Sustainable Computing-Informatics & Systems最新文献

{"title":"Spatial-temporal analysis of atmospheric environment in urban areas using remote sensing and neural networks","authors":"Marzieh Mokarram ,&nbsp;Farideh Taripanah ,&nbsp;Tam Minh Pham","doi":"10.1016/j.suscom.2024.100987","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.100987","url":null,"abstract":"<div><p>Rapid urbanization has given rise to escalating land surface temperatures, climate change, and the emergence of surface urban heat islands (SUHIs) and urban hot spots (UHSs), posing significant environmental challenges. This study, situated in the dynamic urban landscape of southern Iran, leverages Landsat satellite imagery to scrutinize the repercussions of temperature escalation on the environment. Our approach harnesses a novel Urban Thermal Field Variance Index (UTFVI) in conjunction with thermal and spectral indices to gain insights into these challenges. We employ a multifaceted methodology that integrates linear regression, cellular automata (CA)-Markov chains, and advanced neural network techniques to predict land surface temperature (LST) values and associated indicators. Over the span of 2000–2019, our findings reveal a 5% augmentation in urban heat islands (UHIs), signifying an alarming temperature increase. A striking 46% of the region, as uncovered by UTFVI, falls into the most severe categories of ecological discomfort. Our analysis underscores the robust correlations between LST and critical indices, notably the Normalized Difference Built Index (NDBI) (0.96), Normalized Difference Vegetation Index (NDVI) (-0.71), UTFVI (0.98), and SUHI (0.82). Notably, our original contributions lie in the application of Artificial Neural Networks (ANNs), wherein the Multilayer Perceptron (MLP) method excels in predicting UTFVI (R²=0.96) and NDBI (R²=0.96), while the Radial Basis Function (RBF) method demonstrates remarkable accuracy in forecasting the SUHI index (R²=0.96). These achievements signify a groundbreaking advancement in comprehending the intricate dynamics of urban environmental conditions. The repercussions of increased urbanization, the proliferation of barren land, and dwindling vegetation in 2019 manifest in a marked decline in ecological quality, with a concomitant surge in temperatures within the study area. These findings underscore the pressing need for informed urban planning and sustainable practices to mitigate the detrimental effects of urban heat islands and their impact on local climates.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100987"},"PeriodicalIF":4.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140646910","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":"Muddy irrigation ditch understanding for agriculture environmental monitoring","authors":"Luping Wang ,&nbsp;Hui Wei","doi":"10.1016/j.suscom.2024.100984","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.100984","url":null,"abstract":"<div><p>Understanding an irrigation ditch plays an important role in intelligent agriculture environmental monitoring, especially in field environments where large chunks of ditches are particularly covered by various types of natural unstructured soil, vegetation and weeds. However, due to the diverse and unstructured muddy ditches, understanding them remains a challenge. Traditional approaches of understanding a scene from three-dimensional (3D) point clouds or multi-sensor fusion are energy intensive and computationally complex, making them quite laborious in application on a resource-constrained system. In this study, we propose a methodology to understand irrigation ditches and reconstruct them in a 3D scene, using only a resource-constrained monocular camera, without prior training. Spatial similar textures projections are extracted and clustered. Through geometric constraints of distribution and orientation, similar texture projections are refined and their corresponding surfaces are shaped. By contours and evidence lines, the ditch bottom surfaces are represented. Thus an irrigation ditch can be understood and reconstructed in a 3D environment, which can be used in agricultural automatic control system, agricultural robots, and precise agriculture. Unlike machine learning-based algorithms, the proposed method requires no prior training nor knowledge of the camera’s internal parameters such as focal length, field angle, and aperture. Additionally, pure geometric features make the presented method robust to varying illumination and colour. The percentage of incorrectly classified pixels was compared to the ground truth. Experimental results demonstrated that the approach can successfully elucidate irrigation ditches, meeting requirements in safety monitoring in an agriculture environment.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100984"},"PeriodicalIF":4.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140344746","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":"Optimal allocation of solar PV and wind energy power for radial distribution system using spider monkey optimization","authors":"Waseem Sultana,&nbsp;S.D.Sundarsingh Jebaseelan","doi":"10.1016/j.suscom.2024.100986","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.100986","url":null,"abstract":"<div><p>The integration of renewable energy sources, relatable as Solar Photovoltaic (PV) and Wind Power, into the radial distribution system has gained significant attention due to their eco-friendly and sustainable attributes. This article presents a narrative advent for achieving the finest share of Solar PV and Wind force power through a radial distribution system using the innovative Spider Monkey Optimization (SMO) algorithm. Multi-objective function for the minimization of distribution loss and voltage deviation with the constraints of power balance equation and boundary limits of voltage and power is considered. The Spider Monkey Optimization algorithm, stimulated via the community activities of spider monkeys, be employed to effectively search for the finest allotment of Solar PV and Wind Energy Power within the distribution network. The SMO algorithm exhibits robustness in handling non-linear and multi-dimensional optimization problems, making it suitable for this complex task. To authorize the usefulness and efficiency of the planned approach, it is functional to standard 33-bus radial division coordination. Comparative analyses of optimization techniques are reported and SMO reduces the losses to 104 KW and the voltage deviation is minimized to 0.0458 pu. The valuable perception is that incorporating Solar PV and Wind Energy sources into radial distribution systems improves the quality.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100986"},"PeriodicalIF":4.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140554427","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 systematic review of green-aware management techniques for sustainable data center","authors":"Weiwei Lin ,&nbsp;Jianpeng Lin ,&nbsp;Zhiping Peng ,&nbsp;Huikang Huang ,&nbsp;Wenjun Lin ,&nbsp;Keqin Li","doi":"10.1016/j.suscom.2024.100989","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.100989","url":null,"abstract":"<div><p>Cloud computing is one of the powerful engines driving global industrial upgrading and the booming digital economy. However, the explosive growth of cloud data centers (DCs) has resulted in inevitable energy consumption and carbon emission problems. Therefore, constructing energy-efficient and sustainable DCs will be essential for green cloud computing. This review makes several efforts to thoroughly investigate and track the research progress and routes to sustainable DCs. Firstly, we construct a new conceptual model of sustainable DCs to cover cutting-edge research results and indicate future evolutionary directions. Secondly, this review provides a comprehensive survey of five topics from a technical perspective: workload management, virtual resource management, energy management, thermal management, and waste heat recovery. Subsequently, some real-world datasets relevant to the topics, including workload traces, renewable energy data, and electricity price traces, have been specifically collected to support researchers in conducting further research. Finally, based on observations of existing works, we highlight some salient technical challenges and promising solutions to provide sensible energy and carbon reduction suggestions in sustainable DCs.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100989"},"PeriodicalIF":4.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633171","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":"Rock-hyrax: An energy efficient job scheduling using cluster of resources in cloud computing environment","authors":"Saurabh Singhal ,&nbsp;Shabir Ali ,&nbsp;Mohan Awasthy ,&nbsp;Dhirendra Kumar Shukla ,&nbsp;Rajesh Tiwari","doi":"10.1016/j.suscom.2024.100985","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.100985","url":null,"abstract":"<div><p>In a cloud computing environment, job scheduling allows the service provider to schedule resources based on demand. Job scheduling must also ensure QoS, end-user satisfaction, and the efficient usage of resources. Cloud computing vendors assign virtualized computing resources to end-users based on job requirements that are dynamically scalable and pay-per-use. The assignment of jobs requires proper investigation and mapping of available resources. In this paper, we have proposed a novel job scheduling scheme based on Rock Hyrax. Our Rock Hyrax approach uses objective functions to map jobs to available resources. The objective function considers a variety of QoS parameters like makespan, response time and energy efficiency. Our method employs two key QoS parameters: makespan and energy consumption. The node behavior and characteristics, such as processing power, storage, and network connectivity to cluster similar resources, have also been considered for scheduling. An experimental setup is created for a thorough study of the proposal using CloudSim simulator. For both the jobs and virtual machines, static and dynamic scenarios for performance evaluation have been developed. To compare our work with existing scheduling algorithms like ACO, PSO, BFO, and ABC has been considered and we have found that the proposal reduces makespan by 2–9% as increased in jobs. Furthermore, the proposed method reduces total energy consumption in data centers by 7–23% as jobs request increases. The findings support the claim that the proposed method surpasses the existing methods and significantly shortens the time needed to determine the resource required for the job.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100985"},"PeriodicalIF":4.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140558415","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 integrated energy storage framework with significant energy management and absorption mechanism for machine learning assisted electric vehicle application","authors":"K. Valarmathi ,&nbsp;J. Seetha ,&nbsp;N.V. Krishnamoorthy ,&nbsp;M. Hema ,&nbsp;G. Ramkumar","doi":"10.1016/j.suscom.2024.100982","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.100982","url":null,"abstract":"<div><p>Regarding environmental friendliness, low maintenance needs, and statuses as a renewable technology, Hybrid Electric Vehicles (HEV) have become more and more popular around the world. In this, the energy management system is crucial for the effective storage of power and regulation of the energy flow system. As a result, Hybrid Energy Storage Systems (HESS) has increased interest due to their superior capabilities in system performance and battery capacity when compared to solo energy sources. Additionally, the primary problem interaction applications, including such battery electric vehicles, are the energy storage system. Multiple energy storage technologies, including battery packs, flywheels, super-capacitors and fuel cells, are combined into a HESS due to their complementing properties. The goal of this setup is to make renewable energy sources more reliable by storing power generated from intermittent sources or by providing backup energy generation from traditional energy sources. A HESS could be utilized as an alternate energy storage system to help them make up for their lack of power density. HESS needs a smart Energy Management System (EMS) to function properly since it combines the dynamic characteristics of a battery and a SuperCapacitor (SC). The motive of the study is to suggest an actual power management control system to accomplish these objectives. The plan is built using a wavelet transform, deep learning mechanism, and fuzzy logic together. A useful tool for separating the various frequency elements of a load's power requirements to reflect the properties of a battery or supercapacitor is the wavelet transform. It is challenging to immediately apply it in a system, though. Because of this, the traditional optimization-model-based facility energy management system encounters substantial difficulties with online forecast and calculation. To solve this problem, the paper proposes a ML technique dependent on a Long Short-Term Memory (LSTM). The suggested control system structure allows for the separation of the offline and online stages of the LSTM technique. The LSTM is being used to map states (inputs) to decisions (outputs) based on system training during the offline stage. As a result, the supercapacitor receives an online calculation and distribution of the high-frequency power requirement. The SOC of the supercapacitor is kept within the appropriate range via fuzzy logic control. To evaluate the efficacy of the suggested energy management control technique, a 70 V battery with 92 V supercapacitor hybrid energy storage devices for hardware platforms have been created.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100982"},"PeriodicalIF":4.5,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140163353","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":"Enhancing micro grid sustainability: Genetic-driven reptile search algorithm based carbon emission and cost reduction strategy","authors":"Jiahua Hu ,&nbsp;Xiaozhe Yin ,&nbsp;Xuehai Zhao ,&nbsp;Zheng Zhou","doi":"10.1016/j.suscom.2024.100981","DOIUrl":"10.1016/j.suscom.2024.100981","url":null,"abstract":"<div><p>Grid architecture is continuously changing in response to modern energy rules that ensure the integration of more renewable sources to lower the carbon footprint. The integration of non-dispatchable renewable energy sources (RESs) using low voltage grids is a possible alternative to a centralized method. Due to a recent trend, microgrids (MGs) are reducing their reliance on the primary grid by incorporating localized sources of electricity including energy storage devices, micro turbines, and fuel cells. Due to the expanding number of spread generating sources with diverse features, power dispatching schedule for distribution micro grids is becoming more challenging. This study offers a dispatch scheduling method from the perspective of an operator. The primary objective of this study is to employ the Genetic-driven Reptile search algorithm (G-RSA) for the dual purpose of minimizing carbon emissions and reducing costs within each microgrid. Additionally, fewer sale and purchase are made from the primary grid. Transmission losses are consequently reduced.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 100981"},"PeriodicalIF":4.5,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140073595","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 fault-tolerant and energy-efficient design of RAM cell and PIM structure in quantum technology","authors":"Leila Dehbozorgi,&nbsp;Reza Akbari-Hasanjani,&nbsp;Reza Sabbaghi-Nadooshan","doi":"10.1016/j.suscom.2024.100979","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.100979","url":null,"abstract":"<div><p>In this paper, a RAM cell in ternary QCA is proposed. Moreover, a 2×1 memory array and a TPIM (ternary processing in memory) structure are designed using the proposed ternary RAM cell. The evaluation of the design parameters shows that the proposed ternary SRAM and PIM structures are efficient in terms of cost, area, and fault tolerance while the volume of information-carrying is high because of the ternary structure. The manufacturing defects in the chemical manufacturing process of QCA circuits are possible. One of these defects is cell omission which has not yet been investigated for QCA-based SRAM in ternary structure. According to the results, by migrating from binary to ternary QCA, the fault tolerance can be increased without increasing the occupied area. Then, the fault tolerance of ternary RAM cells is calculated and compared with that of binary structures. The primary aims of this study are to improve fault tolerance and optimization of design parameters, which are achieved according to the results.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100979"},"PeriodicalIF":4.5,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140014516","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":"Mining of heterogeneous time series information for predicting chlorophyll accumulation in oceans","authors":"Atharva Ramgirkar ,&nbsp;Vadiraj Rao ,&nbsp;Janhavi Talhar ,&nbsp;Tusar Kanti Mishra ,&nbsp;Swathi Jamjala Narayanan ,&nbsp;Shashank Mouli Satapathy ,&nbsp;Boominathan Perumal","doi":"10.1016/j.suscom.2024.100980","DOIUrl":"10.1016/j.suscom.2024.100980","url":null,"abstract":"<div><p>Harmful algal blooms cause environmental harm, financial losses, and disease epidemics. It is also known that the algal blooms cannot be eradicated; hence the best option is to foresee their growth and regulate it. Machine learning algorithms can be used to forecast their presence and further classify the threat that each concentration level presents. In this research work, the dataset collected from Santa Monica, US region is analyzed and processed to predict algae concentration using machine learning algorithms. In this process, the machine learning models such as multiple linear regression, Regression Gradient Boosting Decision Tree (RGBDT), and Hidden Markov Model (HMM) are applied to predict the chlorophyll (Chl-a) content, which serves as a proxy for the presence of algae in the water. The obtained results show that for prediction, the Multilinear regression model outperforms the RGBDT (Regression Gradient Boosting Decision Tree) algorithm. Similarly, for modeling chlorophyll using HMM (Hidden Markov Model), parameter <em>bbp555.00_sd</em> is the best among parameters like <em>aot443.00_sd</em>, <em>kd490.00_sd</em>, <em>poc_sd</em> and <em>pic_sd</em>. The multiple linear regression model gave an adjusted R-squared error of 0.94 with the parameter pic_sd having the least VIF value of 1.78 followed by <em>aot</em> and <em>bbp</em> which have VIF<span><math><mo>&lt;</mo></math></span>5 (2.28 and 4.95 respectively). The outcome of the HMM-based model represents the probability of the presence of chlorophyll given the presence of each of the variables individually. From the results, it is observed that <em>bbp</em> has the highest probability of 0.405 implying that there is a 40% chance of chlorophyll in the presence of <em>bbp</em>.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100980"},"PeriodicalIF":4.5,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140018734","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 port consolidation model for data center network infrastructure energy efficiency","authors":"Syed Muhammad Sheraz ,&nbsp;Asad Arfeen ,&nbsp;Umaima Haider","doi":"10.1016/j.suscom.2024.100973","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.100973","url":null,"abstract":"<div><p>Technological advancements have increased the energy consumption of data center network infrastructure causing an increase in global carbon footprints. Consolidation of hardware is considered to be one of the acceptable techniques for reducing energy consumption in data centers. However, consolidation of hardware may result in scarce fault tolerance which consequently leads to performance degradation. Therefore, a technique is required that performs consolidation while data center performance remains unaffected. In this work, a port consolidation model has been proposed. The proposed model diverts the traffic of the ports in a switch to other ports so that the number of active ports can be reduced. The reduction of active ports will result in the energy-efficient utilization of the switch. The proposed diverts the traffic by evaluating the Arrival Rate of packets and the Forwarding Rate of the switch. The proposed algorithm used in the model has been examined through a case study. The proposed model is implemented in integration with our previously proposed network refresh model.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100973"},"PeriodicalIF":4.5,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139985197","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}