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Korde, Geetanjali, Pooja Hardiya, and Shraddha Kumar. "Towards a Greener Cloud: Reducing Carbon Footprint for a Sustainable Future." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43387.
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Green cloud computing aims to reduce the environmental impact of expanding cloud infrastructure. Existing tools often address specific sustainability aspects, such as energy efficiency or carbon monitoring. This project proposes a unified dashboard that integrates essential green cloud computing features, including energy optimization, renewable energy management, lifecycle carbon tracking, and gamification. By centralizing these capabilities, the platform enables organizations to monitor, manage, and optimize their cloud operations sustainably. The dashboard offers real-time data visualization, AI-driven workload management, and user-centric engagement tools to reduce the carbon footprint of cloud computing operations. The custom dashboard will provide users with insights into energy consumption, carbon footprint, and resource utilization while utilizing energy-efficient cloud services. It will optimize resource allocation and offer suggestions to improve energy efficiency. Tailored for a seamless user experience, the dashboard helps businesses and individuals monitor and manage their environmental impact while maintaining operational efficiency. This solution supports sustainability and aids organizations in achieving environmental goals. The project will explore the technologies behind the system, address existing challenges, and evaluate its effectiveness in reducing the carbon footprint and energy usage of cloud computing operations. KEYWORDS - Cloud computing, green cloud computing, carbon footprint, data centres, energy efficiency.
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Gulati, Mandeep Kaur. "Green Cloud Computing and Sustainability: Innovations for a Greener Digital Future." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50281.
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Cloud computing is a cornerstone of modern digital infrastructure, offering scalable and flexible resources for computing needs. However, the significant energy consumption and carbon footprint of data centers have raised critical environmental concerns. This paper explores the concept of Green Cloud Computing (GCC), emphasizing its role in fostering sustainability within cloud environments. It reviews the challenges of traditional cloud infrastructure, highlights innovative solutions, and proposes a novel AI-driven framework to optimize energy use and integrate renewable energy sources. Industry case studies illustrate practical implementations, while future trends such as quantum computing and bio-inspired cooling are discussed. The paper concludes with policy recommendations to advance sustainable digital ecosystems. Keywords: Green Cloud Computing, Sustainability, Data Centers, Energy Efficiency, AI, Renewable Energy, Carbon Footprint, Sustainable IT
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Nazish, Baliyan, and Ahmed Muneeba. "ACHIEVING A SUSTAINABLE ENVIRONMENT USING GREEN CLOUD COMPUTING." International Journal of Engineering Sciences & Emerging Technologies 11, no. 2 (2023): 135–41. https://doi.org/10.5281/zenodo.10441396.
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<em>With the rising use of IT infrastructure around the world, the rise in energy consumption and carbon emissions is a reason of concern for everyone because of the direct impact on the environment. The growing usage of information technology infrastructure and its waste is endangering the environment. To meet the ever-increasing enterprise data storage and processing needs, cloud service providers are developing cutting-edge technologies such as Green Cloud Computing in cloud architecture design to reduce massive power consumption, water consumption and harmful carbon emissions, among other things. To preserve our environment from the harmful effects of cloud computing, service providers must embrace and update their cloud architecture to support green computing. By detecting energy emission reductions, CO2 removal, assisting in the development of greener transportation networks, monitoring deforestation, and anticipating extreme weather events, green cloud computing has the ability to expedite global efforts to safeguard the environment and conserve resources. As part of our study on green clouds, this article provides a full analytical report on green cloud computing and its properties.</em>
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Li, Hongming, Dongxiu Ou, and Yuqing Ji. "An Environmentally Sustainable Software-Defined Networking Data Dissemination Method for Mixed Traffic Flows in RSU Clouds with Energy Restriction." International Journal of Environmental Research and Public Health 19, no. 22 (2022): 15112. http://dx.doi.org/10.3390/ijerph192215112.
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The connected multi road side unit (RSU) environment can be envisioned as the RSU cloud. In this paper, the Software-Defined Networking (SDN) framework is utilized to dynamically reconfigure the RSU clouds for the mixed traffic flows with energy restrictions, which are composed of five categories of vehicles with distinctive communication demands. An environmentally sustainable SDN data dissemination method for safer and greener transportation solutions is thus proposed, aiming to achieve the lowest overall SDN cloud delay with the least working hosts and minimum energy consumption, which is a mixed integer linear programming problem (MILP). To solve the problem, Joint optimization algorithms with Finite resources (JF) in three hyperparameters versions, JF (DW = 0.3, HW = 0.7), JF (DW = 0.5, HW = 0.5) and JF (DW = 0.7, HW = 0.3), were proposed, which are in contrast with single-objective optimization algorithms, the Host Optimization (H) algorithm, and the Delay optimization (D) algorithm. Results show that JF (DW = 0.3, HW = 0.7) and JF (DW = 0.5, HW = 0.5), when compared with the D algorithm, usually had slightly larger cloud delays, but fewer working hosts and energy consumptions, which has vital significance for enhancing energy efficiency and environmental protection, and shows the superiority of JFs over the D algorithm. Meanwhile, the H algorithm had the least working hosts and fewest energy consumptions under the same conditions, but completely ignored the explosive surge of delay, which is not desirable for most cases of the SDN RSU cloud. Further analysis showed that the larger the network topology of the SDN cloud, the harder it was to find a feasible network configuration. Therefore, when designing an environmentally sustainable SDN RSU cloud for the greener future mobility of intelligent transportation systems, its size should be limited or partitioned into a relatively small topology.
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Pascual, J. A., T. Lorido-Botrán, J. Miguel-Alonso, and J. A. Lozano. "Towards a Greener Cloud Infrastructure Management using Optimized Placement Policies." Journal of Grid Computing 13, no. 3 (2014): 375–89. http://dx.doi.org/10.1007/s10723-014-9312-9.
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Singh, Harvinder, and Pinky . "Smart E bicycle: an efficient and effective way to greener future." International Journal of Engineering & Technology 7, no. 4.5 (2018): 477. http://dx.doi.org/10.14419/ijet.v7i4.5.21137.
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This paper presents and proposes a smart electric bicycle(SeB) leveraging the power of wireless technologies, artificial intelli- gence and cloud computing in order make its user’s experience smooth, safe and enjoyable hence encouraging the user to choose SeB over other modes of transportation. The proposed system introduces an Electric Bicycle connected with a smartphone in one variant or with “smartphone and cloud” in another variant for smart decision-making and efficiency and other related tips for the user. The range of bicycle is predicted based upon the user profile (weight, age etc.), route details (inclinations, distances of al- ternative routes), State of Charge(Soc) and State of Health(SoH) of the battery used. Multiple user profiles and minute details of the route (slope, speed breakers etc.) are captured using sensor like accelerometer and basis on these data smart decisions for pow- er saving and range extensions are made. Also, safety critical and predictive maintenance features are presented.
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Mahender, Mr Mula. "“Delay-Constrained Task Offloading and Resource Optimization in Edge-Cloud Networks’’." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03640.
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ABSTRACT-Delay-Constrained Task Offloading and Resource Optimization in Edge-Cloud Networks A joint optimization model is presented to reduce task latency and energy expenditure while satisfying application deadlines. The problem is cast as a mixed-integer nonlinear program (MINLP), and a hybrid solution integrating deep reinforcement learning and convex optimization is formulated to handle its complexity. This paper is extremely applicable to latency-sensitive IoT, augmented reality, and smart transportation applications. Our method is shown through extensive simulations to significantly reduce latency and save energy compared to conventional methods. In comparison to random or cloud-only approaches, our model guarantees timely completion of delay-sensitive tasks while optimizing resource usage throughout the network. This framework has great potential for facilitating faster, greener, and smarter future computing services in hybrid edge-cloud networks. Keywords: Edge Computing, Cloud Computing, Task Offloading, Delay Constraints.
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Wang, Xiushuang, Jing Zhu, Shunfu Jin, Wuyi Yue, and Yutaka Takahashi. "Performance Evaluation and Social Optimization of an Energy-Saving Virtual Machine Allocation Scheme Within a Cloud Environment." Journal of the Operations Research Society of China 8, no. 4 (2019): 561–80. http://dx.doi.org/10.1007/s40305-019-00272-x.
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AbstractAchieving greener cloud computing is non-negligible for the open-source cloud platform. In this paper, we propose a novel virtual machine allocation scheme with a sleep-delay and establish a corresponding mathematical model. Taking into account the number of tasks and the state of the physical machine, we construct a two-dimensional Markov chain and derive the average latency of tasks and the energy-saving degree of the system in the steady state. Moreover, we provide numerical experiments to show the effectiveness of the proposed scheme. Furthermore, we study the Nash equilibrium behavior and the socially optimal behavior of tasks and carry out an improved adaptive genetic algorithm to obtain the socially optimal arrival rate of tasks. Finally, we present a pricing policy for tasks to maximize the social profit when managing the network resource within the cloud environment.
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Kulkarni, Deven. "Smart City Model." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 2812–16. https://doi.org/10.22214/ijraset.2025.72201.
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With the rapid urbanization of cities, effective waste management and water conservation have become critical challenges. This paper presents a Smart City Module leveraging the Internet of Things (IoT) to monitor garbage levels and automate watering systems for roadside plants on dividers. The system integrates smart sensors, real-time data processing, and cloud computing to optimize resource utilization and reduce manual intervention. The proposed solution enhances sustainability, reduces operational costs, and contributes to a cleaner and greener urban environment.
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Asad, Zakia, Mohammad Asad Rehman Chaudhry, and David Malone. "Greener Data Exchange in the Cloud: A Coding-Based Optimization for Big Data Processing." IEEE Journal on Selected Areas in Communications 34, no. 5 (2016): 1360–77. http://dx.doi.org/10.1109/jsac.2016.2520245.
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MAMBO, ABDULHAMEED, ONYEBUCHI MOGBO, ADEBISI BAMGBADE, ALHASSAN HARUNA, and LEKO HARUNA. "THE IMPACT OF CLOUD COMPUTING ON CONSTRUCTION PROJECT DELIVERY ABUJA NIGERIA." Nile Journal of Engineering and Applied Science 2, no. 2 (2025): 1. https://doi.org/10.5455/njeas.188528.
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Cloud computing is the delivery of computing services, such as storage, processing power, and software applications, via the internet. Cloud computing offers various advantages and opportunity to construction industry, such as cost reduction, improving quality, enhancing productivity, and fostering innovation. Nigeria is lagging behind in this technological advancement due to lack of awareness, resistant to knowledge, which lead to lack of empirical evidence on the impact of cloud computing on project delivery and performance in Nigeria. The adoption and application of cloud computing in the construction industry is still low and limited in Nigeria. This study is aim at filling the gap by conducting a comprehensive study on the concept, adoption, impact, and perceive benefits of cloud computing in construction project delivery in Nigeria. To achieve this, mix-method of analysis was used, to asses, identify, determine, and evaluate the level of adoption of cloud computing in construction industry in Nigeria. From the findings, the level of adoption of cloud computing in the construction industry in Nigeria was moderate. The most adopted cloud service model was software as a service SaaS, followed by platform as a service, PaaS and infrastructure as a service IaaS. The most adopted cloud deployment model was public cloud, followed by hybrid cloud and private cloud. The factors that influenced the adoption and usage of cloud computing were organizational factors, individual factors, and environmental factors. Cloud computing has a positive and significant impact on the project delivery, in terms of time, cost, quality, and sustainability, as it enables the project managers to deliver the project faster, cheaper, better, and greener, by using the cloud services and solutions that suit their project needs and demands. The study recommend, The increase of the awareness and understanding of cloud computing among the construction project site managers, and to provide them with the necessary training and education, to enhance their knowledge and skills of cloud computing, and to increase their confidence . Keywords: Cloud computing, Empirical Evidence,Project Delivery
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Yang, Dong, Qidong Liu, Jia Li, and Yongji Jia. "Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability." Sustainability 12, no. 18 (2020): 7733. http://dx.doi.org/10.3390/su12187733.
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Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and sustainability into cloud manufacturing to produce a much greener product has become an urgent issue since there is fierce market competition and an increasing environment consciousness from customers. In this paper, we present a multi-objective optimization approach to selecting and scheduling cloud manufacturing services from the viewpoints of the economy and environment including carbon emissions and water resource. Subject to the carbon cap regulation, a multi-objective model for a cloud manufacturing task is built with the aim of minimizing total costs, carbon emissions, and water resource use. Transportation mode selections and carbon emissions from both cloud manufacturing services and transportation activities are taken into account in this model. The ε-constraint method is employed to obtain the exact Pareto front of optimal solutions. A case study from automobile cloud manufacturing is used to illustrate the effectiveness of the presented approach. Numerical experiments are conducted to compare the presented approach and the simple additive weighting method. The results show that the presented ε-constraint method can obtain a better and more diverse Pareto set of solutions and that it can solve the models in a reasonable time.
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Oluwafemi Oloruntoba. "Green cloud computing: AI for sustainable database management." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 3242–57. https://doi.org/10.30574/wjarr.2024.23.3.2611.
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The exponential growth of digital data has intensified the demand for cloud computing resources, leading to increased energy consumption and environmental concerns. Traditional cloud data centers operate at high energy levels, contributing significantly to carbon emissions and escalating operational costs. Green Cloud Computing (GCC) has emerged as a sustainable solution that integrates energy-efficient technologies, renewable energy sources, and artificial intelligence (AI) to optimize cloud infrastructure. By leveraging AI-driven algorithms, sustainable database management in GCC enhances resource allocation, workload distribution, and predictive maintenance, reducing the overall energy footprint while maintaining performance efficiency. This study explores the role of AI in sustainable database management within the GCC framework, focusing on intelligent workload scheduling, dynamic resource provisioning, and energy-efficient data storage techniques. AI-driven optimization models, such as reinforcement learning and deep learning-based predictive analytics, enable real-time adaptation to fluctuating workloads, ensuring minimal energy wastage. Furthermore, techniques like deduplication, compression, and auto-scaling enhance data storage efficiency while reducing redundancy. However, implementing AI for green cloud management presents challenges, including computational overhead, data security risks, and the need for regulatory compliance. Through an in-depth analysis of case studies and industry best practices, this research highlights how AI-driven sustainable database management can balance environmental responsibility with high-performance computing. The findings advocate for a holistic approach, combining AI innovations, regulatory frameworks, and green infrastructure investments to achieve carbon-neutral cloud ecosystems. By adopting AI-powered sustainability strategies, cloud providers and enterprises can significantly reduce their carbon footprint, ensuring a greener and more resilient digital future.
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Yang, Ming-Jeng, Chin-Lin Kuo, and Yao-Ming Yeh. "Dynamic Rightsizing with Quality-Controlled Algorithms in Virtualization Environments." International Journal of Grid and High Performance Computing 3, no. 2 (2011): 29–43. http://dx.doi.org/10.4018/jghpc.2011040103.
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Virtualization and partitioning are the means by which multiple application instances can share and run multiple virtual machines supported by a platform. In a Green Cloud environment, the goal is to consolidate multiple applications onto virtual machines associated by fewer servers, and reduce cost and complexity, increase agility, and lower power and cooling costs. To make Cloud center greener, it is beneficial to limit the amount of active servers to minimize energy consumption. This paper presents a precise model to formulate the right-sizing and energy-saving mechanism, which not only minimizes energy consumption of the server but also maintains a service quality through the Mt/M/Vt strategy of queuing theory. The authors map the complicated formula of the energy-saving mechanism to an approximation equation and design the fast decidable algorithms for calculating the right size of virtual machines in constant time complexity for power management systems.
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Sudheer Chennuri, Rajesh Kumar Srirangam, and Vasudev Pendyala. "Building a Greener Future : The Role of Data Centers in Eco-Friendly Computing." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 369–80. http://dx.doi.org/10.32628/cseit241051021.
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This comprehensive article explores the critical issue of environmental sustainability in the rapidly evolving landscape of information technology, focusing on the substantial environmental impact of data centers. It delves into innovative strategies and technologies to create more sustainable and eco-friendly computing environments. The article covers key areas such as energy efficiency, renewable energy integration, green cloud computing, edge computing, circular economy practices, and AI-powered optimization. By examining these aspects, the article demonstrates how the IT industry is working to minimize its environmental footprint while meeting the growing demands of our digital world. It highlights not only the environmental benefits of these initiatives but also their economic advantages, showcasing how sustainable practices in computing can lead to significant cost savings and competitive advantages in the market.
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Zain, N. N. M., N. K. Abu Bakar, S. Mohamad, and N. Md Saleh. "Optimization of a greener method for removal phenol species by cloud point extraction and spectrophotometry." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 118 (January 2014): 1121–28. http://dx.doi.org/10.1016/j.saa.2013.09.129.
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Jiang, Fuu-Cheng, and Ching-Hsien Hsu. "Fault-tolerant system design on cloud logistics by greener standbys deployment with Petri net model." Neurocomputing 256 (September 2017): 90–100. http://dx.doi.org/10.1016/j.neucom.2016.08.134.
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Idir, Yasmine, Idriss Moumen, Jaafar Abouchabaka, and Najat Rafalia. "Enhancing IoT Data Integrity and Effectiveness through hybrid Compression Method: A Step Towards Energy Efficiency." E3S Web of Conferences 477 (2024): 00042. http://dx.doi.org/10.1051/e3sconf/202447700042.
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The expansion of the Internet of Things (IoT) has magnified the challenge of managing data generated by IoT devices, notably in meteorological applications like temperature and humidity monitoring. This research addresses the imperative of efficiently reducing IoT data volume while preserving data integrity and underscores the significant implications for energy consumption. Our approach involved a two-fold strategy, employing the DHT11 sensor and ESP32 microcontroller for data collection, followed by an exploration of various data compression algorithms: delta encoding, run-length encoding (RLE), variable-length integer encoding (VLI), and bit-packing. The strategic combination of RLE and delta encoding yielded an exceptional compression rate of 98%. Beyond data reduction, this methodology offers energy savings by minimizing data transmission times, evidenced by the swift 133-microsecond compression process. Furthermore, the seamless transmission of compressed IoT data to Azure Cloud not only reduced cloud storage costs but also optimized storage space, contributing to energy efficiency. This research illuminates the significance of data compression in mitigating the environmental impact of IoT technologies, fostering a greener, more energy-conscious future.
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Alagirisamy, Mukil, Sagar Shinde, Pranav Nikumbh, Karan Jadhav, and Nikita Jadhav. "Enhancing sustainability and energy conservation in Indian railway tunnels through innovative lighting control and predictive maintenance." International Journal of Innovative Research and Scientific Studies 8, no. 2 (2025): 674–84. https://doi.org/10.53894/ijirss.v8i2.5288.
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The proposed innovation presents an innovative method for optimizing energy consumption and maintenance in railway tunnel operations, responding to the urgent need for sustainable and cost-effective infrastructure. This solution utilizes advanced geofencing technology integrated with a powerful cloud-based automation platform to guarantee accurate control of tunnel illumination and infrastructure management. The technology optimizes illumination levels in real-time according to train location, speed, and movement patterns, thereby minimizing energy waste, substantially lowering operational costs, and improving safety standards. The cloud platform functions as a single hub for overseeing and administering the tunnel's essential infrastructure, facilitating the seamless integration of real-time diagnostics, predictive maintenance algorithms, and automatic anomaly detection. In cases of system abnormalities, the platform enables swift remote intervention, hence ensuring system dependability and reducing downtime. This holistic strategy not only tackles the urgent operating issues of railway systems but also aligns with long-term sustainability objectives by fostering energy conservation and minimizing environmental impact. This breakthrough enhances operational efficiency and dependability, providing a transformative solution for contemporary railway networks and facilitating the development of greener, more intelligent transportation systems.
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Research Scholar, Shubhi Yadav,, and Prince Jaiswal, Assistant Professor. "Transition to a Greener Era with Industry 5.0 and Sustainability." SMS Journal of Entrepreneurship & Innovation 10, Issue - 2 (2024): 60–70. http://dx.doi.org/10.21844/smsjei.v10i02.30006.
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Industry 5.0, also known as the Fifth Industrial Revolution, is a brand-new stage of industrialization that isnow in developing phase. In this stage, enhanced workplace operations are performed with the use ofartificial intelligence and new industrial technology. This is paired with a greater emphasis on sustainabilityand pliancy; it also includes the strategy that places greater attention on people. In initial phase of theIndustrial Revolution 5.0, manpower was gradually replaced by automated processes that were powered bysteam and hydropower. In the second stage, mass manufacturing was made possible by the introduction ofelectricity. In the third stage, programmable computers and a strong digital ecosystem arose. The potentialof big data, cloud computing, robotics, the Internet of Things (IoT), and artificial intelligence was harnessedin smart factories as a component of the previous industrial revolution (Industry 4.0). Important questionsthose were left unanswered during these consecutive advancements are addressed by Industry 5.0, Are weundervaluing human resources at this point in the automation process? We did not decide to completelyautomate the industry since we found that individuals still play an important part in the revolution. Is itpossible to reconcile productivity and the environment with technology? Resilience, sustainability, andhuman-centeredness serve as the cornerstones around which Industry 5.0 is constructed. It is able toaccomplish its objectives thanks to these pillars. The plan of action for industry 5.0 driven environmentalresponsibility that this research has accomplished ought to deliver an expanded comprehension of how thenewest version of industry 5.0 can help to environmentally friendly growth. It describes how eachcomponents development should be carried out in an instrument that maximizes their combined influenceand involvement with future sustainability values. The data that has been collected through questionnairesand a range of secondary sources indicates that industry 5.0 with human-machine collaboration has a brightfuture and findings shows that productivity rises and can lead to a greener future through the use oftechnologies.
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Bhardwaj, Rakhi, R. Padmavathy, M. Preetha, et al. "EMS for Sustainable Data Centers." E3S Web of Conferences 591 (2024): 01006. http://dx.doi.org/10.1051/e3sconf/202459101006.
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With the rapid expansion of cloud computing, data centers have become one of the largest consumers of energy globally. To achieve energy efficiency and sustainability, integrating renewable energy sources such as solar and wind into the energy management system (EMS) of data centers has become essential. This paper proposes an intelligent EMS framework designed for sustainable data centers, which dynamically balances energy loads between renewable energy generation, battery storage, and grid supply. The system optimizes energy consumption by leveraging real-time data from renewable sources, minimizing grid dependency, and reducing overall operational costs. A simulation over a 24-hour period demonstrates that the EMS can significantly reduce grid power usage while maintaining data center load demands, thereby supporting greener and more efficient data center operations.
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Akavaram, Sravanthi. "Optimizing AI Performance at Scale: A FLOPs-Centric Framework for Efficient Deep Learning." European Journal of Computer Science and Information Technology 13, no. 31 (2025): 10–22. https://doi.org/10.37745/ejcsit.2013/vol13n311022.
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This framework introduces a novel approach for designing, measuring, and optimizing AI models through a FLOPs-centric methodology, enabling scalable deep learning with reduced computational and energy overhead. By analyzing model architecture, hardware utilization, and training efficiency, the framework supports both cloud-scale and edge AI deployments. Through comprehensive profiling, dynamic scaling, and computation-aware training, the system addresses efficiency challenges across vision, NLP, and multimodal models without compromising accuracy. The environmental impact assessment component provides organizations with tools to quantify and reduce the carbon footprint of AI workloads. Key innovations include a FLOPs-first design philosophy, granular profiling capabilities, FLOPs-aware loss formulations, and integrated benchmarking metrics that unify performance and efficiency considerations, contributing to greener, more sustainable AI development practices.
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Sim Wei Jie, Danial, Haslina Arshad, Siok Yee Tan, and Nur Fazidah Elias. "THE SMART WASTE MANAGEMENT SYSTEM OF SOLID WASTE MANAGEMENT IN UNIVERSITY CAMPUS." Journal of Information System and Technology Management 6, no. 22 (2021): 71–87. http://dx.doi.org/10.35631/jistm.622007.
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It has been a challenge for Malaysia to handle solid waste management for more than a decade. The increase in population size has led to an increase in the waste amount contributed by Malaysians. Due to that, proper solid waste management is essential in protecting the environment. There are many ways to execute solid waste management but developing countries may find it challenging to find the best way to dispose of waste efficiently. The same phenomenon seems to happen on university campuses as well. This article aims to propose a smart waste management system for university campuses. Most of the related work concentrates on detecting bin levels, despite making it a full-fledged system that comprises several modules regardless of web or mobile platform. A prototype of a smart bin is proposed, and this paper discusses its architecture and functionality. A web-based user application is also proposed in this article. With these components, the system enables the user to obtain information on the bins around them and can help the management to manage solid waste more efficiently. With the help of the Internet of Things and Cloud services, the system can achieve a greener and more sustainable campus environment. The proposed system will also be enhanced further to increase the awareness among the university students to move forward to a greener campus.
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Mohammed, Mostafa Abdulghafoor, Munef Abdullah Ahmed, and Abdullayev Vugar Hacimahmud. "Data-Driven Sustainability: Leveraging Big Data and Machine Learning to Build a Greener Future." Babylonian Journal of Artificial Intelligence 2023 (May 11, 2023): 17–23. http://dx.doi.org/10.58496/bjai/2023/005.
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Environmental challenges like climate change and resource depletion necessitate sustainable solutions that balance present and future needs. Advanced information technologies offer immense potential for confronting these issues via data-driven intelligence. This paper explores frameworks harnessing big data and machine learning (ML) to promote ecological sustainability across contexts like energy, agriculture, conservation and resilience. First, we review existing literature establishing this domain as an emerging transdisciplinary field. Next, we propose an architectural pipeline encompassing: (i) multi-modal data acquisition from sensors, surveys and satellites; (ii) preprocessing via cleaning, integration and transformation; (iii) application of supervised algorithms for prediction and unsupervised techniques for pattern discovery tailored to sustainability objectives; (iv) cloud-based model operationalization. Through sample use cases on optimizing renewables forecasting, boosting efficiency of infrastructure systems and monitoring ecosystems, we demonstrate analytical versatility. However, challenges around bias, transparency and scale necessitate ethical governance. Nonetheless, prudent development of specialized ML solutions offers sociotechnical instruments for evidence-driven sustainability planning and impactful interventions promoting resilience and welfare globally. This research aims to advance computational sustainability by outlining conceptual foundations, architectures and directions for real-world deployments of artificial intelligence that align with ecological priorities for current and upcoming generations worldwide.
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PS, VAIBHAV. "Carbon Emissions in Cloud Computing: Challenges and Opportunities for Sustainability." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34159.
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In the land of the unreal cloud computing, where bits go on unseen routes, the grim truth manifests itself to the abundant marvels in among the virtuals; carbon emissions. The following concerns the novel problem of greenhouse gas collection within cloud computing - it finds the most hidden source of pollution and building blocks behind this digital paradox. From the humming data centers, crammed with complex meshes of hardware and processes drawing massive amounts of energy, and the kaleidoscope of sources power these huge fiends, an actual stage is created to allow emissions to appear on it in silence. And in the midst of this complexity, even if we do not see very clearly where we are heading towards solutions, there is some light, making us capable for what comes. Source is the right word. The alchemist of innovation, from the energy saving squeezing to the resource sharing singing, all found the soil. Transparency thus turns out to be a steering light amid the obscurity and leads stakeholders towards the trajectories of greener spheres as carbon accounting frameworks show the way. Coordination pivots to the focal point linking stakeholders in the sector as well as the policy and advocacy networks. This weft and waft forms a device that corresponds to the structure of collective action. Hence, beneath the infinitude of the cloud, the two play a dance together where the advanced technology and environmental preservation work together to pave the way of a future that celebrates resilience and sustainability.
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Khalid, Adia, Sheraz Aslam, Khursheed Aurangzeb, Syed Haider, Mahmood Ashraf, and Nadeem Javaid. "An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid." Energies 11, no. 12 (2018): 3500. http://dx.doi.org/10.3390/en11123500.
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An unprecedented opportunity is presented by smart grid technologies to shift the energy industry into the new era of availability, reliability and efficiency that will contribute to our economic and environmental health. Renewable energy sources play a significant role in making environments greener and generating electricity at a cheaper cost. The cloud/fog computing also contributes to tackling the computationally intensive tasks in a smart grid. This work proposes an energy efficient approach to solve the energy management problem in the fog based environment. We consider a small community that consists of multiple smart homes. A microgrid is installed at each residence for electricity generation. Moreover, it is connected with the fog server to share and store information. Smart energy consumers are able to share the details of excess energy with each other through the fog server. The proposed approach is validated through simulations in terms of cost and imported electricity alleviation.
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Gupta, Pooja, and Vijay Kumar Jain. "Interpretive Structural Modeling of GIoT enablers." Journal of Information Technology Research 13, no. 2 (2020): 129–40. http://dx.doi.org/10.4018/jitr.2020040108.
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Recent emerging developments and enthusiastic adoption of technology are leading towards a smarter world but have also led to an increase in carbon traces. The Green Internet of Things (G-IoT) has been widely promoted as a strategy to make environment greener, safer and more sustainable. The authors investigate and discuss different enabling technologies (smart objects, ICT, Cloud Computing, etc.) that can be cleverly deployed to attain G-IoT. This research article is an effort to build a structural model of different enablers, vital to implement G-IoT. An array of enablers of G-IoT accomplishment has been recognized from literature review and experts' opinions. After a number of brainstorming sessions, contextual relationships have been identified among these enablers. In addition to this, enablers have been categorized based upon the driving power and dependence. Further, a structural model of G-IoT enablers has also been developed by means of Interpretive Structural Modeling (ISM) procedure. A total of nine enablers have been acknowledged from the literature and experts' opinions.
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Ruiu, Andrea, Cécile Bouilhac, Olinda Gimello, et al. "Synthesis and Phase Behavior of a Platform of CO2-Soluble Functional Gradient Copolymers Bearing Metal-Complexing Units." Polymers 14, no. 13 (2022): 2698. http://dx.doi.org/10.3390/polym14132698.
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The synthesis and characterization of a platform of novel functional fluorinated gradient copolymers soluble in liquid and supercritical CO2 is reported. These functional copolymers are bearing different types of complexing units (pyridine, triphenylphosphine, acetylacetate, thioacetate, and thiol) which are well-known ligands for various metals. They have been prepared by reversible addition–fragmentation chain-transfer (RAFT) polymerization in order to obtain well-defined gradient copolymers. The copolymers have been characterized by proton nuclear magnetic resonance (1H-NMR) spectroscopy, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, thermal gravimetric analysis (TGA), dynamical scanning calorimetry (DSC) and cloud point measurements in dense CO2. All the investigated metal-complexing copolymers are soluble in dense CO2 under mild conditions (pressure lower than 30 MPa up to 65 °C), confirming their potential applications in processes such as metal-catalyzed reactions in dense CO2, metal impregnation, (e.g., preparation of supported catalysts) or metal extraction from various substrates (solid or liquid effluents). Particularly, it opens the door to greener and less energy-demanding processes for the recovery of metals from spent catalysts compared to more conventional pyro- and hydro-metallurgical methods.
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Kechagias, Evripidis P., Sotiris P. Gayialis, Grigorios D. Konstantakopoulos, and Georgios A. Papadopoulos. "An Application of an Urban Freight Transportation System for Reduced Environmental Emissions." Systems 8, no. 4 (2020): 49. http://dx.doi.org/10.3390/systems8040049.
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Today, there is a great need for greener urban freight transportations due to their ever-increasing environmental impact. The planet’s climate has been significantly affected as the temperature is constantly rising and extreme weather events are occurring more and more often. Aiming to reduce the environmental impact of freight transportation in urban areas, an advanced vehicle routing and scheduling system for improving urban freight transportations, has been developed. This paper presents the functionality of the advanced system, while also analyzing its subsystems and demonstrating its use in a case study. The system is provided as an integrated cloud-based software to support the needs of logistics companies, in order to efficiently schedule their deliveries and perform the routing of their vehicles. The utilized multi-objective algorithm produces solutions that minimize either the distribution cost or the environmental emissions or a combination of these parameters. An application of the system is performed for validation purposes, concerning the comparison of the system’s results with corresponding real-life data provided by a medium-sized logistics company. The results of the testing reveal its significant contribution to the reduction of the environmental impact of the company’s distribution services.
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Hernández, Daniel, José M. Cecilia, Juan-Carlos Cano, and Carlos T. Calafate. "Flood Detection Using Real-Time Image Segmentation from Unmanned Aerial Vehicles on Edge-Computing Platform." Remote Sensing 14, no. 1 (2022): 223. http://dx.doi.org/10.3390/rs14010223.
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With the proliferation of unmanned aerial vehicles (UAVs) in different contexts and application areas, efforts are being made to endow these devices with enough intelligence so as to allow them to perform complex tasks with full autonomy. In particular, covering scenarios such as disaster areas may become particularly difficult due to infrastructure shortage in some areas, often impeding a cloud-based analysis of the data in near-real time. Enabling AI techniques at the edge is therefore fundamental so that UAVs themselves can both capture and process information to gain an understanding of their context, and determine the appropriate course of action in an independent manner. Towards this goal, in this paper, we take determined steps towards UAV autonomy in a disaster scenario such as a flood. In particular, we use a dataset of UAV images relative to different floods taking place in Spain, and then use an AI-based approach that relies on three widely used deep neural networks (DNNs) for semantic segmentation of images, to automatically determine the regions more affected by rains (flooded areas). The targeted algorithms are optimized for GPU-based edge computing platforms, so that the classification can be carried out on the UAVs themselves, and only the algorithm output is uploaded to the cloud for real-time tracking of the flooded areas. This way, we are able to reduce dependency on infrastructure, and to reduce network resource consumption, making the overall process greener and more robust to connection disruptions. Experimental results using different types of hardware and different architectures show that it is feasible to perform advanced real-time processing of UAV images using sophisticated DNN-based solutions.
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Enobong Hanson, Chukwuebuka Nwakile, Yetunde Adenike Adebayo, and Andrew Emuobosa Esiri. "Conceptualizing digital transformation in the energy and oil and gas sector." Global Journal of Advanced Research and Reviews 1, no. 2 (2023): 015–30. http://dx.doi.org/10.58175/gjarr.2023.1.2.0059.
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Digital transformation in the energy and oil & gas sector is increasingly pivotal for optimizing operations, enhancing efficiency, and ensuring alignment with industry standards. This abstract explores how the integration of digital technologies—such as real-time data analytics, cloud-based systems, and automation—can revolutionize energy and oil & gas operations. By leveraging real-time data analytics, companies can monitor and adjust production processes with precision, leading to reduced operational downtime and optimized resource management. Cloud-based systems provide a scalable platform for storing and processing large volumes of data, facilitating better decision-making through enhanced collaboration and access to insights across multiple sites. Automation technologies streamline repetitive tasks, improving operational safety and minimizing human error, which contributes to cost savings and increased productivity. This study presents a conceptual framework for deploying digital tools to achieve operational excellence in the sector. The framework emphasizes aligning digital initiatives with core operational goals, ensuring compliance with industry standards, and fostering a data-driven culture to support continuous improvement. Additionally, it highlights the role of predictive maintenance in reducing equipment failures by analyzing historical data, thereby preventing costly unplanned downtimes. The paper also addresses the importance of cybersecurity measures in safeguarding digital infrastructure, given the increased exposure to cyber threats in a more interconnected operational environment. Ultimately, the integration of these digital technologies not only enhances efficiency but also supports sustainability efforts by optimizing energy use, reducing emissions, and advancing the shift towards greener energy solutions. By examining case studies and best practices, this abstract underscores the transformative potential of digitalization in driving long-term success and competitive advantage for energy and oil & gas companies.
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Iyya, Zayyani, Saidu Rabiu Saidu, Najib Mohmmed, Momoh Shaibu, and Bilyaninu Ahemed Rufai. "SUSTAINABLE PRODUCTION OF BIO-LUBRICANTS FROM NEEM SEED OIL USING EGGSHELL-DERIVED CALCIUM OXIDE CATALYST." International Journal of Trendy Research in Engineering and Technology 08, no. 05 (2024): 42–49. http://dx.doi.org/10.54473/ijtret.2024.8508.
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Power and energy are prerequisites for material driving. Nowadays in developing countries (Nigeria), more work needs to be done to find inexpensive and environmentally benign energy fuels to replace home and vehicle lubricants. This will signal a shift away from the use of fossil fuel-based products and toward cleaner, greener products as an alternative to the current energy system. The process of turning Neem seed oil into biolubricant is being studied in this work. Additionally, calcium oxide was produced by calcining egg shells and then activated using a solution of phosphoric acid. TGA and FT-IR were used to characterize the activated calcium oxide that was generated. Using activated calcium oxide as a catalyst, methanol was also used to transesterify Neem seed oil (biodiesel), which was then transesterified once more using trimethylolpropane (TMP). The target Biolubricant was produced under reflux at various reaction conditions, and it was then characterized using FT-IR and GC-MS. The biolubricant's physicochemical properties were examined using techniques approved by the American Society for Testing and Materials (ASTM). The analysis of parameters like viscosity (53.2), cloud point (2.8), pour point (-8.7), flash point (218), and viscosity index (128) of the Biolubricant revealed results that were in line with the ASTM specification for lubricant.
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Prieto, Cristina, and Lourdes Calvo. "Performance of the Biocompatible Surfactant Tween 80, for the Formation of Microemulsions Suitable for New Pharmaceutical Processing." Journal of Applied Chemistry 2013 (July 14, 2013): 1–10. http://dx.doi.org/10.1155/2013/930356.
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The aim of this work was to investigate the phase behaviour and the structure of the n-hexane/water emulsions based on a nonionic, nontoxic and biocompatible surfactant, Tween 80. This system is of interest for new pharmaceutical techniques based on supercritical fluids to form nano- and encapsulated particles. However, it showed a lack of stability denoted by large areas of macroemulsion. For this reason, the effect of additives (alcohols and brine) and external variables (temperature) were explored. The replacement of water by brine caused negligible impact due to the nonionic character of Tween 80. On the contrary, the presence of an alcohol (ethanol or 1-butanol) enhanced the solubility of the surfactant in the oil phase and decreased the mixture viscosity, resulting in improved surface activity. Similar results were obtained by raising the temperature until the cloud point was reached (60°C). With these modifications, microemulsions at relatively low concentrations of surfactant (around 30%) and within a broad interval of compositions could be obtained, widening their possible use in pharmaceuticals manufacturing (such as controlled drug delivery, enzymatic reactions, or excipient processing). The understanding of the surfactant performance could be further used to substitute the n-hexane by a greener solvent, such as supercritical CO2.
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Msekelwa, Dr Patrick Zingisa. "Impact of AI on Education: Innovative Tools and Trends." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 5, no. 1 (2024): 227–36. http://dx.doi.org/10.60087/jaigs.v5i1.198.
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Every year, digital technologies appear in every industry. The new, developing technologies offer both advantages and disadvantages. The following are some recent examples of cutting-edge innovations in technology: data science, cybersecurity, block chain technology, artificial intelligence, machine learning, quantum learning, Internet of Things (IoT), 5G and 6G networks, hyper automation, cloud computing, robotics, and natural language processing. AL and ML combined with other cutting-edge, popular technologies have the potential to yield the positive outcomes and contribute to a greener future. Personalized medicine, drug development and predictive diagnostics using large scale data sets are all areas where machine learning might be beneficial to physicians. Students studying mechanical engineering must have a solid understanding of emerging trends such as autonomous vehicles. The potential of AV to create new, improved lifestyle and revolutionize urban planning and transportation has attracted a lot of interest. A research utilized a quantitative technique to further his research. A questionnaire was used to collect data from different participants, and 120 students from different fields in higher education sector were chosen at random. According to research, students who used popular technologies acquired more sophisticated abilities that will increase their output at work. Technology is always changing because it takes ongoing training to keep up with the latest development. The issue of the digital divide will be resolved by ongoing training.
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Arnold, Luc, François-Marie Bréon, and Simon Brewer. "The Earth as an extrasolar planet: the vegetation spectral signature today and during the last Quaternary climatic extrema." International Journal of Astrobiology 8, no. 2 (2009): 81–94. http://dx.doi.org/10.1017/s1473550409004406.
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AbstractThe so-called vegetation red-edge (VRE), a sharp increase in the reflectance around 700 nm, is a characteristic of vegetation spectra, and can therefore be used as a biomarker if it can be detected in an unresolved extrasolar Earth-like planet integrated reflectance spectrum. Here, we investigate the potential for the detection of vegetation spectra during the last Quaternary climatic extrema, the Last Glacial Maximum (LGM) and the Holocene optimum, for which past climatic simulations have been made. By testing the VRE detectability during these extrema, when Earth's climate and biomes maps were different from today, we are able to test the vegetation detectability on a terrestrial planet different from our modern Earth. Data from the Biome3.5 model have been associated to visible Global Ozone Monitoring Experiment (GOME) spectra for each biome and cloud cover to derive Earth's integrated spectra for given Earth phases and observer positions. The VRE is then measured. Results show that the vegetation remains detectable during the last climatic extrema. Compared to the current Earth, the Holocene optimum, with a greener Sahara, slightly increases the mean VRE on one hand, while on the other hand, the large ice cap over the northern hemisphere during the LGM decreases vegetation detectability. We finally discuss the detectability of the VRE in the context of recently proposed space missions.
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Reya, Nurzihan Fatema, Abtahi Ahmed, Tashfia Zaman, and Md Motaharul Islam. "GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing." Annals of Emerging Technologies in Computing 7, no. 3 (2023): 92–110. http://dx.doi.org/10.33166/aetic.2023.03.005.
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The increased use of software applications has resulted in a surge in energy demand, particularly in data centers and IT infrastructures. As global energy consumption is projected to surpass supply by 2030, the need to optimize energy consumption in programming has become imperative. Our study explores the energy efficiency of various coding patterns and techniques in Python, with the objective of guiding programmers to a more informed and energy-conscious coding practices. The research investigates the energy consumption of a comprehensive range of topics, including data initialization, access patterns, structures, string formatting, sorting algorithms, dynamic programming and performance comparisons between NumPy and Pandas, and personal computers versus cloud computing. The major findings of our research include the advantages of using efficient data structures, the benefits of dynamic programming in certain scenarios that saves up to 0.128J of energy, and the energy efficiency of NumPy over Pandas for numerical calculations. Additionally, the study also shows that assignment operator, sequential read, sequential write and string concatenation are 2.2 times, 1.05 times, 1.3 times and 1.01 times more energy-efficient choices, respectively, compared to their alternatives for data initialization, data access patterns, and string formatting. Our findings offer guidance for developers to optimize code for energy efficiency and inspire sustainable software development practices, contributing to a greener computing industry.
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Sahu, Abhishek. "Plant Care Guide App." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–8. https://doi.org/10.55041/ijsrem49286.
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Abstract: The Plant Care Guide App is a mobile-based intelligent solution designed to assist users in maintaining the health and well-being of their plants. With the growing interest in home gardening and sustainable living, many individuals lack the necessary knowledge and tools to properly care for their plants. This application aims to bridge that gap by offering a comprehensive, user-friendly platform that provides plant-specific care information, including watering schedules, sunlight exposure, soil preferences, and fertilization needs. The app incorporates advanced technologies such as TensorFlow Lite for plant image recognition, enabling users to identify unknown plant species and diagnose common health issues through photo uploads. By integrating Firebase Firestore, the app ensures real-time data storage and synchronization, while Firebase Cloud Messaging enables timely, personalized care reminders. The digital plant diary feature allows users to track growth progress and maintain a care history for each plant. Developed using Android Studio with Java/Kotlin, the app also utilizes Material Design principles to offer an intuitive and aesthetically pleasing interface. Overall, the Plant Care Guide App enhances plant care experiences, promotes environmental awareness, and supports healthier, greener lifestyles through the use of smart technology. . Keywords: Plant care, Image recognition, TensorFlow Lite, Firebase Firestore, Android Studio, Smart gardening, Plant health diagnosis, Mobile app development, Environmental awareness, Personalized reminders.
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Aldossary, Mohammad, Hatem A. Alharbi, and Nasir Ayub. "Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability." Mathematics 12, no. 17 (2024): 2627. http://dx.doi.org/10.3390/math12172627.
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As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R2 Score of 0.87. This validation of SARLDNet’s potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices.
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S, Nandhini. "An Innovative Approach of Economic Smart Aquaponics System." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2848–52. https://doi.org/10.22214/ijraset.2025.68728.
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Aquaponics is a sustainable method of food production that combines aquaculture and hydroponics to cultivate fish and plants in a symbiotic environment. This paper presents the development of an IoT-based smart aquaponics system aimed at automating and monitoring key environmental parameters to optimize plant and fish health. The system is powered by a NodeMCU microcontroller, which integrates various sensors including a DHT11 sensor for temperature and humidity measurement, and a water level sensor to monitor the plant bed's hydration. A self-operating pump system manages the transfer of nutrient-enriched water from the fish tank to the plant bed, triggered by live data collected from the sensors. The collected data is transmitted via Wi-Fi to the Ubidots IoT platform, enabling live monitoring through a cloud-based dashboard. This approach not only reduces manual intervention but also enhances resource efficiency and promotes sustainable urban farming. The system demonstrates a practical and scalable solution for smart agriculture, making it suitable for both research and realworld applications. This system is designed to reduce the need for manual effort by automating key functions involved in aquaponics management. By streamlining routine tasks such as water circulation and environmental monitoring, the setup helps improve efficiency and reliability. Moreover, it promotes sustainable farming by making use of affordable, easy-to-deploy hardware components. The project illustrates how practical and cost-effective technology can support smarter, greener agricultural methods suitable for both small-scale and larger operations.
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Pasek, Anne. "On Being Anxious About Digital Carbon Emissions." Social Media + Society 9, no. 2 (2023): 205630512311779. http://dx.doi.org/10.1177/20563051231177906.
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This essay examines how many scholars—including myself—are thinking and feeling about growing concerns about the climate impacts of digital networks. Whether in news headlines, civil society reports, or peer presentations, we increasingly encounter alarming figures that link streaming video and cloud storage practices with a potential carbon time bomb. As a result, an eclectic range of personal behaviors have blossomed that seek to acknowledge and respond to these potential harms, including digital land-energy acknowledgements, low-res aesthetics, conspicuous non-consumption, and media arts attempts to prefigure greener futures online. These digital environmental actors may lack a clear account of the relative impacts of a given gesture, but are nevertheless motivated by a strong sense of urgency and responsibility to modify the means by which they communicate online. I have been both a scholar of, and participant in, this panoply of low-carbon digital experiments. In tracing how my thinking has evolved, I seek to provide a self-reflexive assessment of what we might be responding to through these practices and what the role of climate anxiety is or should be in guiding such efforts. While remaining sympathetic to these behavioral shifts, I explore how an emphasis on discrete actions could risk misapprehending the material character of the digital systems we seek to change, overattributing both responsibility and agency to users. I conclude with some evolving criteria for assessing the environmental impacts of digital networks, as well as personal reflections on how the hermeneutics and practices of infrastructural care provides a productive alternative for thinking and action on the issue.
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A, SHILPA, and Ms Rajeshwari. "Next-Generation Online EV Charging Slot Management Framework." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43534.
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With the rapid adoption of Electric Vehicles (EVs), the need for an efficient and robust charging infrastructure has become essential to support the growing demand for E-Mobility services. However, the current EV charging stations face several challenges, including long waiting times, charging delays, uneven charge scheduling, and an unequal distribution of charging stations. These issues are particularly prevalent during peak hours, leading to increased queues and extended waiting times for EVs. To address these challenges, this project aims to design a comprehensive framework for an advanced online EV charging slot booking system. The proposed system leverages a stochastic queuing model to optimize the charging process at stations. By formulating an objective function that considers charging time, cost, queuing delay, and distance, the system aims to minimize these inefficiencies while maximizing the overall user experience. Additionally, the project introduces a cloud-based Charging Station Management platform that will network and manage multiple charging stations, facilitating real-time charging forecasts and efficient scheduling. This server-based approach will reduce waiting times, improve resource allocation, and help prevent EVs from running out of battery on the road. Ultimately, the proposed system aims to provide a cost-effective, scalable, and user-friendly solution for optimizing EV charging infrastructure. KEYWORD: The Next-Generation Online EV Charging Slot Management Framework integrates key technologies like real-time availability tracking, dynamic pricing, and predictive demand modeling to optimize the user experience and enhance station efficiency. With AI-driven optimization algorithms, it ensures efficient allocation of resources, balancing energy load across the grid and integrating renewable energy sources for greener charging options
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Prof., Mohamed M. El Hadi 1. and Azza Monir Ismail 2. "A PROPOSED MODEL FOR IMPROVING PERFORMANCE AND REDUCING COSTS OF IT THROUGH CLOUD COMPUTING OF EGYPTIAN BUSINESS ENTERPRISES." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 6, February (2018): 01–07. https://doi.org/10.5281/zenodo.1413295.
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Information technologies are affecting the big business enterprises of todays from data processing and transactions to achieve the goals efficiently and effectively, affecting creates new business opportunities and towards new competitive advantage, service must be enough to match the recent trends of IT such as cloud computing. Cloud computing technology has provided all IT services. Therefore, cloud computing offers an alternative to adaptable with technology model current , creating reducing cost (Fixed costs and ongoing), the proliferation of high speed Internet connections through Rent, not acquisitions, cheaper powerful computing technology and effective performance. The public and private clouds are characterized by flexibility, operational efficiency that reduces costs improve performance. Also cloud computing generates business creativity and innovation resulted from collaborative ideas of users; presents cloud infrastructure and services; paving new markets; offering security in public and private clouds; and providing environmental impact regarding utilizing green energy technology. In this paper, the main concentrate the cloud computing.
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Chen, Jiaqing, Yindi Zhao, Congtang Meng, and Yang Liu. "Multi-Feature Aggregation for Semantic Segmentation of an Urban Scene Point Cloud." Remote Sensing 14, no. 20 (2022): 5134. http://dx.doi.org/10.3390/rs14205134.
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With the rapid development of cities, semantic segmentation of urban scenes, as an important and effective imaging method, can accurately obtain the distribution information of typical urban ground features, reflecting the development scale and the level of greenery in the cities. There are some challenging problems in the semantic segmentation of point clouds in urban scenes, including different scales, imbalanced class distribution, and missing data caused by occlusion. Based on the point cloud semantic segmentation network RandLA-Net, we propose the semantic segmentation networks RandLA-Net++ and RandLA-Net3+. The RandLA-Net++ network is a deep fusion of the shallow and deep features of the point clouds, and a series of nested dense skip connections is used between the encoder and decoder. RandLA-Net3+ is based on the multi-scale connection between the encoder and decoder; it also connects internally within the decoder to capture fine-grained details and coarse-grained semantic information at a full scale. We also propose incorporating dilated convolution to increase the receptive field and compare the improvement effect of different loss functions on sample class imbalance. After verification and analysis of our labeled urban scene LiDAR point cloud dataset—called NJSeg-3D—the mIoU of the RandLA-Net++ and RandLA-Net3+ networks is 3.4% and 3.2% higher, respectively, than the benchmark network RandLA-Net.
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Ustymenko, Victor, Volodymyr Ageiev, Mykola Nazarenko, Andrii Holyk, and Serhii Sosida. "CREATION AND EVOLUTION OF THE SYSTEM FOR VERIFYING VEHICLE COMPLIANCE WITH THE REQUIREMENTS OF THE ECMT GUIDE AND THE 1997 UN AGREEMENT." Avtoshliakhovyk Ukrayiny 4, no. 281 (2024): 70–78. https://doi.org/10.33868/0365-8392-2024-4-281-70-78.
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This study examines the creation and development of a System in Europe for verifying the compliance of the design and technical condition of freight wheeled vehicles with the requirements of the ECMT Resolutions/Guides within the framework of the ECMT multilateral quota. The main stages of the System’s development regarding the identification and implementation of the following aspects are defined: – the introduction of the “green lorry” concept for trucks/semi-trailer tractors meeting EURO I standards; – procedures for checking the roadworthiness of trucks and trailers; – validation authorities authorized to verify the roadworthiness of vehicles and issue ECMT certificates; – ECMT legal acts defining market rules, vehicle requirements, and administrative procedures within the ECMT quota, including the ECMT Multilateral Quota User Guides (Resolutions) of 2001, 2005, 2014, 2022, and 2026; – evolution of technical and environmental safety requirements from “greener and safe” to “EURO VI/6 safe” lorries in line with ECMT Guides. For classifying lorries within the ECMT quota based on environmental properties, new definitions — “ECMT vehicle clas-sification” and “ECMT vehicle category” — have been proposed. It has been concluded that the System in Europe was established between 1993 and 1995. The main steps in creating the Ukraine’s national System for checking wheeled vehicles involved in international road traffic have been studied: – the 1999 appointment of the Validation Authority responsible for vehicle roadworthiness verification and ECMT cer-tificate issuance; – the 2009 designation of the Executive body responsible for technical inspection of vehicles per the 1997 UN Agree-ment; – establishing a network of laboratories from 2006 to participate in vehicle checking; – consolidation of the vehicle verification System according to ECMT requirements with international technical inspec-tion per the 1997 UN Agreement; – implementation of modern methods for monitoring the vehicle inspection process — photo documentation, video surveillance, and monitoring of inspection documents; – introduction of information technologies and electronic databases for vehicle documents, inspection results, photo documents, video recordings, and electronic registers using servers and cloud storage; – ensuring the circulation of issued ECMT certificates throughout their life cycle. The authors of the work are finalizing the development of two standards that will improve the regulatory framework of the System Paths to enhance the efficiency and transparency of the System have been outlined, with the implementation of the 2026 Guide. Keywords: wheeled vehicle, ECMT multilateral quota, roadworthiness test, ECMT certificate, international technical inspection certificate.
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Pružinec, Filip, and Renata Ďuračiová. "A Point-Cloud Solar Radiation Tool." Energies 15, no. 19 (2022): 7018. http://dx.doi.org/10.3390/en15197018.
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Current software solutions for solar-radiation modeling in 3D focus on the urban environment. Most of the published tools do not implement methods to consider complex objects, such as urban greenery in their models or they expect a rather complex 3D mesh to represent such objects. Their use in an environment that is difficult to represent geometrically, such as vegetation-covered areas, is rather limited. In this paper, we present a newly developed solar-radiation tool focused on solar-radiation modeling in areas with complex objects, such as vegetation. The tool uses voxel representations of space based on point-cloud data to calculate the illumination and ESRA solar-radiation model to estimate the direct, diffuse, and global irradiation in a specified time range. We demonstrate the capabilities of this tool on a forested mountain area of Suchá valley in the Hight Tatra mountains (Slovakia) and also in the urban environment of Castle Hill in Bratislava (Slovakia) with urban greenery. We compare the tool with the r.sun module of GRASS GIS and the Area Solar Radiation tool of ArcGIS using point-cloud data generated from the digital-terrain model of Kamenistá valley in High Tatra mountains in Slovakia. The results suggest a higher detail of the model in rugged terrain and comparable results on smooth surfaces when considering its purpose as a 3D modeling tool. The performance is tested using different hardware and input data. The processing times are less than 8 min, and 8 GB of memory is used with 4 to 16 core processors and point clouds larger than 100,000 points. The tool is, therefore, easily usable on common computers.
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Bihari Nandan Pandey, Et al. "Deep Convolutional Neural Networks For Classification of Satellite Images." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2457–62. http://dx.doi.org/10.17762/ijritcc.v11i9.9314.
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Deep learning algorithms that can learn from image, video, audio, and text data are becoming more successful as hardware power increases. Given the effectiveness and benefits of deep learning in many domains with more data, architecture should see similar implications. This study examined textures using particular rather than overall images. The deep convolutional neural network model classified 4500 satellite photos of clouds, deserts, greenery, and water. The constructed model classified previously unused test data (675 images) with 0.97 accuracies for cloud images, 0.98 for desert images, 0.96 for green areas, and 0.98 for water bodies. Although cloud and desert photos and green and water body images are comparable, this textural success shows that it can detect, analyze, and classify architectural elements. Deep convolutional neural networks can recognize, analyze, and classify architectural materials and elements, enabling shape recognition among many data to help architects collect helpful information. Thus, it will provide more extensive data than manual data analysis, enabling more accurate decisions. Understanding deep convolutional neural network data categorization characteristics explains architectural design differences and similarities. This condition reveals the hidden relationship in designs, allowing architects to create unique designs..
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Ren, Jingzhi, Yongqiang Li, Huiyun Liu, Kanghong Li, Daoqian Hao, and Zhiyao Wang. "Analysis of Light Obstruction from Street Lighting in Road Scenes." Remote Sensing 15, no. 24 (2023): 5655. http://dx.doi.org/10.3390/rs15245655.
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As urban greenery improves and the ecological environment is continuously optimized, road facilities are also impacted to varying degrees. For example, as vegetation grows, it causes varying degrees of obstruction to the lighting facilities on the roads. This article is based on vehicle-mounted LiDAR data and focuses on the point cloud data characteristics of different objects. Using appropriate modeling techniques, it accurately models road surfaces, green belts, streetlights, and other objects. On the Lumion platform, this system creates a 3D visualization of road scenes and examines the interplay between objects and lighting space, analyzing lit areas. Leveraging the precise 3D spatial relationships found in point clouds, it determines the effective illumination area on the ground from streetlights after object obstruction, comparing it to the theoretical illumination area. This not only visualizes the road scene but also quantifies the lighting obstruction rate. Furthermore, it assesses the lighting conditions in road scenes based on illuminance distribution, offering scientific insights and suggestions for enhancing road lighting.
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Atrey, Ankita, Nikita Jain, and N. Ch S. N. Iyengar. "A Study on Green Cloud Computing." International Journal of Grid and Distributed Computing 6, no. 6 (2013): 93–102. http://dx.doi.org/10.14257/ijgdc.2013.6.6.08.
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Xia, Tianyu, Bing Zhao, Zheng Xian, and Jinguang Zhang. "How to Systematically Evaluate the Greenspace Exposure of Residential Communities? A 3-D Novel Perspective Using UAV Photogrammetry." Remote Sensing 15, no. 6 (2023): 1543. http://dx.doi.org/10.3390/rs15061543.
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The quantity and quality of green space (GS) exposure play an important role in urban residents’ physical and psychological health. However, the current framework for assessing GS quality is primarily based on 2-D remote sensing data and 2.5-D street-view images. Few studies have comprehensively evaluated residential community GSs from an overall 3-D perspective. This study proposes a novel systematic framework for evaluating the quantity and quality of residential GSs based on the generation of a high-resolution 3-D point cloud using Unmanned Aerial Vehicle (UAV)-digital aerial photogrammetry (DAP). Nine indices were proposed: green volume ratio, floor green volume index, green groups diversity index, vegetation diversity index, greenspace fragmentation, average vegetation colour distance, vegetation colour diversity, activity areas ratio, and green cohesion index of activity site. These metrics were calculated using the classified point clouds from four typical Chinese residential communities with different residential greenery types and population densities. The results showed that our method could quantitatively identify the differences in residential GS exposure within urban residential communities. For example, a residential community with a large plant distribution and rich greenery variations had higher greenspace volume ratio and vegetation diversity index values. Our findings suggest that this novel framework, employing cost-effective UAV-DAP, can clearly describe different GS attributes and characteristics, aiding decision-makers and urban planners in comprehensively implementing GS interventions to improve the residents’ quality of life.
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Westphall, Carlos Becker, and Sergio Roberto Villarreal. "PRINCIPLES AND TRENDS IN GREEN CLOUD COMPUTING." Revista Eletrônica de Sistemas de Informação 12, no. 01 (2013): 7. http://dx.doi.org/10.5329/resi.2013.1201007.
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