raw materials. This study introduces a blockchain and AI-integrated framework designed with the possibility to optimize vehicle components lifecycle management with recycling and tracing throughout the supply chain. The system uses <italic>Decentralized Identifiers (DIDs) for secure identification of components, enabling transparent tracking from production to recycling. <italic>Hyperledger Fabric ensures immutable data anchoring across stakeholders. <italic>Hyperledger Caliper is used for benchmarking the system, assessing metrics such as transaction speed, latency, and scalability. <italic>AI models, including <italic>regression and <italic>clustering algorithms, are utilized to optimize recycling processes, predict component lifespans, and enhance resource recovery in the system. A <italic>tokenized reward mechanism incentivizes eco-friendly practices among stakeholders. The system also shows its environmental benefits, including energy savings from improved recycling performance. The proposed framework effectively supports the circular economy by enhancing resource recovery processes. While its design has the potential to reduce environmental impact, this benefit depends on the reusable, modular system design.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 3","pages":"227-239"},"PeriodicalIF":3.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171907","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

2025 Reviewers List* 2025审稿人名单*

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-02-04 DOI: 10.1109/TSUSC.2026.3653582

引用次数: 0

Formal Modeling and Analysis of Small-Scale Data Centers Integrating Renewable Energy Using Timed Automata 利用时间自动机集成可再生能源的小型数据中心形式化建模与分析

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-12-16 DOI: 10.1109/TSUSC.2025.3645150

Ismael Samaye;Gilles Sassatelli;Abdoulaye Gamatié

引用次数: 0

Adaptive Load Balancing in Vehicular Edge Computing Using Deep Reinforcement Learning and Model Compression 基于深度强化学习和模型压缩的车辆边缘计算自适应负载平衡

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-12-11 DOI: 10.1109/TSUSC.2025.3643431

Liang Zhao;Jiating Xu;Ammar Hawbani;Zhi Liu;Keping Yu;Yuanguo Bi

{"title":"Adaptive Load Balancing in Vehicular Edge Computing Using Deep Reinforcement Learning and Model Compression","authors":"Liang Zhao;Jiating Xu;Ammar Hawbani;Zhi Liu;Keping Yu;Yuanguo Bi","doi":"10.1109/TSUSC.2025.3643431","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3643431","url":null,"abstract":"In Vehicular Edge Computing (VEC), load imbalances among edge servers, driven by varying traffic densities and computational demands across geographic areas, can lead to significant delays, decreased efficiency, and potential service disruptions, adversely affecting both user experience and system reliability. This study proposes an innovative adaptive load balancing method that integrates deep reinforcement learning with predictive analytics to optimize resource allocation in VEC. The framework comprises a predictive model called ST-ChebNet, enhanced with Chebyshev polynomials in graph convolutional networks for accurate workload forecasting, and an adaptive model compression strategy utilizing knowledge distillation to dynamically adjust compression ratios based on anticipated workloads. Additionally, the integration of this predictive model with the Soft Actor-Critic (SAC) algorithm, termed GC-SAC, effectively combines graph-based predictive insights with reinforcement learning techniques to tailor resource distribution, minimizing computational delays and enhancing system responsiveness. The simulation results show that the GC-SAC algorithm can significantly reduce the average delay and average energy consumption of vehicular tasks, as well as the workload rate of edge servers.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 1","pages":"42-56"},"PeriodicalIF":3.9,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116898","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

Overlapping Coalition Formation-Enabled Noncooperative Game-Combined Multi-Agent DRL for UAV-Assisted Resource Allocation 基于重叠联盟的非合作博弈组合多智能体DRL无人机辅助资源分配

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-12-10 DOI: 10.1109/TSUSC.2025.3642616

Bing Ai;Guodong Ye;Zijun Wu;Yu Sun

{"title":"Overlapping Coalition Formation-Enabled Noncooperative Game-Combined Multi-Agent DRL for UAV-Assisted Resource Allocation","authors":"Bing Ai;Guodong Ye;Zijun Wu;Yu Sun","doi":"10.1109/TSUSC.2025.3642616","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3642616","url":null,"abstract":"Coalition Formation (CF) game emerges as a pioneering framework for resource allocation in uncrewed aerial vehicles (UAVs) equipped with various types of complementary resources. However, both the overlapping-enabled collaborative CF and inter-coalition competitive behaviors significantly impact the system performance in complex multi-UAV scenarios. In this paper, we propose a Multiple Overlapping Coalitions (MOC) noncooperative game. Specifically, we first establish an optimization model encompassing coupled resource constraints. Subsequently, a task-priority-based incentive mechanism is designed to better motivate participation. To achieve the Nash equilibrium, a two-step solution technique incorporating relaxation and fine-tuning of resource granularity is designed. We propose a MOC noncooperative game-combined Multi-agent Proximal Policy Optimization (MAOPPPO). The simulation results substantiate that our approach outperforms the other five state-of-the-art learning countermeasures in terms of average reward with a gain of up to 4.59% after 800 training episodes. In terms of throughput, the proposed MOC noncooperative game increases by 66.67%, 93.68%, and 11.76% compared with that of CF noncooperative game, non-CF noncooperative game, and consensus-based algorithm, respectively. For total resource contribution, the improvements are 62.99%, 94.59%, and 23.16%, respectively. The energy efficiency enhances by 6.82%, 23.68%, and 4.78% compared to the other three baselines, respectively.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 1","pages":"29-41"},"PeriodicalIF":3.9,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116891","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-Efficient Joint Deployment and Routing for Delay-Sensitive Microservices in Edge Computing 边缘计算中延迟敏感微服务的节能联合部署和路由

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-17 DOI: 10.1109/TSUSC.2025.3633312

Kai Peng;Hao Wen;Zhiyong Guo;Hanfang Ge;Chao Cai;Bo Zhou;Menglan Hu

{"title":"Energy-Efficient Joint Deployment and Routing for Delay-Sensitive Microservices in Edge Computing","authors":"Kai Peng;Hao Wen;Zhiyong Guo;Hanfang Ge;Chao Cai;Bo Zhou;Menglan Hu","doi":"10.1109/TSUSC.2025.3633312","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3633312","url":null,"abstract":"Microservice as a promising architecture has been widely employed in edge computing to support sensitive-latency online applications. Unfortunately, the deployment of numerous microservices creates complex invocations and requires frequent communications, which brings significant challenges to service deployment and request routing. Moreover, the strict requirements for low energy consumption and low latency in edge computing further exacerbate these difficulties. In this case, it is crucial to optimize the joint microservices deployment and request routing using a meticulous and energy-efficient approach. However, existing studies often overlook their interdependence and treat them as separate problems. Therefore, we propose a fine-grained approach in this paper to jointly optimize the deployment and request routing of microservices within edge computing scenarios. First, we utilize queuing networks to conduct detailed modeling and mathematical analysis that study the complex invocation relationships, microservice instance sharing, and communication latency. Second, we propose an energy-efficient microservice orchestration algorithm, referred to as Cluster-Processing-based Adaptive Memory Procedure. This algorithm maintains a memory storing elite solution elements, and it iteratively picks up suitable elements from the memory to construct superior solutions. Finally, extensive simulation experiments demonstrate that the proposed algorithm outperforms baseline algorithms significantly in terms of response latency and energy consumption.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 1","pages":"1-14"},"PeriodicalIF":3.9,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116821","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

Quantifying Robustness and Sustainability Trade-Off in Federated Adversarial Learning for Cyber-Physical Systems 量化网络物理系统联邦对抗学习的鲁棒性和可持续性权衡

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-17 DOI: 10.1109/TSUSC.2025.3633995

Syed Mhamudul Hasan;Ahmed Imteaj;Abdur R. Shahid

{"title":"Quantifying Robustness and Sustainability Trade-Off in Federated Adversarial Learning for Cyber-Physical Systems","authors":"Syed Mhamudul Hasan;Ahmed Imteaj;Abdur R. Shahid","doi":"10.1109/TSUSC.2025.3633995","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3633995","url":null,"abstract":"Cyber-Physical Systems (CPS) are increasingly leveraging Federated Learning (FL) to enable decentralized intelligence while preserving privacy across distributed devices. Federated adversarial learning (FAL) leverages FL and adversarial training to enhance model robustness against adversarial attacks while maintaining data privacy across decentralized, heterogeneous devices. While FAL strengthens CPS resilience against adversarial threats, variations in energy constraints, carbon emissions, computational capabilities, and latency requirements introduce additional complexity. These variations impact energy consumption, carbon emissions, and power source efficiency, creating a complex trade-off between sustainability and robustness. This underscores the critical need for standardized metrics to systematically evaluate and balance these competing factors in FAL-enabled CPS. In this paper, we propose three novel robustness metrics designed to quantify the interplay between energy efficiency, sustainability dimensions, and adversarial robustness in FAL setups for CPS. The proposed methodology accounts for diverse CPS scenarios, addressing factors such as emissions, energy consumption, latency, renewable energy, and low-energy devices with critical latency needs. We validate our approach through simulations in two setups, including a single-device environment to isolate device variability and a heterogeneous multi-device environment to evaluate architectural impacts. The results demonstrate the effectiveness of our proposed metrics in systemically quantifying the trade-off between sustainability and robustness in FAL-based CPS.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 1","pages":"15-28"},"PeriodicalIF":3.9,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116923","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

Improving Energy Efficiency of Graph Processing on Shared-Memory Systems 提高共享内存系统中图形处理的能效

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-13 DOI: 10.1109/TSUSC.2025.3632842

Tao Jiang;Le Luo;Chao Li;Jinyang Guo;Sheng Xu

{"title":"Improving Energy Efficiency of Graph Processing on Shared-Memory Systems","authors":"Tao Jiang;Le Luo;Chao Li;Jinyang Guo;Sheng Xu","doi":"10.1109/TSUSC.2025.3632842","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3632842","url":null,"abstract":"With the number of cores increasing in shared-memory systems, the energy consumption of parallel computing on them is becoming increasingly prominent. Currently, researchers concern with the performance optimization, while ignoring the energy efficiency of graph processing. Meanwhile, existing works that optimize energy efficiency involve mainly the general benchmarks by using dynamic voltage and frequency scaling and thread throttling methods. However, these methods cannot be directly transplanted to graph processing, because most graph algorithms converge in fewer iterations and traditional energy efficiency optimization methods are not applicable to them and will produce much overhead, resulting in the fact that the loss outweighs the gain. And some energy-saving methods estimate the subsequent CPU frequency based on the run-time system state, which leads to an inaccurate prediction of the optimal energy-saving CPU frequency. In view of the above issues, we propose a pre-allocated thread throttling method and a static frequency scaling method. The former achieves thread throttling by establishing a pre-allocated scheduling method, which calculates the optimal energy-saving number of threads promptly when the graph is loaded; On this basis, in order to reduce the cost of dynamic frequency setting at runtime and improve the energy efficiency further, the latter introduces the static frequency scaling method to reduce the execution speed of some tasks by relaxing thread execution time. The experimental results show that the pre-allocated thread throttling method improves the energy efficiency by about 10% compared to the original framework, and the static frequency scaling method further improves it by about 20% with trivial performance loss.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1449-1460"},"PeriodicalIF":3.9,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145712560","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

Privacy-Preserving Distributed Economic Dispatch for Microgrids Based on Hybrid Privacy Strategies 基于混合隐私策略的微电网隐私保护分布式经济调度

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-10-31 DOI: 10.1109/TSUSC.2025.3626687

Lei Sun;Derui Ding;Xingzhen Bai

{"title":"Privacy-Preserving Distributed Economic Dispatch for Microgrids Based on Hybrid Privacy Strategies","authors":"Lei Sun;Derui Ding;Xingzhen Bai","doi":"10.1109/TSUSC.2025.3626687","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3626687","url":null,"abstract":"Economic dispatch plays a significant role in intelligent microgrids, which offer a promising approach to facilitate the integration of distributed renewable energy. In the past few years, information sharing has promoted new development opportunities for distributed economic dispatch algorithms of microgrids. However, integrating communications into distributed algorithms gives rise to significant concerns about data security and privacy. As such, this paper investigates a privacy-preserving distributed economic dispatch problem within microgrids. The primary objective is to propose a privacy-preserving distributed zero-gradient-sum (DZGS) algorithm that realizes optimal power dispatch with the balance of supply and demand while ensuring sensitive information from potential leakage. To achieve this goal, the hybrid privacy strategies based on a variable decomposition rule and noise injection are implemented to enhance the security of sensitive data. Sufficient conditions, including a suitable range of step sizes, are established theoretically to reveal the linear convergence of the algorithm. Furthermore, the privacy of the algorithm is analyzed from the perspectives of both honest-but-curious nodes and external eavesdroppers. Finally, the effectiveness of the algorithm is validated through empirical analyses of the IEEE 39-bus system.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1409-1420"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478867","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

Cloud Power Meter: Bridging the Gap in Power Measurement for Heterogeneous Cloud 云功率计：弥合异构云功率测量的差距

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-09-08 DOI: 10.1109/TSUSC.2025.3607207

Amina Nasrin;Dayuan Chen;Ziliang Zong

{"title":"Cloud Power Meter: Bridging the Gap in Power Measurement for Heterogeneous Cloud","authors":"Amina Nasrin;Dayuan Chen;Ziliang Zong","doi":"10.1109/TSUSC.2025.3607207","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3607207","url":null,"abstract":"The significance of accurate power measurement in cloud computing can be attributed to its increasing usability as well as for attaining energy savings through optimization and advancing green computing in diverse cloud platforms like AWS, GCP, Chameleon Cloud, and Microsoft Azure. The challenge of providing a single yet accurate methodology, suitable for the cloud environment, is exacerbated by factors like numerous heterogeneity of the cloud, unavailability of administrative access required by precise methods like Intel’s Running Average Power Limit (RAPL) and the inaccuracy of widely-accepted methods like Thermal Design Power (TDP) based estimation. In this paper, we propose Cloud Power Meter (CPM) in pursuit of sustainability in cloud operations, which integrates a decision tree with multi-variable polynomial regression. Being an ML-based method that is trained using diverse cloud instances from Amazon, Microsoft, and Google, CPM is capable of providing precise power estimation on the cloud platforms without administrative privileges. The decision tree guarantees performance capacity across heterogeneous cloud environments by locating the “CPM Instance” - the closest match of instances that are not familiar to CPM. In evaluation through the Standard Performance Evaluation Corporation (SPEC) benchmark’s CPU power consumption, CPM outperforms 14 existing power models and its power estimation matches Intel’s RAPL power model. To evaluate the effectiveness of CPM, we conduct an extensive analysis of approximately 2.6 million real-world Virtual Machines (VMs) running on Microsoft Azure. CPM successfully identifies opportunities to save over 80,000 kW-hours (kWh) of energy in a single month through optimized resource allocation. Notably, about 25,000 kWh of this energy is being wasted by more than 17% of the VMs. Further, CPM’s energy estimations reveal that 366 VMs (out of roughly 2.6 million) wasted nearly 15,000 kWh of energy despite never exceeding 2% CPU utilization.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1252-1266"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480798","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