Sustainable Computing-Informatics & Systems最新文献

DT-GWO: A hybrid decision tree and GWO-based algorithm for multi-objective task scheduling optimization in cloud computing DT-GWO：一种基于混合决策树和gwo的云计算多目标任务调度优化算法

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-20 DOI: 10.1016/j.suscom.2025.101138

Mohaymen Selselejoo, HamidReza Ahmadifar

{"title":"DT-GWO: A hybrid decision tree and GWO-based algorithm for multi-objective task scheduling optimization in cloud computing","authors":"Mohaymen Selselejoo, HamidReza Ahmadifar","doi":"10.1016/j.suscom.2025.101138","DOIUrl":"10.1016/j.suscom.2025.101138","url":null,"abstract":"<div><div>Cloud computing faces significant challenges in task management, particularly in balancing server loads to prevent both overload and underload conditions while meeting diverse quality of service requirements. The need to manage multiple criteria further increases the complexity of this problem. Additionally, the heterogeneity of cloud resources often complicates efficient task scheduling. To overcome these challenges, this paper introduces a hybrid model that integrates the decision tree approach with the Grey Wolf Optimization (GWO) algorithm for the scheduling of independent tasks. The model aims to optimize makespan, reduce total cost, enhance resource utilization, and maintain load balance. In the proposed approach, tasks are first classified using a decision tree, after which the GWO algorithm allocates resources to the selected tasks. Simulations are conducted using the CloudSim toolkit, in a heterogeneous environment. The experiments consider various input scenarios, ranging from 200 to 3200 tasks. Compared to the standalone GWO algorithm, the proposed DT-GWO hybrid model achieves improvements of at least 18.5 % in makespan, 3.4 % in average resource utilization, and 12.7 % in total cost, all while maintaining load balance.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101138"},"PeriodicalIF":3.8,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106842","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

Retraction notice to “Prediction method of environmental pollution in smart city based on neural network technology” [Sustain. Comput.: Inf. Syst. 36 (2022) 100799] 《基于神经网络技术的智慧城市环境污染预测方法》撤回通知[j]。第一版。[参考文献36 (2022)100799]

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-19 DOI: 10.1016/j.suscom.2025.101129

Xiujuan Jiang , Ping Zhang , Jinchuan Huang

引用次数: 0

Combined optimization strategy for IoT resource allocation with workload prediction 物联网资源分配与工作负荷预测的组合优化策略

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-16 DOI: 10.1016/j.suscom.2025.101136

B. Sasikala , K. Kalaiselvi , V. Senthil Murugan

{"title":"Combined optimization strategy for IoT resource allocation with workload prediction","authors":"B. Sasikala , K. Kalaiselvi , V. Senthil Murugan","doi":"10.1016/j.suscom.2025.101136","DOIUrl":"10.1016/j.suscom.2025.101136","url":null,"abstract":"<div><div>A significant limitation in IoT technology is the challenge of handling the diverse and dynamic nature of IoT workloads, which complicates accurate workload prediction and efficient resource allocation. IoT devices generate vast amounts of heterogeneous data with varying speeds, volumes, and varieties, making traditional methods inadequate for managing this variability and leading to inefficient resource management, suboptimal performance, and increased operational costs. To address these issues, this research proposes a novel hybrid optimization algorithm known as the Lyrebird-Adapted Kookaburra Optimization Algorithm-Improved Analytic Hierarchy Process (LAKO-IAHP) for work load prediction and resource allocation. This approach includes two main phases: the Improved Analytic Hierarchy Process (IAHP) for workload prediction and the LAKO algorithm for resource allocation. The IAHP phase enhances conventional Analytic Hierarchy Process (AHP) techniques by incorporating the Improved k-means clustering (IKMC) process and Euclidean distance calculations to improve the accuracy of workload predictions by considering specific Load Balancing (LB) parameters such as server load and response time. Following this, the LAKO algorithm- an advanced hybrid method combining Kookaburra Optimization Algorithm (KOA) and Lyrebird Optimization Algorithm (LOA)- performs the resource allocation phase, that considers the Quality of Service (QoS) parameters including degree of imbalance, execution time, reliability, and resource utilization. The effectiveness of the LAKO-IAHP approach is demonstrated through various performance metrics and comparisons with existing methods, proving its capability to enhance resource management and maintain high performance and reliability in IoT environments.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101136"},"PeriodicalIF":3.8,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106843","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

Building sustainable federated learning models in Fog-enabled UAV-as-a-Service for aerial image classification 在支持雾的无人机即服务中构建可持续的联邦学习模型，用于航空图像分类

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-15 DOI: 10.1016/j.suscom.2025.101133

Raju Imandi , Arijit Roy , Yong-Guk Kim , Pavan Kumar B.N.

{"title":"Building sustainable federated learning models in Fog-enabled UAV-as-a-Service for aerial image classification","authors":"Raju Imandi , Arijit Roy , Yong-Guk Kim , Pavan Kumar B.N.","doi":"10.1016/j.suscom.2025.101133","DOIUrl":"10.1016/j.suscom.2025.101133","url":null,"abstract":"<div><div>The Fog-enabled UAV-as-a-Service (FU-Serve) platform leverages distributed fog nodes to enable real-time data processing for multiple concurrent applications. However, the computational limitations of these fog nodes significantly hamper the execution of resource-intensive deep learning (DL) algorithms, compromising both operational performance and energy sustainability. To address these challenges, we integrated Federated Learning (FL) within the FU-Serve platform, coupled with the development of three specialized FL-based models tailored for on-device image classification. First, we introduced a sustainable adaptation of MobileNetV2 that synergizes Transfer Learning (TL) with FL principles. This model achieves 97.68% accuracy with an 8.64 MB footprint by distributing pre-trained weights to optimize bandwidth efficiency. To further address resource constraints of fog nodes, we designed FUSERNet—a lightweight DL architecture employing separable convolutions and skip connections, which reduces computational overhead while preserving critical feature representations. This model achieves 97.47% accuracy with an ultra-compact footprint of 237 KB, demonstrating a 98.59% reduction in size compared to state-of-the-art models. Finally, our third model, FusionNet, combines the strengths of MobileNetV2 and FUSERNet to deliver a balanced solution, achieving 97.75% accuracy with moderate resource requirements (8.86 MB). We evaluated our models on the AIDER and NDD disaster response datasets, our models demonstrate superior performance in classifying critical natural disaster scenarios. Notably, FusionNet matches SOTA accuracy levels while reducing memory consumption by 50%, and FUSERNet’s 0.23 MB size enables deployment on even the most resource-constrained UAVs. These contributions enhance the FU-Serve platform’s real-time decision-making capabilities, balancing computational efficiency and mission-critical accuracy for sustainable disaster response.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101133"},"PeriodicalIF":3.8,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089588","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

Towards a data fabric framework for industrial metaverse integration 面向工业元数据集成的数据结构框架

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-14 DOI: 10.1016/j.suscom.2025.101132

Abhishek Kumar , Lauri Lovén , Muhammad Talha Arshad , Susanna Pirttikangas , Sasu Tarkoma

{"title":"Towards a data fabric framework for industrial metaverse integration","authors":"Abhishek Kumar , Lauri Lovén , Muhammad Talha Arshad , Susanna Pirttikangas , Sasu Tarkoma","doi":"10.1016/j.suscom.2025.101132","DOIUrl":"10.1016/j.suscom.2025.101132","url":null,"abstract":"<div><div>The integration of digital and physical assets in industrial settings is increasingly facilitated by the concept of the Industrial Metaverse, a unified platform that leverages advanced technologies like AR/VR/XR to create interconnected 3D environments. As industries within supply chains become more interlinked, the need for seamless integration across these digital environments becomes critical. This paper addresses the challenge of interconnecting private industrial metaverses by proposing a novel data fabric framework that supports diverse modalities, ensures privacy, and adheres to business agreements. We highlight the role of the distributed compute continuum in the framework and demonstrate its practical utility through deployments in Unity and Omniverse across various geographic locations, highlighting the need for AI-based interconnection to optimize real-time analytics and operational scalability. Our experiment shows that even transmitting a 3D patch file, which is considerably smaller than the original base file, across metaverses in different geographic locations requires substantial computational and communication resources, potentially limiting real-time collaboration between metaverses. This observation highlights the importance of AI-based interconnection in enabling a textual metaverse, where instead of transmitting 3D patch objects over the web, only a fine-grained textual description of the patch file is shared. This method allows for more efficient transmission using current networking technology. The textual metaverse is expected to employ an AI-based encoder at the source and an AI-based decoder at the destination to convert the 3D patch into text and subsequently reconstruct it back into the 3D patch.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101132"},"PeriodicalIF":3.8,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139288","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

Distributed data storage using decision tree models and support vector machines in the Internet of Things 物联网中使用决策树模型和支持向量机的分布式数据存储

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-11 DOI: 10.1016/j.suscom.2025.101134

Seyed Payam Fatemi , Nahideh Derakhshanfard , Fahimeh Rashidjafari , Ali Ghaffari

{"title":"Distributed data storage using decision tree models and support vector machines in the Internet of Things","authors":"Seyed Payam Fatemi , Nahideh Derakhshanfard , Fahimeh Rashidjafari , Ali Ghaffari","doi":"10.1016/j.suscom.2025.101134","DOIUrl":"10.1016/j.suscom.2025.101134","url":null,"abstract":"<div><div>The rapid development of IoT technologies generates a considerable amount of diverse and distributed data, mostly real-time and sensitive. Due to the diversity of data types (text, image, video) and geographical dispersion, efficient management becomes essential for maintaining performance and ensuring speedy responses to users. Traditional data storage methods are unfit for dynamic IoT environments, due to their lack of scalability, energy efficiency, and bandwidth. Recent research indicates that machine learning might offer enhanced security with reduced latency and improved energy efficiency. However, most of these techniques are complex and resource-intensive, hence inappropriate for resource-constrained IoT devices. While various developments have been made in this regard, a holistic approach that not only forecasts the requirements for data replication but also selects the most optimized storage nodes remains an unmet challenge. The presented paper offers a hybridized approach by incorporating Decision Trees and SVM, which manage data optimally with higher speeds and reduced computational costs. Simulation results indicate that this method can reduce access latency by up to 22.2–41.6 %, increase accuracy by 5–12.3 %, and improve resource utilization efficiency by 7.7–15.3 %.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101134"},"PeriodicalIF":3.8,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948588","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 parallel computational approach for energy-efficient hydraulic analysis of water distribution networks using learning automata 基于学习自动机的配水管网节能水力分析并行计算方法

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-10 DOI: 10.1016/j.suscom.2025.101135

Ali Suvizi, Ruhollah Ahmadi, Morteza Saheb Zamani, Mohammad Reza Meybodi

{"title":"A parallel computational approach for energy-efficient hydraulic analysis of water distribution networks using learning automata","authors":"Ali Suvizi, Ruhollah Ahmadi, Morteza Saheb Zamani, Mohammad Reza Meybodi","doi":"10.1016/j.suscom.2025.101135","DOIUrl":"10.1016/j.suscom.2025.101135","url":null,"abstract":"<div><div>The hydraulic analysis of water distribution networks (WDNs) is crucial for ensuring efficient management of water resources, a key aspect of sustainable urban development. Formulation and steady-state hydraulic analysis of these networks have been conducted using both numerical and non-numerical methods. WDN hydraulic equations are complex and non-linear, requiring multiple executions, making their hydraulic analysis computationally demanding and energy intensive. This paper introduces an energy-efficient parallel computing approach using learning automata to significantly enhance the speed and energy efficiency of hydraulic analysis. By employing a cellular automaton framework that reflects the WDN structure, and a solution methodology based on the Taylor series enhanced with learning automata, we propose a system that reduces computational time and energy consumption. We compare the performance of our proposed approach with the EPANET software across networks of varying complexity and topologies. The results suggest our parallel algorithm not only accelerate the hydraulic analysis process up to 60 times compared to existing methods, but also significantly decrease the energy consumption, highlighting its potential for sustainable water management practices.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101135"},"PeriodicalIF":3.8,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071617","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 transfer learning for water consumption forecasting

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-07 DOI: 10.1016/j.suscom.2025.101130

A. Gil-Gamboa, J.F. Torres, F. Martínez-Álvarez, A. Troncoso

{"title":"Energy-efficient transfer learning for water consumption forecasting","authors":"A. Gil-Gamboa, J.F. Torres, F. Martínez-Álvarez, A. Troncoso","doi":"10.1016/j.suscom.2025.101130","DOIUrl":"10.1016/j.suscom.2025.101130","url":null,"abstract":"<div><div>Artificial intelligence is expanding at an unprecedented rate due to the numerous advantages it provides to all types of businesses and industries. Water utilities are adopting artificial intelligence models to optimize water management in cities nowadays. However, the substantial computational demands of artificial intelligence present challenges, particularly regarding energy consumption and environmental impact. This paper addresses this problem by proposing a transfer learning approach for water consumption forecasting that reduces computational time, energy usage, and CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions. The proposed methodology consists in developing a transfer learning approach based on a deep learning model already trained for a task with similar characteristics such as predicting electricity consumption. Thus, a pre-trained deep learning model designed for electricity consumption prediction is adapted to the water consumption domain, leveraging shared characteristics between these tasks. Experiments are conducted to determine the optimal amount of knowledge transfer and compare the performance of this approach with other state-of-the-art time-series forecasting models. Using real data from a water company in Spain, the transfer learning model achieves a similar or better accuracy than the other methods, while demonstrating significantly lower computational times, energy consumption and CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions. In addition, a scalability analysis has been conducted leading to the conclusion that the proposed transfer learning model is highly suitable to deal with big data. These findings highlight the potential of transfer learning as a sustainable and scalable solution for big data challenges in water management systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101130"},"PeriodicalIF":3.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943214","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

Deep learning BiLSTM and Branch-and-Bound based multi-objective virtual machine allocation and migration with profit, energy, and SLA constraints 基于利润、能量和SLA约束的深度学习BiLSTM和Branch-and-Bound多目标虚拟机分配和迁移

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-05 DOI: 10.1016/j.suscom.2025.101128

Neeraj Kumar Sharma , Sriramulu Bojjagani , Ravi Uyyala , Anup Kumar Maurya , Saru Kumari

{"title":"Deep learning BiLSTM and Branch-and-Bound based multi-objective virtual machine allocation and migration with profit, energy, and SLA constraints","authors":"Neeraj Kumar Sharma , Sriramulu Bojjagani , Ravi Uyyala , Anup Kumar Maurya , Saru Kumari","doi":"10.1016/j.suscom.2025.101128","DOIUrl":"10.1016/j.suscom.2025.101128","url":null,"abstract":"<div><div>This paper highlights a novel approach to address multiple networking-based VM allocation and migration objectives at the cloud data center. The proposed approach in this paper is structured into three distinct phases: firstly, we employ a Bi-Directional Long Short Term Memory (BiLSTM) model to predict Virtual Machines (VMs) instance’s prices. Subsequently, we formulate the problem of allocating VMs to Physical Machines (PMs) and switches in a network-aware cloud data center environment as a multi-objective optimization task, employing Linear Programming (LP) techniques. For optimal allocation of VMs, we leverage the Branch-and-Bound (BaB) technique. In the third phase, we implement a VM migration strategy sensitive to SLA requirements and energy consumption considerations. The results, conducted using the CloudSim simulator, demonstrate the efficacy of our approach, showcasing a substantial 35% reduction in energy consumption, a remarkable decrease in SLA violations, and a notable 18% increase in the cloud data center’s profit. Finally, the proposed multi-objective approach reduces energy consumption and SLA violation and makes the data center sustainable.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101128"},"PeriodicalIF":3.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923424","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

Corrigendum to “An energy efficient and secure model using chaotic levy flight deep Q-learning in healthcare system” [Sustain. Comput.: Inform. Syst. 39 (2023) 100894] “在医疗保健系统中使用混沌征费飞行深度q学习的节能和安全模型”的更正[可持续]。第一版。:通知。系统39 (2023)100894]

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-04-28 DOI: 10.1016/j.suscom.2025.101131

V. Gowri , B. Baranidharan

引用次数: 0