Blockchain-Based Solutions Supporting Reliable Healthcare for Fog Computing and Internet of Medical Things (IoMT) Integration

Sustainability, Journal Year: 2022, Volume and Issue: 14(22), P. 15312 - 15312

Published: Nov. 18, 2022

The Internet of Things (IoT) has radically transformed how patient information and healthcare monitoring are monitored recorded revolutionized the area by ensuring regular 24 × 7 tracking without costly restricted human resources with a low mistake probability. Medical (IoMT) is subsection things that uses medical equipment as or nodes to enable cost-effective efficient recording. IoMT can cope wide range problems, including observing patients in hospitals, their homes, assisting consulting physicians nurses health conditions at intervals issuing warning signals if emergency care necessary. EEG signals, electrocardiograms (ECGs), blood sugar levels, pressure other be examined. In crucial situations, quick real-time analysis essential, failure provide careful attention fatal. A cloud-based IoT platform cannot handle these latency-sensitive conditions. Fog computing (FC) novel paradigm for assigning, processing, storing devices limited resources. Where substantial processing power storage required, all fog scheme delegate jobs local rather than forwarding them cloud module greater distance. Identifying potential security risks putting place adequate measures critical. This work aims examine blockchain (BC) tool mitigating impact difficulties conjunction computing. research shows overcome computing’s privacy concerns. It also discusses blockchain’s issues limitations from perspective IoMT.

Language: Английский

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Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS

Drones, Journal Year: 2022, Volume and Issue: 6(8), P. 193 - 193

Published: Aug. 2, 2022

Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made detect forest using variety of approaches, including optical fire sensors, and satellite-based technologies, all which unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is thriving field can be used successfully. This paper describes unique clustering approach that identifies presence zone in transfers sensed data base station as soon feasible via wireless communication. The department takes required steps prevent spread fire. It proposed this study an efficient deal with routing energy challenges extend lifetime unmanned aerial vehicle (UAV) case fires. Due restricted high mobility, directly impacts duration FANET nodes. As result, it vital enhance sensor (WSNs) maintain system availability. Our algorithm EE-SS regulates usage nodes while taking into account features disaster region other factors. For firefighting, placed throughout collect essential points for identifying dividing them distinct clusters. All cluster communicate their packets continually through head. When one another, transmission range constantly adjusted meet operating requirements. examines existing techniques detection approaches limitations. newly designed chooses most optimum heads (CHs) based fitness, reducing overhead increasing efficiency. method results from simulations compared such LEACH, LEACH-C, PSO-HAS, SEED. evaluation carried out concerning overall usage, residual energy, count live nodes, network lifetime, time build approaches. our outperforms considered state-of-art algorithms.

Language: Английский

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Efficient Middleware for the Portability of PaaS Services Consuming Applications among Heterogeneous Clouds

Sensors, Journal Year: 2022, Volume and Issue: 22(13), P. 5013 - 5013

Published: July 2, 2022

Cloud providers create a vendor-locked-in environment by offering proprietary and non-standard APIs, resulting in lack of interoperability portability among clouds. To overcome this deterrent, solutions must be developed to exploit multiple clouds efficaciously. This paper proposes middleware platform mitigate the application issue A literature review is also conducted analyze for portability. The allows an ported on various platform-as-a-service (PaaS) supports deploying different services disparate efficiency abstraction layer validated experimentation that uses message queue, Binary Large Objects (BLOB), email, short service (SMS) via proposed against same using these their native code. experimental results show adding mildly affects latency, but it dramatically reduces developer’s overhead implementing each make portable.

Language: Английский

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Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

Sustainability, Journal Year: 2022, Volume and Issue: 14(19), P. 11982 - 11982

Published: Sept. 22, 2022

As more people utilize the cloud, employment opportunities become available. With constraints such as a limited make-span, high utilization rate of available resources, minimal execution costs, and rapid turnaround time for scheduling, this becomes an NP-hard optimization issue. The number solutions/combinations increases exponentially with magnitude challenge, tasks computing making task scheduling problem NP-hard. result, achieving optimum user is difficult. An intelligent resource allocation system can significantly cut down costs waste resources. For instance, binary particle swarm (BPSO) was created to combat ineffective heuristic approaches. However, optimal solution will not be produced if these algorithms are paired additional or meta-heuristic algorithms. Due temporal complexity algorithms, they less useful in real-world settings. NP problem, variation PSO presented workload balancing cloud computing. Considering updating stated research, our objective function determines heterogeneous virtual machines (VMs) Phave most significant difference completion time. In conjunction load balancing, we developed method placements particles. According experiment results, proposed surpasses existing metaheuristic regarding work balancing. This level success has been attainable because application Artificial Neural Networks (ANN). ANN demonstrated promising outcomes distribution. accurate faster than multilayer perceptron networks at predicting targets.

Language: Английский

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Advancing green computing: Practices, strategies, and impact in modern software development for environmental sustainability

World Journal of Advanced Engineering Technology and Sciences, Journal Year: 2024, Volume and Issue: 11(1), P. 220 - 230

Published: Feb. 17, 2024

Advancing Green Computing: Practices, Strategies, and Impact in Modern Software Development for Environmental Sustainability explores the evolving landscape of green computing within realm software development, emphasizing imperative environmentally sustainable practices. In response to escalating environmental concerns, industry is undergoing a paradigm shift towards reducing its carbon footprint mitigating ecological impacts. This particularly crucial given pervasive influence on technological ecosystems. The review delves into multifaceted dimensions computing, elucidating various practices strategies that are instrumental fostering sustainability. From optimizing code efficiency embracing energy-efficient architectures, underscores diverse approaches available developers minimizing resource consumption emissions. Furthermore, it examines broader ramifications these practices, their potential reshape industry's contribute global efforts conservation. Moreover, highlights symbiotic relationship between modern development methodologies. It elucidates how principles such as agile DevOps can be synergistically integrated with enhance sustainability throughout lifecycle. By adopting an interdisciplinary approach integrates considerations design, deployment processes, organizations catalyze transformative changes greener ecosystem. also investigates tangible impact metrics. Through case studies empirical analyses, showcases efficacy energy consumption, emissions, electronic waste generation. Additionally, discusses economic societal benefits accrued from ranging cost savings enhanced corporate social responsibility. provides comprehensive overview context development. myriad opportunities challenges associated integrating

Language: Английский

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Resource allocation in Fog–Cloud Environments: State of the art

Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: 227, P. 103891 - 103891

Published: April 28, 2024

Language: Английский

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Can government digital transformation improve corporate energy efficiency in resource-based cities?

Energy Economics, Journal Year: 2024, Volume and Issue: 141, P. 108043 - 108043

Published: Nov. 13, 2024

Language: Английский

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Reviewing energy efficiency and environmental consciousness in the minerals industry Amidst digital transition: A comprehensive review

Resources Policy, Journal Year: 2024, Volume and Issue: 91, P. 104851 - 104851

Published: March 3, 2024

Language: Английский

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Towards an Energy Consumption Index for Deep Learning Models: A Comparative Analysis of Architectures, GPUs, and Measurement Tools

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 846 - 846

Published: Jan. 30, 2025

The growing global demand for computational resources, particularly in Artificial Intelligence (AI) applications, raises increasing concerns about energy consumption and its environmental impact. This study introduces a newly developed index that evaluates the efficiency of Deep Learning (DL) models, providing standardized adaptable approach various models. Convolutional neural networks, including both classical modern architectures, serve as primary case to demonstrate applicability index. Furthermore, inclusion Swin Transformer, state-of-the-art non-convolutional model, highlights adaptability framework diverse architectural paradigms. analyzes during training inference representative DL AlexNet, ResNet18, VGG16, EfficientNet-B3, ConvNeXt-T, trained on Imagenette dataset using TITAN XP GTX 1080 GPUs. Energy measurements are obtained sensor-based tools, OpenZmeter (v2) with integrated electrical sensors. Additionally, software-based tools such CarbonTracker (v1.2.5) CodeCarbon (v2.4.1) retrieve data from component results reveal significant differences across architectures GPUs, insights into trade-offs between model performance use. By offering flexible comparing this advances sustainability AI systems, supporting accurate evaluations applicable settings.

Language: Английский

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Optimizing the Design of TES Tanks for Thermal Energy Storage Applications Through an Integrated Biomimetic-Genetic Algorithm Approach

Biomimetics, Journal Year: 2025, Volume and Issue: 10(4), P. 197 - 197

Published: March 24, 2025

Building upon an experimentally validated bio-inspired thermal energy storage (TES) tank design, this study introduced a novel computational framework that integrated genetic algorithms (GA) with biomimetic principles to systematically generate TES geometries. Inspired by natural distribution patterns found in vascular networks, the AI-driven methodology explored 13 geometric parameters, focusing on branching structures and spatial distribution, resulted computationally generated designs 29% increase heat transfer surface area while maintaining manufacturability constraints within fixed diameter of 150 mm height 155 mm. Unlike previous studies relied predefined configurations, approach developed dimensional constraints, ensuring relevance allowing for broader structural exploration. The resulting exhibited key characteristics high-efficiency configurations providing systematic, scalable architecture. This represented first step integrating biomimicry into establishing structured generating high-performance, manufacturable configurations. While current work focused future research will emphasize experimental validation real-world implementation confirm practical benefits these AI-generated designs. By bridging gap between intelligence nature-inspired engineering, provided pathway developing more efficient, manufacturable, sustainable solutions applications.

Language: Английский

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