Sustainable Computing-Informatics & Systems最新文献

{"title":"Optimizing regional energy systems with concentrated solar power for enhanced efficiency, sustainability, and cost-effective energy management","authors":"Songzhi Zhang,&nbsp;Peng Sun","doi":"10.1016/j.suscom.2025.101205","DOIUrl":"10.1016/j.suscom.2025.101205","url":null,"abstract":"<div><div>The current study optimizes a regional integrated energy system that combines concentrated solar power, wind turbines, energy storage, and thermal components to enhance energy efficiency, reduce costs, and minimize environmental impact. The primary objectives were to reduce operational expenses, address environmental concerns, and ensure a reliable electricity supply through integrated load response mechanisms. Fuzzy probability-constrained programming was used to model the uncertainty of renewable energy output, and a modified gravitational search algorithm (MGSA) was employed for optimization. Two different approaches to energy demand response were studied: one using electric boilers with a fixed thermoelectric power ratio, and another employing a flexible system for cooling, heating, and power that could adjust as needed. The implementation of the load response program resulted in a 0.75 % increase in the electrical peak-valley difference and a 0.51 % increase in the thermal peak-valley difference, indicating slight shifts in demand distribution. Additionally, valley values decreased by 0.37 % for electrical loads and by 2.71 % for thermal loads, suggesting modest improvements in off-peak load utilization. These changes demonstrate the program's potential to reshape load profiles; however, significant peak reduction will require further enhancement.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101205"},"PeriodicalIF":5.7,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118049","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}

{"title":"Nonlinear energy modeling for UAVs in critical missions using multiplicative calculus","authors":"Özlem Sabuncu ,&nbsp;Bülent Bilgehan","doi":"10.1016/j.suscom.2025.101206","DOIUrl":"10.1016/j.suscom.2025.101206","url":null,"abstract":"<div><div>Energy efficiency in Unmanned Aerial Vehicles (UAVs) is crucial for operations, where effective payload delivery, stabilization, and communication are essential. This study presents a nonlinear energy consumption model tailored for UAVs, built upon exponential scaling and multiplicative calculus to reflect the interdependencies among payload weight, wind speed, altitude, velocity and communication power. Unlike conventional approaches that rely on linear or polynomial formulations, the proposed method incorporates energy demands from integrated systems, focusing on energy consumption. The proposed multiplicative model provides valuable insights into the energy trade-offs influenced by changing environmental and operational conditions. It improves the practicality of using UAVs for real-time aid delivery, resource allocation, and communication in challenging, resource-constrained environments, offering better accuracy than traditional energy consumption models. Validation using experimental datasets demonstrates that the proposed model achieves an 85 % improvement in accuracy compared to the recently established cubic polynomial model for predicting energy consumption. The effectiveness of the proposed multiplicative model was evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) as performance metrics. The basic polynomial model recorded an MSE of 57.4269, while the parametric polynomial model significantly improved this to 5.7794. In comparison, the multiplicative model demonstrated superior accuracy, achieving a markedly lower MSE of 0.8472. Consistently, the multiplicative model also outperformed the others in terms of RMSE, attaining the lowest value of 0.9205, thereby confirming its robustness and predictive reliability. The Mean Absolute Error (MAE) was reduced from 6.44 to 0.73, representing an 88.66 % improvement. Furthermore, the R² value increased from 0.95 to 0.99, indicating a stronger fit between the predicted and actual data. These results underscore the multiplicative model's robustness, accuracy, and reliability, demonstrating its strong potential for real-world predictive applications. The findings demonstrate that the proposed model more accurately represents energy consumption, providing a robust foundation for precise analysis and design.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101206"},"PeriodicalIF":5.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060496","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}

{"title":"Efficient QCA‐Based Circuits for Low‐Power Medical IoT System","authors":"D. Ajitha ,&nbsp;Muhammad Zohaib ,&nbsp;Firdous Ahmad ,&nbsp;Khalid Zaman ,&nbsp;S.M. Prabin","doi":"10.1016/j.suscom.2025.101203","DOIUrl":"10.1016/j.suscom.2025.101203","url":null,"abstract":"<div><div>The Internet of Things (IoT) plays a vital role in the recent healthcare industry by providing precise diagnostic and treatment capabilities. There is a growing interest in medical IoT incorporated into healthcare systems. The processing unit of all medical IoT comprises complementary metal-oxide semiconductor (CMOS) technology. However, CMOS Medical IoT technology has become integrated into biomedical hardware systems at the nanoscale regime. Due to regulatory, ethical, and technological challenges, including slow processing speeds, high power consumption, and slow switching frequencies, particularly in the gigahertz (GHz) range. On the other hand, compared to traditional computers, quantum technology will accelerate processing by an order of magnitude and affect all artificial and medical (AI) and medical IoT processing applications. Quantum-dot cellular automata (QCA) present a promising alternative digital hardware system in medical IoT. QCA technology makes an optimal choice for establishing circuit design frameworks for AI in medical IoT applications, where low-cost, real-time, energy-efficient performance is crucial. Moreever, encryption and decryption circuits have been used in medical IoT operations to protect sensitive patient data while it is being transmitted and stored. The essential arithmetic and logic unit (ALU) is proposed in this context, which is the foundation for processing and computational units for medical IoT systems at the nanoscale devices. A systematic approach is involved in integrating adders, multiplexers, an ALU, and a logic unit to enhance processor drive and privacy via encryption and decryption in medical IoT. The experimental outcomes reveal that the recommended design overtakes the previous design by 15.48 % in terms of cells and 16.07 % in terms of area. The designs are accurately simulated using the QCADesigner-E 2.0.3 software tool.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101203"},"PeriodicalIF":5.7,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060099","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}

{"title":"Sustainable transient frequency management in eco-industrial park microgrids considering e-shared mobility storage using efficient fractional-order computing","authors":"SeyedJalal SeyedShenava,&nbsp;Peyman Zare,&nbsp;Amir Mohammadian","doi":"10.1016/j.suscom.2025.101197","DOIUrl":"10.1016/j.suscom.2025.101197","url":null,"abstract":"<div><div>The evolving architecture of rich-renewable Eco-Industrial Park Microgrids (EIP-MGs) introduces significant frequency stability challenges due to the intermittent nature and low inertia of integrated renewable energy sources. To address these limitations, advanced energy storage systems, comprising fixed and mobile electric energy storage systems, have been adopted. Among them, mobile EV energy storage, particularly in the context of e-shared mobility, offers a flexible and scalable solution for load frequency control in modern EIP-MGs. This study presents a novel framework for sustainable transient frequency management using a fractional-order computing-based hybrid cascade controller, TFOID–3DOF–TID (Tilted Fractional-Order Integral and Derivative with Three Degrees of Freedom), optimized via the Crested Porcupine Optimizer (CPO). The proposed control scheme is validated through six case studies under three industrial load disturbance scenarios, with emphasis on transient stability and real-world uncertainties. The evaluations are structured around frequency-domain design criteria based on integral error metrics, including squared and absolute formulationsaimed at analyzing efficiency, sensitivity, adaptability, robustness, stability, and computational burden. The proposed control scheme, featuring the TFOID and 3DOF-TID controllers, is evaluated in comparison with validated metaheuristic-based algorithms. Simulation results demonstrate that the CPO-based TFOID–3DOF–TID controller consistently outperforms other schemes, with improvements including a 22 %–48 % reduction in settling time, a 25 %–55 % decrease in undershoot, and a 30 %–60 % reduction in overshoot across varying scenarios. Additionally, Bode plot evaluations confirm superior phase margins and damping characteristics, while robustness margins improve by up to 60 %, affirming the controller’s resilience under non-ideal operational conditions. These findings provide practical insights for policymakers and engineers aiming to enhance the resilience and sustainability of future-ready industrial microgrids.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101197"},"PeriodicalIF":5.7,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004814","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}

{"title":"Task scheduling in cloud computing system by improved honey badger optimization algorithm with two dimensional and three dimensional fractals","authors":"Yu-Feng Sun,&nbsp;Si-Wen Zhang,&nbsp;Jie-Sheng Wang,&nbsp;Shi-Hui Zhang,&nbsp;Yu-Cai Wang,&nbsp;Xiao-Fei Sui","doi":"10.1016/j.suscom.2025.101201","DOIUrl":"10.1016/j.suscom.2025.101201","url":null,"abstract":"<div><div>Cloud computing task scheduling is not only the foundation for ensuring the efficient operation of the cloud platform, but also an important means of improving service quality and reducing costs. With the continuous development of cloud computing technology, the requirements for intelligent and automated task scheduling are also increasing. To address the demand for more efficient and flexible computations, an enhanced honey badger algorithm (HBA) utilizing two dimensional and three dimensional fractals is introduced. The digging phase of the honey badger's foraging strategy is improved by using the mathematical expressions of two dimensional and three dimensional fractals in rectangular and polar coordinates, which enhances the algorithm's performance while speeding up its convergence. The optimal solution HBACBKS-Z was selected by verification on the benchmark functions. The optimization problem of task scheduling in cloud computing systems is divided into large-scale task scheduling and small-scale task scheduling. Experiments were conducted in these two cases by using HBACBKS-Z and other traditional swarm intelligence optimization algorithms. It has been proved that HBACBKS-Z has significant advantages in terms of total cost, time cost, load cost and price cost, and can effectively solve the task scheduling optimization problem of cloud computing systems of various sizes.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101201"},"PeriodicalIF":5.7,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004812","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}

{"title":"Power management for smart grids integrating renewable energy sources using Greylag goose optimization and anti-interference dynamic integral neural network","authors":"G.K. Jabash Samuel ,&nbsp;P. Rajendran ,&nbsp;Papana Venkata Prasad ,&nbsp;Chinthalacheruvu Venkata Krishna Reddy","doi":"10.1016/j.suscom.2025.101199","DOIUrl":"10.1016/j.suscom.2025.101199","url":null,"abstract":"<div><div>This paper proposes a hybrid power management strategy for smart grids (SGs) that integrates renewable energy sources (RESs), such as battery energy storage systems (BESS), fuel cells (FCs), wind turbines (WT), and solar photovoltaic (PV). The GGO-AIDINN approach integrates Greylag Goose Optimization (GGO) and an Anti-Interference Dynamic Integral Neural Network (AIDINN) to address high emissions during low renewable energy (RE) availability and rising operational costs from advanced infrastructure. The GGO optimizes resource allocation and energy distribution, maximizing the use of available RE. Meanwhile, AIDINN predicts energy consumption patterns based on weather conditions, improving overall system performance. The proposed GGO-AIDINN model is implemented on MATLAB and evaluated against several existing methods, including Fuzzy Logic Control (FLC), Non-dominated Sorting Genetic Algorithm (NSGA-II), and others. Results show the hybrid method achieves significant improvements, with an operational cost of $1328 per MW, emissions of 13.76 kg per MW, and an efficiency of 98.7 %. These outcomes demonstrate that GGO-AIDINN outperforms traditional techniques, offering lower costs, reduced emissions, and enhanced system efficiency. This makes it a superior solution for sustainable power management in SGs incorporating RESs and BESS.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101199"},"PeriodicalIF":5.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048493","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}

{"title":"Integrating IoT and fuzzy logic for intelligent irrigation in sustainable agriculture for improving water scarcity: Benefits and challenges","authors":"Abdennabi Morchid ,&nbsp;Ishaq G. Muhammad Alblushi ,&nbsp;Haris M. Khalid ,&nbsp;Hassan Qjidaa ,&nbsp;Rachid El Alami","doi":"10.1016/j.suscom.2025.101191","DOIUrl":"10.1016/j.suscom.2025.101191","url":null,"abstract":"<div><div>Modern agriculture faces significant challenges related to water scarcity and the impacts of climate change. To ensure crop sustainability and food security, irrigation systems must be optimized. Fuzzy logic and the Internet of Things (IoT) are two cutting-edge approaches to intelligent irrigation management that adjust water delivery to plants' real needs. Conventional irrigation techniques are wasteful and ineffective. Fuzzy logic and the IoT have exciting opportunities, but integrating them presents difficulties, especially (1) concerning implementation, (2) cost, and (3) data security. In light of water shortage, food security, and sustainable development issues, this proposed article examines how IoT and fuzzy logic might be used to create smart irrigation systems. It evaluates contemporary methods for optimizing water management using fuzzy logic and the IoT, as well as the effects of climate change on irrigation. While addressing the challenges of installation costs, implementation complexity, communication reliability, and data security, the proposed review highlights the benefits of these technologies, including reduced water consumption, increased agricultural yields, automation, and environmental adaptability. The main topics of this review's final section, including the integration of new, cutting-edge technology, enhanced decision-making models, and the adoption of sustainable solutions for more resilient and effective agriculture, also address potential directions for future research. importance of the research. Due to water constraints and climate change, this study highlights the importance of intelligent irrigation systems. It showcases creative methods to maximize water management and raise agricultural productivity by fusing IoT with fuzzy logic.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101191"},"PeriodicalIF":5.7,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926276","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}

{"title":"Flexibility regulation-based economic energy scheduling in multi-microgrids with renewable/non-renewable resource and stationary storage systems considering sustainable computing by hybrid metaheuristic algorithm","authors":"Ahad Faraji Naghibi ,&nbsp;Ehsan Akbari ,&nbsp;Saeid Shahmoradi ,&nbsp;Mehdi Veisi ,&nbsp;Sasan Pirouzi","doi":"10.1016/j.suscom.2025.101196","DOIUrl":"10.1016/j.suscom.2025.101196","url":null,"abstract":"<div><div>This plan presents energy scheduling in a distribution grid with multi-microgrid according to estimation of environmental, economic, flexibility, operation, and security indicators in microgrids. Microgrid has a multi-bus structure, which includes renewable solar, wind and bio-waste devices, non-renewable resources, compressed air and hydrogen storage. Study contains the three objectives optimization. The objective functions are the minimization of operation cost of microgrids and resources, the environmental pollution of microgrids and voltage deviation function. The constraints of the problem include the optimal power flow formulation of microgrids based on the flexibility and voltage security limits, the performance model of renewable/non-renewable units, and storage devices. Study has parameters of price of energy, load, and renewable phenomena as uncertainty. For their modeling, the point estimation approach is used to according to low computational time and accurately model flexibility. The ε-constraint method is used to extract the single-objective model, and fuzzy decision-making technique is used to achieve the compromise solution. This scheme has a non-convex nonlinear formulation. To access a reliable response considering low deviation for last point, a combination of red panda optimization and ant-lion optimization is used. Funding indicate the ability of plan for improve the technical, environmental, and economic conditions of microgrids. Thus, energy scheduling of the aforementioned units and storages can improve operational, economic, environmental, and voltage stability conditions of microgrids by about 59.2 %, 44.2 %, 24.5 %-75 % and 17.3 %-27.4 %, respectively. In these conditions, study achieves 100 % flexibility for microgrids. Solution approach achieves the sustainable computing conditions, such that it has the most optimal solution at low computational time and a standard deviation of 0.97 % in the final response.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101196"},"PeriodicalIF":5.7,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921922","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}

{"title":"Memetic salp swarm algorithm optimized control for operational resilience in grid-tied microgrid","authors":"Ravita Saraswat,&nbsp;Sathans Suhag","doi":"10.1016/j.suscom.2025.101195","DOIUrl":"10.1016/j.suscom.2025.101195","url":null,"abstract":"<div><div>To ensure reliable &amp; resilient operation of a microgrid, efficient voltage and power regulation strategies have to be in place. The instant study proposes the memetic salp swarm algorithm (MSSA) tuned fractional order proportional-integral-derivative (FOPID) control strategy towards improving operational resilience of the grid-connected microgrid, comprising solar panels, wind turbine, battery bank, and AC load, in the backdrop of solar, wind, and load uncertainties besides the eventuality of grid isolation. MATLAB® simulation results, both qualitative and quantitative, ideate effectiveness of recommended control strategy whose novelty lies in synergetic use of MSSA and FOPID, with the tuning competency of MSSA established against grey wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101195"},"PeriodicalIF":5.7,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903445","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}

{"title":"Research on optimization of distributed network security framework based on blockchain under green computing framework","authors":"Ling Liu,&nbsp;Jianbo Xu,&nbsp;Junwen Fang,&nbsp;Guoli Sun","doi":"10.1016/j.suscom.2025.101183","DOIUrl":"10.1016/j.suscom.2025.101183","url":null,"abstract":"<div><div>In the current fast-changing digital world, distributed networks are under severe threat in terms of security and efficiency. Their decentralized nature and expanding amount of data raise system complexity and vulnerability. At the same time, sustainable computing demands energy-efficient solutions for network operations. This research proposes a Distributed Network Security Framework Based on Blockchain within a Green Computing Framework. It introduces a Dynamic Whale Optimized Adjustable Graph Neural Network (DWO-AGNN) to assess network security. The model leverages blockchain’s decentralized and tamper-proof features, using smart contracts to enhance resilience against cyberattacks. The framework also focuses on reducing the energy footprint of security operations. Key performance metrics include security effectiveness, energy consumption, and throughput. Results show strong performance: availability at 99.0 %, integrity at 96.8 %, and confidentiality at 95.2 %. The system achieves 95.7 Megabits per Second (Mbps) throughput, reduces energy usage from 1.20 to 0.85, and cuts energy costs from $500 to $375. This research demonstrates that blockchain-based models can deliver high security while supporting environmentally responsible computing. The DWO-AGNN offers a practical solution for resilient, energy-efficient distributed networks.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101183"},"PeriodicalIF":5.7,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893610","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}