Sustainable Computing-Informatics & Systems最新文献

{"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":"2025-10-15","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":"Hybrid AI framework for detecting cyberattacks and predicting cascading failures in power systems","authors":"Lalit Agarwal ,&nbsp;Bhavnesh Jaint ,&nbsp;Anup K. Mandpura","doi":"10.1016/j.suscom.2025.101222","DOIUrl":"10.1016/j.suscom.2025.101222","url":null,"abstract":"<div><div>The power grid is a critical infrastructure, relies on Supervisory Control and Data Acquisition (SCADA), a computer-based system for real-time monitoring and control of the grid. However, these systems are increasingly being targeted by cyberattackers, posing significant risks to grid stability and security. Existing security solutions focus on either attack detection by verifying their signatures or predicting their cascading failure to isolate the failed component from the rest of the working components. In the current paper, our objective is to detect new or existing attacks and predict their cascading failure. This research accomplish the objective by introducing a new multi-model framework that combines three models, XGBoost, Transformer, and Graph Neural Networks (GNNs), to identify both known and unknown cyberattacks with forecast their cascading impacts on power grid systems. The XGBoost model detects the known attack patterns, which includes Data Injection, Remote Tripping Command Injection, Relay Setting Change Attacks. The Transformer model identifies the deviations from established attack patterns, which result in the discovery of new threats. Our evaluation of grid infrastructure attacks utilizes a GNN-based cascading failure prediction model that represents the power grid as a graph to forecast failure propagation through interconnected nodes. Through rigorous testing using an real world dataset, our framework shows exceptional detection performance while maintaining effective generalization to new attacks and strong cascading failure prediction capabilities. The results showcase accuracy up to 98. 6% and a score of 0.98 F1 in multisource datasets, outperforming single-model baselines.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101222"},"PeriodicalIF":5.7,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320427","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":"Life cycle assessment of digital memories: The memristor’s environmental footprint","authors":"Nerea Benito ,&nbsp;Jose Carlos Pérez-Martínez ,&nbsp;Juan B. Roldán ,&nbsp;Ángela Lao ,&nbsp;Antonio Urbina ,&nbsp;Lucía Serrano-Luján","doi":"10.1016/j.suscom.2025.101229","DOIUrl":"10.1016/j.suscom.2025.101229","url":null,"abstract":"<div><div>Memristor technologies, pivotal in the evolution of energy-efficient digital devices, have the potential to revolutionize fields like non-volatile memories, hardware cryptography, neuromorphic computing and artificial intelligence acceleration. This study applies Life Cycle Assessment (LCA) methodology to analyse the environmental impact of five memristor designs, focusing on materials and manufacturing processes. The analysis adheres to ISO 14040–44 standards and employs the ReCiPe methodology to evaluate 18 environmental impact categories, emphasizing categories such as freshwater ecotoxicity and global warming potential. The results highlight significant variations in environmental impacts across the designs, largely attributed to differences in active layer materials and manufacturing processes. Molybdenum exhibits the highest impact, particularly in freshwater ecotoxicity, while SiO₂ demonstrates the lowest overall impact. Manufacturing processes like sputtering and photolithography carried out at laboratory scale contribute disproportionately to energy consumption and environmental damage, suggesting that upscaling production to industrial efficiencies is mandatory to mitigate these impacts. Furthermore, several materials required for memristor fabrication are listed as critical by the International Energy Agency (IEA), raising concerns about supply security, resource scarcity and environmental sustainability. This analysis serves as a foundational step for optimizing memristor technologies, balancing performance demands with environmental stewardship. To the best of our knowledge, this is the first comprehensive Life Cycle Assessment that compares multiple memristor architectures using real laboratory data and evaluates their environmental impacts. This work provides a methodological foundation for future sustainability assessments in the context of emerging memory technologies.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101229"},"PeriodicalIF":5.7,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266649","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":"Multi-objective energy-efficient power system scheduling using Stochastic State Space Model and reinforcement learning","authors":"Jiaying Wang,&nbsp;Xiaoqian Meng,&nbsp;Xuan Yang,&nbsp;Haibing Yin,&nbsp;Pingkai Fang","doi":"10.1016/j.suscom.2025.101224","DOIUrl":"10.1016/j.suscom.2025.101224","url":null,"abstract":"<div><div>The increasing complexity of modern power systems, arising from increased electricity demand and large-scale renewable energy resource integration, creates significant challenges for real-time scheduling and operational reliability. Conventional deterministic scheduling processes mostly cannot incorporate the inherent uncertainty and variability associated with the fluctuations of wind and solar generation, as well as fluctuating load demand, which leads to inefficiencies in the amount of necessary resources required and increased operational costs. This research proposes a novel multi-objective power scheduling framework that incorporates a Stochastic State-Space Model (SSSM) and Reinforcement Learning (RL) for dynamic management of generation, storage, and demand uncertainty. The SSSM takes into account the stochastic variability of renewable generation, uncertain demand profiles, and exogenous contingencies of the system. The RL agent is continuously learning the best scheduling strategies as it operates the power system to minimize operational costs while improving availability and maximizing scheduling performance. Simulation results using Monte Carlo testing over a 24-hour horizon demonstrated that the proposed method achieved a reduction of up to 20 % in operational costs, 10 % more system availability, and scheduling efficiencies of over 90 % compared to traditional methods. The proposed approach offers a feasible way forward for power systems operators to simultaneously meet the objectives of cost, reliability, and sustainability under a paradigm of uncertainty, while also having relevant application to real-time operation in smart grid systems, particularly in systems with high renewable energy.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101224"},"PeriodicalIF":5.7,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267157","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":"2025-10-08","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":"F2S-WSS: A forecast-driven two-stage workload scheduling scheme for carbon-aware geo-distributed data centers with wind power integration","authors":"Xueying Zhai ,&nbsp;Guojun Zhu ,&nbsp;Yunhao Zhang ,&nbsp;Xiuping Guo ,&nbsp;Yunfeng Peng","doi":"10.1016/j.suscom.2025.101216","DOIUrl":"10.1016/j.suscom.2025.101216","url":null,"abstract":"<div><div>The high energy consumption of cloud data centers (DCs) leads to a substantial carbon footprint. By reducing reliance on carbon-intensive fuels, renewable energy sources (RESs) such as wind power help mitigate greenhouse gas emissions. However, the inherent intermittency and fluctuation of RES generation, coupled with the stochastic nature of workload arrivals, complicate real-time scheduling and thereby significantly limit RES utilization efficiency in DCs. To address these issues, we propose a forecast-driven two-stage workload scheduling scheme that improves both scheduling efficiency and environmental sustainability. Specifically, we design a forecasting framework that integrates long short-term memory (LSTM) variants with a hierarchical decomposition using empirical mode decomposition (EMD) followed by variational mode decomposition (VMD). By precisely eliminating high-frequency noise and separately forecasting frequency components, the framework reduces noise interference and more accurately captures temporal patterns in workload and RES series. In the first stage, based on these forecasting results, effective global optimization is achieved in offline scheduling. In the second stage, scheduling results are dynamically adjusted based on real-time RES supply and workload demand to correct prediction errors. Experiments on real-world datasets validate the effectiveness of the proposed scheme. The forecasting models consistently outperform multiple baselines in prediction accuracy, achieving 3.41-69.46% reductions in mean absolute error compared to the state-of-the-art method. In addition, the proposed scheduling scheme increases RES utilization by 17.73–40.40% and achieves a corresponding 8.55-16.27 tons reduction in carbon emissions compared with the baselines. Furthermore, it shortens real-time scheduling latency by 81.3% relative to the real-time-only variant, underscoring its effectiveness in enabling sustainable and efficient DC operations.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101216"},"PeriodicalIF":5.7,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267158","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 resource scheduling scheme using modified load adaptive sequence arrangement (M-LASA) with FILO polling for optical access network","authors":"Mohan V ,&nbsp;Senthil Kumar T ,&nbsp;Chitrakala G","doi":"10.1016/j.suscom.2025.101223","DOIUrl":"10.1016/j.suscom.2025.101223","url":null,"abstract":"<div><div>Power conservation gains more attention in passive optical networks for enhanced performance. Optical networks have pooling sequences that schedule the resources based on traffic load to attain better energy efficiency. Various sequence schemes have been introduced by the research community; however, the load adaptive sequence arrangement (LASA) suits well for optical access networks. This research proposes a Modified LASA (M-LASA) model that improves energy efficiency in Optical Access Networks (OAN) by integrating a First-In-Last-Out (FILO) polling sequence. The proposed scheme increases the optical network units’ (ONUs) idle time, thereby reducing power consumption significantly compared to traditional scheduling strategies. Simulation results reveal that the proposed M-LASA-FILO scheme outperforms existing methods—such as fixed polling DFB, fixed polling VCSEL, LASA, FILO-DFB, and FILO-VCSEL—in terms of reduced power consumption, improved energy savings, higher sleep count, lower delay, and minimized polling cycle time. For instance, the proposed model achieves maximum energy savings and lower delay even at increased idle time and higher traffic load, confirming its efficiency and robustness in dynamic network conditions.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101223"},"PeriodicalIF":5.7,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266645","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":"Partitioned scheduling in mixed-criticality systems with thermal-constrained and semi-clairvoyance","authors":"Yi-Wen Zhang,&nbsp;Jin-Peng Ma","doi":"10.1016/j.suscom.2025.101217","DOIUrl":"10.1016/j.suscom.2025.101217","url":null,"abstract":"<div><div>With the exponential growth of power density in modern high-performance processors, it has not only led to significant energy but also resulted in increased chip temperatures. Therefore, reducing energy consumption and temperature have become two important issues in mixed-criticality systems (MCS) design. This paper focused on semi-clairvoyant scheduling in MCS with multiprocessor platforms. In semi-clairvoyant scheduling, high-criticality jobs are aware of whether their execution time will surpass their Worst-Case Execution Time in the low-criticality mode upon their arrival. Firstly, we give temperature constraints for the MCS task set based on steady-state thermal analysis. Secondly, we propose a new thermal-aware partitioned semi-clairvoyant scheduling algorithm called (TAPMC), aiming to minimize the normalized energy consumption under threshold temperature constraints. Finally, we evaluated TAPMC experimentally compared to other benchmark algorithms, and the experimental results illustrate that the TAPMC algorithm surpasses other algorithms in normalized energy consumption.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101217"},"PeriodicalIF":5.7,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266643","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":"Intelligent reinforcement learning for enhanced energy efficiency in hybrid electric vehicles","authors":"Shilpa Ghode ,&nbsp;Mayuri Digalwar","doi":"10.1016/j.suscom.2025.101219","DOIUrl":"10.1016/j.suscom.2025.101219","url":null,"abstract":"<div><div>Energy Management in Hybrid Electric Vehicles (EMinHEVs) refers to optimizing energy flow within a vehicle’s powertrain to enhance efficiency and range. This process involves complex tasks such as power analysis, component characterization, and hyperparameter reconfiguration, which directly impact the performance of energy management algorithms. However, existing optimization models struggle with scalability and inter-component correlations, limiting their effectiveness. This paper introduces a novel model-based hybrid framework combining Deep Dyna Reinforcement Learning (D2RL) with Genetic Optimization to address these challenges. Unlike conventional model-free approaches, the D2RL leverages a learned internal model to simulate future states, enabling more efficient decision-making and parameter tuning. The framework dynamically refines critical engine parameters — including speed, power, and torque — for both the generator and motor. Initially, D2RL estimates optimal parameter sets, which are then fine-tuned using a Genetic Optimizer. This optimizer incorporates an augmented reward function to iteratively enhance energy efficiency and vehicle performance. The proposed method outperforms state-of-the-art techniques, including Optimal Logical Control, Adaptive Equivalent Consumption Minimization Strategy, and Learnable Partheno-Genetic Algorithm. Experimental results demonstrate a 3.5% reduction in engine costs, an 8.3% improvement in fuel efficiency, optimized torque characteristics, and minimized current requirements. These findings establish our approach as a scalable and effective solution for intelligent energy management in hybrid electric vehicles, offering a significant advancement in model-based optimization strategies.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101219"},"PeriodicalIF":5.7,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266647","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":"Trade-offs between power consumption and response time in deep learning systems: A queueing model perspective","authors":"Yuan Yao,&nbsp;Bin Zhu,&nbsp;Yang Xiao,&nbsp;Hao Liu","doi":"10.1016/j.suscom.2025.101220","DOIUrl":"10.1016/j.suscom.2025.101220","url":null,"abstract":"<div><div>Deep learning has revolutionized numerous fields, yet the computational resources required for training these models are substantial, leading to high energy consumption and associated costs. This paper explores the trade-off between energy usage and system performance, specifically focusing on the average waiting time of tasks in environments that manage multiple types of jobs with varying levels of priority. Recognizing that not all training tasks have the same urgency, we introduce a framework for optimizing GPU energy consumption by adjusting power limits based on job priority. Using matrix geometric approximations, we develop an algorithm to calculate the mean sojourn time and average power consumption for such systems. Through a series of experiments and simulations, we validate the model’s accuracy and demonstrate the existence of a power-performance trade-off. Our findings provide valuable guidance for practitioners seeking to balance the computational efficiency of deep learning workflows with the need for energy conservation, offering potential for both cost reduction and sustainability in large-scale AI systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101220"},"PeriodicalIF":5.7,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266644","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}