IEEE Transactions on Sustainable Computing最新文献

A Hierarchical Geospatial-Temporal Fusion Transformer Framework for Interpretable Mobile Network Payload Forecasting 面向可解释移动网络有效载荷预测的分层地空-时融合变压器框架

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-09-01 Epub Date: 2026-08-06 DOI: 10.1109/TSUSC.2026.3720620

Mehrdad Tanha;Reza Seyed Mohammadi

{"title":"A Hierarchical Geospatial-Temporal Fusion Transformer Framework for Interpretable Mobile Network Payload Forecasting","authors":"Mehrdad Tanha;Reza Seyed Mohammadi","doi":"10.1109/TSUSC.2026.3720620","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3720620","url":null,"abstract":"With the escalating demand for mobile data, accurate forecasting of network traffic is paramount for resource management and Quality of Service (QoS). While Long Short-Term Memory (LSTM) networks have been applied, they often struggle with long-range dependencies and incorporating static metadata. More advanced spatio-temporal models often operate as “opaque model,” limiting their operational utility. This paper proposes GNTFT, a novel, hierarchical framework centered on the Temporal Fusion Transformer (TFT) that prioritizes both accuracy and interpretability. Our system first segments the network into behaviorally homogeneous clusters, then learns static, graph-based spatial embeddings for each sector using a Graph Neural Network (GNN). These embeddings are fused with historical KPI data within a specialized TFT model for each cluster. We conduct a rigorous benchmark against a traditional LSTM, Spatio-Temporal Graph Convolutional Network (STGCN), Diffusion Convolutional Recurrent Neural Network (DCRNN), and the state-of-the-art Graph WaveNet (GWN). The results demonstrate that while GWN achieves the highest absolute accuracy (MAE), our proposed Full GNTFT framework achieves superior performance across all metrics (MAE: 110.12, MAPE: 22.05%), compared to the baseline “TFT Only” model (MAE: 158.82, MAPE: 31.42%). Crucially, the GNTFT’s hierarchical architecture provides deep interpretability; by training specialized models for each behavioral cluster, we demonstrate that the predictive drivers of network traffic are highly heterogeneous. This work presents a transparent solution that bridges the gap between raw predictive power and the actionable, trustable intelligence that state-of-the-art models cannot provide.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 5","pages":"466-475"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131041","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

Flying Over Dynamism: Predictive Intelligence and Evolutionary Optimization for Multi-UAV Crowdsensing 动态飞行：多无人机群体感知的预测智能和进化优化

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-09-01 Epub Date: 2026-07-20 DOI: 10.1109/TSUSC.2026.3715184

Wenhan Jia;Minghui Liwang;Zhipeng Cheng;Xiaoyu Xia

{"title":"Flying Over Dynamism: Predictive Intelligence and Evolutionary Optimization for Multi-UAV Crowdsensing","authors":"Wenhan Jia;Minghui Liwang;Zhipeng Cheng;Xiaoyu Xia","doi":"10.1109/TSUSC.2026.3715184","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3715184","url":null,"abstract":"The rapid growth of mobile crowdsensing (MCS) has motivated the deployment of uncrewed aerial vehicles (UAVs) for large-scale, flexible, and timely data collection. However, trajectory planning in multi-UAV MCS remains highly challenging due to the dynamic and uncertain activation of points of interest (PoIs), stringent energy limitations, and safety risks arising from high-density UAV operations. Existing reinforcement learning approaches can face scalability and training-stability challenges in large-scale coordination under dynamic and uncertain PoI settings, while many heuristic methods rely on oversimplified assumptions such as static PoIs, leading to suboptimal performance in dynamic environments. To overcome these limitations, this paper proposes Pre-Evo, a novel hybrid Prediction-Evolution framework that synergistically integrates predictive intelligence with evolutionary optimization for efficient and reliable multi-UAV coordination. On the prediction side, long short-term memory (LSTM) networks are employed to accurately forecast the active time windows of dynamic PoIs, thereby transforming temporal uncertainty into a tractable decision-making input. On the optimization side, we design an enhanced multi-agent genetic algorithm (EnhancedMAGA) equipped with three tailored mechanisms. This co-design of predictive modeling and evolutionary search enables Pre-Evo to strike a balanced trade-off among maximizing task completion, minimizing flight distance and energy usage, and reducing collision risk in dense UAV swarms. Extensive experiments on both real-world urban traffic datasets and diverse synthetic scenarios demonstrate that Pre-Evo consistently outperforms state-of-the-art benchmarks. Notably, Pre-Evo also maintains robust generalization to large-scale and highly dynamic environments, highlighting its potential for next-generation UAV-assisted MCS systems.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 5","pages":"530-542"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131283","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

KORDI: A Framework for Real-Time Performance and Cost Optimization of Apache Spark KORDI：用于Apache Spark实时性能和成本优化的框架

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-09-01 Epub Date: 2026-07-23 DOI: 10.1109/TSUSC.2026.3716599

Athanasios Kordelas;Spyros Voulgaris;Vasileios Megalooikonomou;Nikos Deligiannis

{"title":"KORDI: A Framework for Real-Time Performance and Cost Optimization of Apache Spark","authors":"Athanasios Kordelas;Spyros Voulgaris;Vasileios Megalooikonomou;Nikos Deligiannis","doi":"10.1109/TSUSC.2026.3716599","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3716599","url":null,"abstract":"Apache Spark is one of the most commonly used frameworks for Big Data processing. Research on the provided streaming dynamic resource allocation feature has shown that large data load fluctuations, such as those seen in website traffic, negatively impact automatic scaling. Further analysis indicates that the lack of data load prediction, particularly during peak hours or days, is the root cause of this issue. In this paper, we propose an enhanced solution, KORDI (Knowledge-based Orchestrated Resource Distribution), which enables the online modeling of data size for real-time forecasting and control of future Spark resource requirements. This solution optimizes the allocation of Spark resources for streaming applications by employing a well-established machine learning technique, namely SARIMAX, to forecast anticipated data load before passing it to the Load Balancing Engine. The engine then appropriately adjusts the Spark cluster size. Experimental results demonstrate that the proposed solution reduces costs by 38% compared to static resource provisioning without compromising stability.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 5","pages":"556-569"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131284","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 Novel Two-Stage Forecasting Framework With Difference-Based Learning for Power Load Forecasting 基于差异学习的电力负荷预测两阶段预测框架

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-09-01 Epub Date: 2026-08-05 DOI: 10.1109/TSUSC.2026.3720821

Zhongpeng Li;Xiaoliang Wang;Juping Gu;Hanjing Cheng;Yehui Zhu;Ke-cai Cao;Hongjie Wu

{"title":"A Novel Two-Stage Forecasting Framework With Difference-Based Learning for Power Load Forecasting","authors":"Zhongpeng Li;Xiaoliang Wang;Juping Gu;Hanjing Cheng;Yehui Zhu;Ke-cai Cao;Hongjie Wu","doi":"10.1109/TSUSC.2026.3720821","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3720821","url":null,"abstract":"Accurate short-term load forecasting is critical for the safe and efficient operation of power systems, in the context of increasing demand fluctuations and the growing penetration of renewable energy sources. However, the strong non-stationarity of load sequences poses significant challenges to existing forecasting models, making it difficult to effectively capture complex temporal dynamics and rapidly changing load patterns. To address these issues, a Two-Stage Decomposition Network (TSD-NET) is proposed as a forecasting framework, which tackles the above challenges through staged prediction, progressive refinement, and structured output fusion. The two-stage architecture first captures coarse-grained temporal trends to establish a stable forecasting baseline, followed by targeted adjustments for short-term fluctuations, thereby alleviating the adverse effects of non-stationarity. To further enhance the model’s capacity to learn fine-grained load features, the network incorporates a series of refinement blocks that iteratively extract previously under-learned patterns. A structured fusion module is employed to integrate outputs from all stages, improving predictive accuracy. Extensive experiments on real-world datasets from Australia, Shanghai, and Tetouan show that our method reduces MSE by up to 46.2% and MAPE by up to 23.7%, and improves <inline-formula><tex-math>$R^{2}$</tex-math></inline-formula> by up to 4.5 percentage points compared with the best baseline, offering a robust and generalizable solution for dynamic load prediction in complex power-system environments.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 5","pages":"476-488"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131042","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

Green Cybersecurity Metrics for Intrusion Detection Systems 入侵检测系统的绿色网络安全指标

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-09-01 Epub Date: 2026-08-21 DOI: 10.1109/TSUSC.2026.3723035

Mohamed Younsi;Omar Abdul Wahab;Adel Abusitta;Martine Bellaïche

{"title":"Green Cybersecurity Metrics for Intrusion Detection Systems","authors":"Mohamed Younsi;Omar Abdul Wahab;Adel Abusitta;Martine Bellaïche","doi":"10.1109/TSUSC.2026.3723035","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3723035","url":null,"abstract":"Intrusion Detection System (IDS) is a vital solution in the cyber defense architecture, and its implementation should guarantee secure data transactions. This role is becoming increasingly critical as ever-increasing technological democratization has transformed the digital field into a high-risk environment for cyberattacks, leading to an increasing volume of network traffic. Under these circumstances, IDSs will be in higher demand and consume more energy. To address this challenge, the objective of this study is to introduce new sustainability metrics, including Green Feature Extraction (GFE), Redundancy Efficient Rule (RER), Waste Rate (WR), and Path Selection Energy Consumption (PSEC), to quantify the environmental impact of IDS operations. The core of our study examines data flows, algorithms, deployment architecture, configuration, and feature engineering techniques to assess their impact on IDS sustainability. The experimental validation confirms the feasibility and effectiveness of the proposed metrics: the GFE analysis shows that using only 5 features achieves 99.58% detection accuracy with optimal energy efficiency, the WR analysis reveals that Random Forest achieves the lowest waste (0.72% vs. 15.48% for Logistic Regression), and the PSEC analysis demonstrates that Gain Ratio consumes 69% less energy than Chi-Square (29.5 kW vs. 95.4 kW). These findings demonstrate substantial potential for sustainable IDS design that balances robust threat detection with environmental responsibility.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 5","pages":"543-555"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131229","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

Scalability of Energy Efficiency in High-Performance Computing Systems 高性能计算系统中能源效率的可扩展性

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-09-01 Epub Date: 2026-07-20 DOI: 10.1109/TSUSC.2026.3715390

Alberto Prieto;Beatriz Prieto;Juan José Escobar;Jesús González

引用次数: 0

CViTRNN: A Lightweight Recurrent Network With an Embedded Channel-Aware Vision Transformer for Multidimensional Wind Power Forecasting CViTRNN：一种具有嵌入式通道感知视觉变压器的轻量级递归网络，用于多维风电预测

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-09-01 Epub Date: 2026-08-07 DOI: 10.1109/TSUSC.2026.3721146

Chuanru Ren;Yao Lu;Cheng Wang;Tianjin Huang;Yunyin Li;Lu Liu

{"title":"CViTRNN: A Lightweight Recurrent Network With an Embedded Channel-Aware Vision Transformer for Multidimensional Wind Power Forecasting","authors":"Chuanru Ren;Yao Lu;Cheng Wang;Tianjin Huang;Yunyin Li;Lu Liu","doi":"10.1109/TSUSC.2026.3721146","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3721146","url":null,"abstract":"Accurate wind power forecasting (WPF) is critical to power system planning and operational management, thereby enhancing grid reliability under large-scale wind power integration. However, most existing methods remain confined to a one-dimensional formulation, which inadequately captures turbine-level spatial heterogeneity and consequently limits the joint modeling of positional structure, multivariate interactions, and temporal dynamics. To overcome this limitation, we reformulate WPF as a three-dimensional spatiotemporal forecasting problem by reorganizing each time step into a multi-channel spatial frame according to turbine locations. On this basis, we propose CViTRNN, a multidimensional collaborative learning framework that combines a channel-aware vision transformer with a lightweight recurrent unit to model multivariate interactions and temporal dependencies under turbine-level spatial priors. Furthermore, a two-phase autoregressive workflow is devised to extend forecasting from single-turbine settings to cross-turbine scenarios. Extensive experiments demonstrate the superior accuracy and adaptability of the proposed model, while further confirming the soundness of its design and the interpretability of its forecasting behavior.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 5","pages":"503-515"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131961","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

FBHS: A High-Performance Framework for Real-Time Task Scheduling on big.LITTLE Architectures FBHS：一个高性能的大数据实时任务调度框架。小的架构

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-09-01 Epub Date: 2026-07-22 DOI: 10.1109/TSUSC.2026.3716066

Yayun Liang;Wufei Wu;Jiafeng Wang;Yong Xie

{"title":"FBHS: A High-Performance Framework for Real-Time Task Scheduling on big.LITTLE Architectures","authors":"Yayun Liang;Wufei Wu;Jiafeng Wang;Yong Xie","doi":"10.1109/TSUSC.2026.3716066","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3716066","url":null,"abstract":"Heterogeneous multi-core processors (such as the ARM big.LITTLE architecture) have become the mainstream platform for edge computing and real-time embedded systems due to their dynamic trade-off between high performance and high energy efficiency. Currently, heterogeneous multi-core scheduling faces the challenge of collaboratively optimizing task execution time and system energy consumption. Existing methods have the problem of insufficient heterogeneous adaptability when dealing with tasks with complex dependencies. To address these challenges, this paper proposes an innovative Federated Bundled Hybrid Scheduling (FBHS) framework, which seamlessly combines federated and bundled scheduling through a dynamic selection mechanism guided by capacity enhancement bounds, alongside a differentiated resource allocation strategy tailored for high and low utilization tasks. Experimental results demonstrate that, in task scenarios ranging from 20 to 70 tasks, FBHS outperforms pure federated scheduling (PFS), pure bundled scheduling (PBS), completely fair scheduler (CFS), and evolutionary algorithm based heterogeneous earliest finish time (E-HEFT), achieving average reductions of 18.3%, 26.1%, 31.7%, and 10.10% in energy consumption and 45.8%, 50.95%, 56.2%, and 21.18% in execution time, respectively. Furthermore, the results were verified in real data streams such as Montage and CyberShake, highlighting the effectiveness of FBHS in simultaneously enhancing real-time performance and energy efficiency on heterogeneous multi-core architectures.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 5","pages":"516-529"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133400","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 DMPC-Based Optimal Dispatch Strategy for Multi-Microgrid System Using Accelerated Distributed Augmented Lagrangian Algorithm 基于加速分布式增广拉格朗日算法的多微网系统优化调度策略

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-09-01 Epub Date: 2026-07-20 DOI: 10.1109/TSUSC.2026.3715242

Jun Yang;Weida Yu;Jiahe Pan;Zhaoyang Sun;Shunjiang Wang

引用次数: 0

A Transportation Problem–Based Heuristic for Energy-Efficient Scheduling of Serverless Workflow Applications 基于运输问题的无服务器工作流节能调度启发式算法

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2026-09-01 Epub Date: 2026-08-05 DOI: 10.1109/TSUSC.2026.3720619

Seyyed Heydar Javadi;Saeid Abrishami;Mahmoud Naghibzadeh;Mohsen Amini Salehi;Mohammad Erfan Arasteh

{"title":"A Transportation Problem–Based Heuristic for Energy-Efficient Scheduling of Serverless Workflow Applications","authors":"Seyyed Heydar Javadi;Saeid Abrishami;Mahmoud Naghibzadeh;Mohsen Amini Salehi;Mohammad Erfan Arasteh","doi":"10.1109/TSUSC.2026.3720619","DOIUrl":"https://doi.org/10.1109/TSUSC.2026.3720619","url":null,"abstract":"Serverless computing has emerged as a cloud computing paradigm that mitigates the complexities of resource provisioning and management while charging users only for the resources they consume. However, some inherent characteristics of serverless environments, such as stateless functions and scaling to zero, make it challenging to execute workflow applications that require data transmission between functions. As a result, serverless service providers need to offer efficient workflow execution services to encourage the adoption of these platforms. At the same time, the high energy consumption of serverless cloud data centers has not received sufficient attention from the research community. Accordingly, we introduce Transportation-based Energy-efficient Serverless Scheduling (TESS), an approach designed to address deadline-constrained workflow scheduling in serverless environments while reducing energy consumption. To achieve this, we first formulate the problem as a variation of the well-known Transportation Problem and then propose a scheduling algorithm based on solutions to this problem. Evaluation results demonstrate that the proposed algorithm reduces energy consumption by 10%–31% compared with state-of-the-art algorithms.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"11 5","pages":"489-502"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131043","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