Sustainable Computing-Informatics & Systems最新文献

{"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":"2025-12-01","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":"2025-12-01","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":"Energy-efficient Load Balanced Edge Computing model for IoT using FL-HMM and BOA optimization","authors":"Xiaochang Zheng ,&nbsp;Ruixiang Guo ,&nbsp;Shujing Lian","doi":"10.1016/j.suscom.2025.101215","DOIUrl":"10.1016/j.suscom.2025.101215","url":null,"abstract":"<div><div>Consumers will have access to ubiquitous, low-latency computing services through the deployment of mobile edge computing (MEC) devices situated at the network's peripheral in next-generation wireless networks. Taking into account the design-based constraints on radio-access coverage and CS stability, we investigate the network's latency performance, namely the latency of computation and communication. Here, we want to model a spatial random network that has properties such as randomly dispersed nodes, parallel processing, non-orthogonal multiple access, and computing jobs that are produced at random. The emerging Internet of Things apps are putting a premium on very fast response times, and more and more people are turning to the edge computing system to handle these demands. Regardless, problems with latency (such as very sensitive delay required by emergent traffic). In this paper, we designed a Load Balanced Edge Computing (LBEC) model for Internet of Things (IoT). The overall contributions lies in three fold: First, the IoT devices are clustered based on load status in order to balance load in the network layer. For cluster formation, we presented K-Hop neighbor approach. In next, the cluster level load balancing is achieved by maintaining cluster reformation through Fuzzy Logic based Hidden Markov Model (FL-HMM). Finally, edge-level load balancing is attained through offloading procedure. We proposed Bobcat Optimization Algorithm (BOA). Final experimental results show that the proposed LBEC achieves better performance up to 5 % in each parameter such as response time, offloading time and throughput.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101215"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266648","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":"2025-12-01","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":"Particle swarm optimization of fuzzy logic-based energy management system for enhanced efficiency in fuel cell hybrid electric vehicles","authors":"Abdesattar Mazouzi ,&nbsp;Nadji Hadroug ,&nbsp;Ahmed Hafaifa ,&nbsp;Abdelhamid Iratni ,&nbsp;Ilhami Colak","doi":"10.1016/j.suscom.2025.101239","DOIUrl":"10.1016/j.suscom.2025.101239","url":null,"abstract":"<div><div>Fuel cell hybrid electric vehicles (FCHEVs) present a promising solution for reducing emissions, enhancing energy efficiency, and extending driving range compared to pure electric vehicles. To overcome the limitations of fuel cell technology, auxiliary energy storage systems are incorporated, resulting in a hybrid powertrain. Effective energy management systems (EMS) are critical for optimizing power distribution among these diverse energy sources. This study proposes a novel EMS approach that combines fuzzy logic control with particle swarm optimization (PSO). The PSO algorithm is employed to optimize the membership functions of the fuzzy logic controller, thereby improving its overall performance. The primary objective is to maximize fuel economy while maintaining the battery state of charge (SOC) at the desired level. The proposed methodology was implemented and tested under four distinct driving conditions. Comparative analysis with both the original EMS and a non-optimized fuzzy logic system demonstrated significant improvements in hydrogen consumption and battery SOC maintenance. Specifically, the optimized fuzzy EMS with triangular membership functions outperformed ADVISOR by 26.91 % and showed a 15.56 % improvement post-optimization. Similarly, the optimized fuzzy EMS with trapezoidal membership functions outperformed ADVISOR by 25.14 %, with a 5.9 % enhancement after optimizing the membership functions. These results highlight the effectiveness of the proposed method in enhancing system performance, achieving significant improvements in hydrogen consumption, and maintaining optimal battery SOC.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101239"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520119","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 low-latency and area-efficient QCA-based quantum-dot design for next-generation digital sustainable systems","authors":"Muhammad Zohaib ,&nbsp;Seyed-Sajad Ahmadpour ,&nbsp;Hadi Rasmi ,&nbsp;Angshuman Khan ,&nbsp;Nima Jafari Navimipour","doi":"10.1016/j.suscom.2025.101204","DOIUrl":"10.1016/j.suscom.2025.101204","url":null,"abstract":"<div><div>Digital sustainable system plays a vital role in the advancement of dynamic industries, including agriculture, healthcare, smart cities, Edge Artificial Intelligence (AI), and the Internet of Things (IoT), by facilitating high-speed, low-power, and highly compressed processing. These systems are based on the capabilities of real-time execution, processing, and analysis of large-scale information with extreme power and area limitations. However, traditional Arithmetic Logic Units (ALUs) based on complementary metal-oxide semiconductors (CMOS) are becoming challenging in terms of scalability, power consumption, space demand, and nanoscale fabrication. The ALU is one of the most important parts of such systems and has a direct effect on the overall computing performance, but current implementations cannot sustain the requirements of next-generation applications. To overcome these shortcomings, this paper offers an area-efficient and low-latency ALU that can be designed with the quantum-dot cellular automata (QCA) technology, with the advantage of employing area-efficient layout and simple cell design. The proposed QCA-based ALU has high performance, less delay, and less energy consumption, which makes it properly suitable for the next generation of digital sustainable systems applications. The outcome of the simulation indicates that there are considerable performance gains, such as an 82.37% decrease in energy consumption, and a 9.21% decrease in area relative to current available design. These enhancements emphasize the power of QCA technology as a scalable and low-energy consumption alternative to CMOS in the realization of critical computing components in sustainable digital systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101204"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096419","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 consumption forecasting with hybrid deep learning approach, explainable AI, and hunger games optimization","authors":"Shahab S. Band ,&nbsp;Faezeh Gholamrezaie ,&nbsp;Fatemeh Asghari Hampa ,&nbsp;Sultan Noman Qasem","doi":"10.1016/j.suscom.2025.101255","DOIUrl":"10.1016/j.suscom.2025.101255","url":null,"abstract":"<div><div>Accurate forecasting of energy consumption is a critical component of effective resource management across the building, industrial, and transportation sectors. This work proposes a hybrid novel approach that incorporates Convolutional Neural Networks (CNN) with Gradient Boosting (GB) and Random Forest (FR) for improving energy demand prediction capabilities. These models will undergo an optimization process by the application of the Hunger Games Search (HGS) algorithm, boosting the prediction accuracy while incorporating Explainable AI (XAI) techniques that make the results interpretable.</div><div>In the PJM region, a regional transmission organization in the United States, the time series data recorded by four monitoring stations are considered. The performance of different models is evaluated based on critical metrics comprising Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), and the coefficient of determination (R²) with 480 data points at each station. Among all models, CNN_RF_HGS performs the best as it tends to show a maximum of up to 0.9175 for the coefficient of determination in some cases. Such accuracy is achieved at the cost of a longer training time due to HGS optimization, highlighting a trade-off between accuracy and computational efficiency. However, the optimized model can be stored and reused as the pre-trained model, which will reduce the inference time by large margins and may fit real-time application purposes. Overall, this research demonstrates an effective blend of deep learning and traditional models for capturing complex nonlinear patterns in energy consumption, enabling more accurate and reliable forecasts.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101255"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614739","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":"Short term load forecasting using optimized deep learning based weighted DenseBiGRU for smart grids","authors":"T.M. Angelin Monisha Sharean ,&nbsp;R.S. Shaji","doi":"10.1016/j.suscom.2025.101240","DOIUrl":"10.1016/j.suscom.2025.101240","url":null,"abstract":"<div><div>Distribution system operators can successfully manage energy through the use of advanced demand-response programs in the smart grid (SG) due to short-term load forecasting. The short-term load forecasting approach is essential for effective energy management when taking into account the electric fields in the energy trade. Short-term load forecasting can be applied to many aspects of daily operations in infrastructure maintenance, energy purchase, contract analysis, energy generation planning, including load shedding, and electric utilities. There are a number of techniques for predicting short-term load. Still, all suffer from a lack of model parameter adaptability, making it impossible to meet the demand for precise and efficient smart grid load forecasting. In order to improve the model's predictive accuracy, an optimized deep learning (DL) model is employed in this study. The proposed Improved Weighted Mean of Vector based Dense Bidirectional Gated Recurrent Unit (I-INFO_DenseBiGRU) is utilized for the short term load forecasting with the weather data. The proposed I-INFO_denseBiGRU performance is calculated based on numerous events like MAPE, MSE, MAE, NRMSE, and R2, and achieves superior performance compared to state-of-the-art methods.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101240"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465674","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 walrus optimization-based control strategy with ESDs for AGC performance enhancement of power systems","authors":"Ravi Choudhary ,&nbsp;J.N. Rai ,&nbsp;Yogendra Arya","doi":"10.1016/j.suscom.2025.101235","DOIUrl":"10.1016/j.suscom.2025.101235","url":null,"abstract":"<div><div>For a sustainable society, continuity of power supply is essential. But, due to slow control action, automatic generation control (AGC) solution typically demonstrates debility in managing frequency fluctuations during significant disruptions in the energy systems penetrated with conventional controllers. To avoid blackouts and preserving the generation/demand balance, controlling the frequency and power variations is essential using AGC with an advanced controller and energy storage devices (ESDs). This study makes the use of rapid-acting ESDs to enhance power system (PS) dynamic performance. The thermal hydro gas (THG) single-area PS (SAPS) and two-area PS (2APS) are thoroughly examined to evaluate the efficacy of the suggested technique. A cascade 1+fractional order tilt integral derivative (1+FOTID)-fractional order proportional integral derivative (FOPID) controller tuned with walrus optimization (WO) technique is recommended for AGC. Authority of the advocated controller is validated over various existing controllers and WO-optimized TID and PID controllers with/without ESDs. Examining dynamic responses for abrupt changes in power demand reveals the supremacy with the advised technique against the prevailing strategies. The integration of capacitive energy storage (CES) ESD in PS improves the system dynamics. But, significant improvement is obtained when CES and redox flow battery (RFB) ESDs are utilized simultaneously. Incorporating ESDs, considered controller generates less value of cost function for SAPS (28.57 %) and 2APS (9.67 % for linear and 75.74 % for nonlinear) systems in comparison when only CES is used. According to the sensitivity analysis, this controller with/without ESDs exhibits resilient performance for random load disturbances and variations of ±25 % in PS parameters.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101235"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465670","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":"Economic and environmental multi-objective functions modeling in storage systems-based hybrid energy microgrid with demand side management strategy","authors":"J. Wen ,&nbsp;Sergey Zhiltsov ,&nbsp;Rustem Shichiyakh ,&nbsp;Samariddin Makhmudov ,&nbsp;Muzaffar Shojonov ,&nbsp;Anorgul I. Ashirova ,&nbsp;Yuldoshev Jushkinbek Erkaboy ugli ,&nbsp;M. Mohammadi","doi":"10.1016/j.suscom.2025.101245","DOIUrl":"10.1016/j.suscom.2025.101245","url":null,"abstract":"<div><div>This paper proposes a multi-objective functions and stochastic modeling aimed at optimizing and managing energy within a microgrid. This microgrid includes electric vehicles (EVs), fuel cell, battery energy storage system, photovoltaic (PV) panels, and microturbine with demand response. The multi-objective functions are modeled considering minimizations of the emissions pollution and operation costs under different weather conditions. Additionally, the stochastic method is represented using an unscented transformation method to model the uncertainties in power prices, power demand, and solar irradiation, thereby ensuring reliable and effective energy scheduling amidst uncertainty. The proposed optimaztion approach is implemented by numerical modeling in some case studies without and with considering demand response, electric vehicle and stochastic modeling. The results show the optimal values of the emissions pollution and operation costs with the participation of the demand response and electric vehicle by comparative analysis with improved sine cosine optimizer than other optimaztion algorithms.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101245"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465673","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}