Sustainable Computing-Informatics & Systems最新文献

{"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":"2025-12-01","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}

{"title":"Innovative IoT and blockchain integration for real-time urban air quality monitoring and autonomous response system","authors":"Eatedal Alabdulkreem ,&nbsp;Randa Allafi ,&nbsp;Munya A. Arasi ,&nbsp;P. Geetha ,&nbsp;Faisal Mohammed Nafie ,&nbsp;A.Sumaiya Begum ,&nbsp;G. Nallasivan ,&nbsp;S. Vivek","doi":"10.1016/j.suscom.2025.101250","DOIUrl":"10.1016/j.suscom.2025.101250","url":null,"abstract":"<div><div>Urban air pollution remains one of the most pressing public health challenges, intensified by the rapid pace of urbanization and industrial development in modern cities. This research introduces a novel model that integrates the Internet of Things (IoT), blockchain, and edge computing to create a secure, real-time, and scalable air quality monitoring system tailored for urban environments. The core objective is to design a decentralized framework that ensures data integrity, minimizes latency, and automates responses to pollution events. Blockchain technology plays a crucial role by providing a transparent and tamper-proof ledger that preserves the historical record of air quality data while safeguarding its authenticity. Additionally, smart contracts embedded within the blockchain enable automated alerts whenever pollution levels exceed predefined safety thresholds allowing the system to respond instantly without human intervention. Through our experimental testing, we found our model provided an average data accuracy rating of more than 95 % and a data completeness level of more than 98 % with an input latency of less than 500 ms, and a power efficiency greater than 90 %, thus providing us with a more responsive and efficient system than existing cloud-based detection solutions. This research would provide an improved method to optimize the surveillance of urban environmental conditions, and assist with advancing additional public health protection confidently due to the scalable process featuring a customized model for a range of urban scenarios.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101250"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568406","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 machine learning-based routing in 5G-VANETs for reducing power consumption in real-time communications","authors":"M. John Peter ,&nbsp;R. Manoharan","doi":"10.1016/j.suscom.2025.101262","DOIUrl":"10.1016/j.suscom.2025.101262","url":null,"abstract":"<div><div>Intelligent Transportation Systems (ITSs) are a critical application of Fifth-Generation (5 G) mobile communication technology, with Vehicular Ad Hoc Networks (VANETs) serving as a fundamental component. Although 5 G infrastructure significantly enhances connectivity, challenges persist in scenarios with limited coverage or high vehicle mobility, where Device-To-Device (D2D) communication becomes essential. VANETs further encounter unstable connectivity, rapidly changing topologies, and uneven vehicle distribution, which lead to frequent route rediscovery, excessive signaling overhead, and increased power consumption. To address these limitations, a sustainable machine learning (ML)-based routing protocol is developed that integrates 5 G and D2D communication for improved reliability and energy efficiency. This research utilizes a 5G-enabled VANET simulation environment to collect mobility, communication, and energy-related data for routing optimization. The dataset undergoes cleaning, standardization, and outlier detection to ensure reliability, while Wavelet Transform and PCA are applied for dimensionality reduction and pattern extraction. The Golden Jackal Optimization (GJO) algorithm is used for feature selection and parameter tuning, optimizing routing decisions and evaluating network connectivity through a nonhomogeneous Poisson process. Routing optimization is achieved using a novel ML model, the Extreme Kernelized Gradient Supported Machine (EKGSM), which exploits kernelized gradient learning to capture nonlinear mobility, connectivity, and energy-related patterns. The proposed model achieves an increase in packet delivery ratio (PDR), a reduction in average end-to-end (E2E) delay, a decrease in energy consumption, and a reduction in routing overhead. These outcomes establish EKGSM as an effective, scalable, and sustainable routing solution for next-generation 5G-VANET environments.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101262"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614636","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":"Dynamic task scheduling in cloud environments using improved K-means and DiffQ Neural Evolution approach","authors":"Divya R,&nbsp;Swapnil M. Parikh","doi":"10.1016/j.suscom.2025.101257","DOIUrl":"10.1016/j.suscom.2025.101257","url":null,"abstract":"<div><div>Dynamic task scheduling in cloud computing for optimizing resource utilization and minimizing execution time, particularly in environments with fluctuating workloads and diverse application requirements. Conventional algorithms often struggle with scalability, computational complexity and slow convergence rates, leading to inefficiencies in resource management and increased costs. To address these challenges, this work proposes the Improved K-means EvoQ Framework which integrates enhanced k-means clustering with DiffQ Neural Evolution approach. This framework employs a global resource manager to monitor and optimize resource allocation while leveraging Differential Evolution (DE) and Deep Q-Learning (DQL) for dynamic policy optimization. The hybrid approach adapts to workload changes which enhances scalability and improves task scheduling efficiency by minimizing response time, waiting time and makespan while maximizing resource utilization. Comprehensive evaluation demonstrates the framework’s effectiveness across metrics such as Makespan, computation time, success rate and resource utilization making it a robust solution for dynamic task scheduling in cloud environments. Thus, this proposed framework provides a scalable, efficient and intelligent task scheduling solution paving the way for enhanced performance in modern cloud computing environments.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101257"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614644","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":"SDN-Based NFV deployment for multi-objective resource allocation in edge computing: A deep reinforcement learning for iot workload scheduling","authors":"Mehdi Hosseinzadeh ,&nbsp;Amir Haider ,&nbsp;Amir Masoud Rahmani ,&nbsp;Farhad Soleimanian Gharehchopogh ,&nbsp;Shakiba Rajabi ,&nbsp;Parisa Khoshvaght ,&nbsp;Thantrira Porntaveetus ,&nbsp;Sang-Woong Lee","doi":"10.1016/j.suscom.2025.101218","DOIUrl":"10.1016/j.suscom.2025.101218","url":null,"abstract":"<div><div>The rapid growth of Internet of Things (IoT) devices presents significant challenges, particularly regarding resource management in real-time data processing environments. Traditional cloud computing struggles with high delay times and limited bandwidth, affecting user interaction and cognitive load. Edge computing mitigates these issues by decentralizing data processing and bringing resources closer to IoT devices, ultimately influencing human-computer interaction. This paper introduces a framework for resource allocation in edge computing environments, leveraging Software-Defined Networking (SDN) and Network Function Virtualization (NFV) alongside Deep Q-Network (DQN) optimization. The framework aims to enhance user experiences by improving CPU, memory, and storage efficiency while reducing network delays, contributing to a smoother and more efficient interaction with IoT systems. Simulated results demonstrate a 40 % improvement in CPU utilization, 30 % in memory, and 20 % in storage efficiency, which can positively impact IoT devices' perceived effectiveness and usability.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101218"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220312","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":"An intelligent framework for energy optimization in IoT networks using LSTM and multi-criteria decision making","authors":"Nahideh Derakhshanfard ,&nbsp;Hossein Heydari ,&nbsp;Abbas Mirzaei ,&nbsp;Ali Asghar Pour Haji Kazem","doi":"10.1016/j.suscom.2025.101246","DOIUrl":"10.1016/j.suscom.2025.101246","url":null,"abstract":"<div><div>Intelligent agriculture, digital health, or smart cities are only a few out of multiple uses of the Internet of Things. The limited energy supply of the IoT nodes, specifically battery-run sensor nodes, prevents them from maintaining consistent work and hinders the network’s functioning. In this regard, a smart framework that utilizes the progressive machine learning models with multi-criteria decision-making should ensure higher energy efficiency in the IoT networks. While the existing researches have attempted to decrease energy levels in the IoT networks, most of them apply primitive concepts: clustering, routing, and node sleep, and do not use the most efficient machine learning algorithms for energy prediction. Indeed, several people have tried using machine learning algorithms, like decision trees, linear regression, and elementary ANN for energy prediction. However, most of these algorithms are efficient if they considered as individual ones, and people almost never combine energy prediction and node priority. As a result, we propose a complex system of several designed operations that result in increased energy efficiency. First, the data on energy consumption are gathered at regular intervals and preprocessed: normalized, denoised, and empty value-imputed. Then the LSTM model is used to find temporal patterns and predict the future changes. After node ranking, various dynamic strategies like routing, some of the nodes put to sleep, and traffic are optimized. As a result, the lifetime of the network increases by 35 % whereas the energy consumption decreases by 23 %.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101246"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465671","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 grid-connected PV system with MDNSOGI-controlled qZSI-DSTATCOM for enhanced power quality","authors":"R. Mahadevan ,&nbsp;P. Karpagavalli","doi":"10.1016/j.suscom.2025.101228","DOIUrl":"10.1016/j.suscom.2025.101228","url":null,"abstract":"<div><div>This study presents a novel control strategy that is based on a multilayer discrete noise-eliminating second-order generalized integrator (MDNSOGI). The objective of this technique is to make the power quality in smart grids more efficient by regulating a quasi-impedance source inverter (qZSI), which is coupled to a photovoltaic (PV) system and a Distribution Static Compensator (DSTATCOM). An imbalanced and distorted voltage situation, harmonic pollution, and system stability under nonlinear and variable load scenarios are some of the difficulties that are addressed by the system. For the purpose of achieving exact compensation, the suggested control strategy makes use of the MDNSOGI algorithm, which is capable of successfully extracting basic voltage components while simultaneously rejecting noise. Simulation findings in MATLAB/Simulink across a variety of case studies reveal a total harmonic distortion (THD) in grid currents that is less than 1.2 %, a decrease in voltage imbalance to less than 2 %, and an improvement in voltage stability. The system performs better than traditional approaches, such as the synchronous reference frame (SRF) and the traditional second-order generalized integrator (SOGI). This is shown by looking at other metrics like the voltage balancing index and how well it holds up under voltage sags. Through the elimination of derivative terms, this technique also helps to minimize the complexity of computing processes, which in turn supports energy-efficient and responsive power management. In light of these findings, the potential of the methodology that was introduced for the delivery of power in sophisticated grids that are coupled with sustainable electrical sources in a manner that is dependable, environmentally friendly, and of high quality has been brought to light.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101228"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266642","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":"Security-aware optimization of PoW-based blockchain performance using a genetic algorithm approach","authors":"Arman Gheysari,&nbsp;Hamid R. Zarandi","doi":"10.1016/j.suscom.2025.101232","DOIUrl":"10.1016/j.suscom.2025.101232","url":null,"abstract":"<div><div>Proof of Work (PoW) Blockchain networks face significant challenges in balancing security and performance. Various attacks, such as selfish mining and Eclipse attacks, pose serious threats to the sustainability of these networks. This paper presents an optimization method of configuring consensus algorithm and network parameters using a Genetic Algorithm (GA). Our goal is to enhance performance while maintaining security. We specifically target key parameters for optimization: block size, block interval, and block propagation mechanism. The goal is to minimize both median block propagation time and stale block rate, and it preserves the attack resilience of PoW blockchain networks. The presented work provides a systematic approach for configuring network of PoW blockchain parameters. It offers a solution to enhance performance without compromising security or increasing vulnerability to common attack vectors. To identify practical configurations, we employ a simulation-based method within a given network simulation environment. The approach is generally quite iterative, with GA selecting the best-performing solutions based on their fitness regarding propagation delays and attack vulnerabilities. As a result, the method achieves an overall enhancement in the performance of PoW blockchain networks without increasing security concerns.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101232"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320428","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":"Energy-efficient routing and predictive sink mobility in mobile wireless sensor networks using reflection equivariant quantum neural network and star fish optimization algorithms","authors":"K. Manojkumar ,&nbsp;Alok Singh Sengar ,&nbsp;A.P. Jyothi ,&nbsp;Syed Mohd Faisal","doi":"10.1016/j.suscom.2025.101231","DOIUrl":"10.1016/j.suscom.2025.101231","url":null,"abstract":"<div><div>Independent sensor nodes that collect environmental data for various uses make up Wireless Sensor Networks (WSNs). By combining clustering, optimized routing, and sink mobility, the Reflection Equivariant Quantum Neural Network using Star Fish Optimization Algorithm (REQNN-SFOA) framework improves performance and reduces energy consumption in WSNs. WSNs face two significant challenges: limited energy resources and frequent topology changes caused by mobile sinks. These issues disrupt routing and significantly reduce network longevity. Conventional protocols struggle with higher energy consumption and increased packet loss. To counteract these issues, Energy-Efficient Routing and Predictive Sink Mobility (EERPSM) is proposed for WSN. The framework first clusters sensor nodes using the Newton-Raphson-based Optimizer (NRBO). Then, the Addax Optimization Algorithm (AOA) selects the cluster heads, and the Billiards Inspired Optimization Algorithm (BIOA) determines the shortest, least energy-consuming path to the sink. Sink mobility is predicted based on a Reflection Equivariant Quantum Neural Network (REQNN). The Starfish Optimization Algorithm (SOA) is used to optimize the weight parameter. Simulation results indicate that the proposed framework achieves a reliability of more than 99.9 % and an efficiency of 99.78 %. These improvements enhance data delivery, reduce energy consumption, and extend network lifetime. The proposed approach effectively addresses clustering, optimized routing, and predictive mobility handling, resulting in a robust solution for instantaneous and energy-efficient communication in mobile WSNs.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101231"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362781","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":"A two-stage spatio-temporal flexibility-based energy optimization of internet data centers in active distribution networks based on robust control and transformer machine learning strategy","authors":"Ashkan Safari ,&nbsp;Kamran Taghizad Tavana ,&nbsp;Mehrdad Tarafdar Hagh ,&nbsp;Ali Esmaeel Nezhad","doi":"10.1016/j.suscom.2025.101214","DOIUrl":"10.1016/j.suscom.2025.101214","url":null,"abstract":"<div><div>Internet data centers (IDCs) are critical infrastructures supporting the digital economy, necessitating stable and resilient energy supply to ensure continuous operation and meet increasing computational demands. This study develops an advanced optimization framework. The framework improves IDC energy efficiency by leveraging their spatio-temporal flexibility for intelligent participation in power system operations. The proposed framework uses an energy portfolio comprising combined heat and power (CHP) units, fuel cells (FCs), locally controllable generators (LCGs), and renewable energy sources (RESs), to reduce reliance on the main grid while maintaining operational efficiency. To address supply/demand uncertainties, robust optimization (RO) is applied. Furthermore, extreme gradient boosting (XGBoost) is used for feature selection and engineering, identifying key parameters mostly effecting the IDCs behavior. These features are then fed into a Transformer-based machine learning (ML) model, which captures complex spatio-temporal dependencies and provides accurate forecasts. The predictions are then incorporated into the RO-based decision-making process to support real-time energy optimization. The proposed framework is validated on the IEEE 33-bus standard distribution network, simulating realistic IDC operation scenarios. Results show the higher performance of the proposed strategy, achieving at least 35.3 % improvement in mean absolute error (MAE), reduced to 16.22 kWh, and 16.7 % improvement in root mean square error (RMSE), reduced to 33.56 kWh, compared to conventional ML models. Additionally, the proposed model is evaluated by the other KPIs of root mean square relative error (RMSRE=0.35), mean square relative error (MSRE=0.12), mean absolute relative error (MARE=0.16), normalized RMSE (nRMSE=0.14), and normalized MAE (nMAE=0.08). These findings confirm the robustness and effectiveness of the proposed hybrid framework in enhancing IDC operational efficiency.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101214"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220314","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}