Sustainable Computing-Informatics & Systems最新文献

DEEPCO-RIS: Joint BD-RIS and hybrid NOMA/OMA optimization for energy-efficient vehicular networks DEEPCO-RIS：联合BD-RIS和混合NOMA/OMA优化节能汽车网络

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-06-07 DOI: 10.1016/j.suscom.2025.101145

Nada Alzaben , Nadhem Nemri , Wahida Mansouri , Othman Alrusaini , Mukhtar Ghaleb , Jihen Majdoubi

{"title":"DEEPCO-RIS: Joint BD-RIS and hybrid NOMA/OMA optimization for energy-efficient vehicular networks","authors":"Nada Alzaben , Nadhem Nemri , Wahida Mansouri , Othman Alrusaini , Mukhtar Ghaleb , Jihen Majdoubi","doi":"10.1016/j.suscom.2025.101145","DOIUrl":"10.1016/j.suscom.2025.101145","url":null,"abstract":"<div><div>Next-generation vehicular networks require wireless infrastructures that deliver ultra-reliable, energy-efficient, and low-latency communication under highly dynamic conditions. Traditional RIS-aided and hybrid NOMA/OMA designs face critical limitations, including rigid phase control, high successive interference cancellation (SIC) complexity, and limited adaptability to rapid vehicular mobility. To address these challenges, this paper proposes <strong>DEEPCO-RIS</strong> (Dinkelbach-Enhanced Energy-Efficient Optimization with Beyond-Diagonal RIS), a unified optimization framework that integrates BD-RIS phase configuration, hybrid NOMA/OMA access mode selection, user scheduling, and power allocation. These components are jointly optimized under realistic constraints, including SIC feasibility, power budgets, RIS energy costs, and QoS guarantees. The energy efficiency maximization problem is formulated as a mixed-integer non-convex program and solved using a modular approach combining Dinkelbach’s method, block coordinate descent, successive convex approximation, and manifold-based optimization for BD-RIS tuning. Extensive simulations demonstrate that DEEPCO-RIS achieves up to 22 Mbits/Joule energy efficiency, maintains outage probabilities below 6% even under stringent QoS targets, and exhibits strong robustness against SIC imperfections and network load variations. These results establish DEEPCO-RIS as a scalable and sustainable solution for next-generation vehicular communication networks.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101145"},"PeriodicalIF":3.8,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271255","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

Fully-connected layers-embedded self-attention optimizer based on quantum-inspired and fuzzy logic for smart household energy management 基于量子启发和模糊逻辑的智能家庭能源管理全连接层嵌入式自关注优化器

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-30 DOI: 10.1016/j.suscom.2025.101151

Lulin Zhao , Linfei Yin

{"title":"Fully-connected layers-embedded self-attention optimizer based on quantum-inspired and fuzzy logic for smart household energy management","authors":"Lulin Zhao , Linfei Yin","doi":"10.1016/j.suscom.2025.101151","DOIUrl":"10.1016/j.suscom.2025.101151","url":null,"abstract":"<div><div>On the road to carbon neutrality, the solution to the power consumption optimization problem of thousands of households is an essential link. This work mainly constructs a mathematical model of a smart household energy management system (HEMS) considering the real-time users’ willingness. The work proposes a fully-connected layers-embedded self-attention optimizer (FCSAO) based on quantum and fuzzy logic for the HEMS models. The FCSAO is an optimization method accelerated by fully-connected layers-embedded self-attention networks (FCSANs), quantum-inspired logic, and fuzzy logic. In a conventional optimization algorithm iteration process, a generative adversarial network incorporating a self-attention mechanism is adopted to characterize the input-output relationship of the optimization problem, and a quantum universal gate is employed to train the deep network by dividing the dataset into four classes based on the output of the optimization problem. The trained deep network can accelerate the iterative process of traditional optimization algorithm. The smart HEMS divides the loads in the home into rigid loads, adjustable loads, and air conditioner loads. The smart HEMS model meets the goals of users to save electrical energy and reduce electricity price expenditure by the proposed FCSAO based on quantum-inspired and fuzzy logic. Besides, the smart HEMS model can effectively control the operation state of the air conditioner and give the optimal operation time of adjustable loads. Furthermore, with three different scenarios simulated in MATLAB, the optimized indoor temperature meets users’ willingness for temperature comfort level by the proposed FCSAO based on quantum-inspired and fuzzy logic with great expression capability; the proposed FCSAO saves 1.05 % electricity cost.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101151"},"PeriodicalIF":3.8,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205201","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

Real-time energy-efficient framework for multi-source harvesting and adaptive communication IIoT networks 多源采集和自适应通信工业物联网网络实时节能框架

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-28 DOI: 10.1016/j.suscom.2025.101150

Kumari Priyanka Sinha , Hosam Alden Riyadh , Y.Mohana Roopa , Saiyed Faiayaz waris , Hatem S.A. Hamatta , L. Bhagyalakshmi , Shadab Alam , Ali Algahtani

{"title":"Real-time energy-efficient framework for multi-source harvesting and adaptive communication IIoT networks","authors":"Kumari Priyanka Sinha , Hosam Alden Riyadh , Y.Mohana Roopa , Saiyed Faiayaz waris , Hatem S.A. Hamatta , L. Bhagyalakshmi , Shadab Alam , Ali Algahtani","doi":"10.1016/j.suscom.2025.101150","DOIUrl":"10.1016/j.suscom.2025.101150","url":null,"abstract":"<div><div>Over the past few years, Industrial Internet of Things (IIoT) devices have proliferated across modern manufacturing ecosystems, facilitatingreal-time monitoring, predictive analytics, and autonomous control. Nevertheless, maintaining these devices in low-resource contexts, especiallyin situations where the use of wired power is impossible and battery management is not feasible, is a significant challenge. Here, we introduce a new hybrid system that combines multi-source ambient energy harvesting (vibration, heat, and RF) with a lightweight adaptive communication protocol specifically designed for energy-limited industrial settings. Harnessing real-time energy buffer levels and environmental feedback, the architecture utilizes a central middleware engine to adapt transmission behaviors, routing decisions, and nodeactivity states. We design and test detailed models to measure the performance of the framework on major metrics such as energy usage, communication delay, packet delivery ratio, and network sustainability. We utilize a synthetic industrial case scenario with 45 IoT nodes out-deployed across three zones we show the framework's strong benefits over baseline and partial methods, reaching as high as 40 % improvement in node life, 28 % improvement in sustained throughput, and substantial improvements in node availability and energy fairness. This study provides a basisfor the installation of self-sustained, resilient IoT systems in realistic industrial environments, connecting the state of the art for energy autonomy design with the state of the art in the field of communication intelligence.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101150"},"PeriodicalIF":3.8,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280973","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

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

Energy-efficient blockchain-integrated IoT and AI framework for sustainable urban microclimate management 节能的区块链集成物联网和人工智能框架，用于可持续的城市微气候管理

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-20 DOI: 10.1016/j.suscom.2025.101137

N. Krishnaraj , Hadeel Alsolai , Fahd N. Al-Wesabi , Yahia Said , Ali Alqazzaz , S. Gayathri Priya , S. Shanmathi , B. Narmada

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