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

Journal of Network and Computer Applications, Год журнала: 2024, Номер 227, С. 103891 - 103891

Опубликована: Апрель 28, 2024

Язык: Английский

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Resource allocation of industry 4.0 micro-service applications across serverless fog federation

Future Generation Computer Systems, Год журнала: 2024, Номер 154, С. 479 - 490

Опубликована: Янв. 21, 2024

Язык: Английский

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An Intelligent LoRaWAN-Based IoT Device for Monitoring and Control Solutions in Smart Farming Through Anomaly Detection Integrated With Unsupervised Machine Learning

IEEE Access, Год журнала: 2024, Номер 12, С. 119072 - 119086

Опубликована: Янв. 1, 2024

Язык: Английский

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Efficient evolutionary modeling in solving maximization of lifetime of wireless sensor healthcare networks

Soft Computing, Год журнала: 2023, Номер 27(16), С. 11853 - 11867

Опубликована: Июнь 14, 2023

Язык: Английский

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Maximizing healthcare security outcomes through AI/ML multi-label classification approach on IoHT devices

Health and Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 8, 2025

Язык: Английский

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A Blockchain Framework for Scalable, High-Density IoT Networks of the Future

Sensors, Год журнала: 2025, Номер 25(9), С. 2886 - 2886

Опубликована: Май 3, 2025

The Internet of Things has transformed industries, cities, and homes through a vast network interconnected devices. As the IoT expands, number devices is projected to reach tens billions, generating massive amounts data. This growth presents significant data storage, management, security challenges, especially in large-scale deployments such as smart cities industrial operations. Traditional centralized solutions struggle handle high volume heterogeneity data, while ensuring real-time processing interoperability. paper design, development, evaluation blockchain framework tailored for secure storage management generated by Our introduces efficient methods managing, transmitting, securing packets within blockchain-enabled network. proposed uses gateway node aggregate multiple into single transactions, increasing throughput, optimizing bandwidth, reducing latency, simplifying retrieval, improving scalability. results obtained from rigorous analysis testing evaluated scenarios show that achieves level performance, scalability, efficiency robust being able integrate large flexible manner.

Язык: Английский

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Machine Learning and Deep Learning Algorithms for Green Computing

Advances in computational intelligence and robotics book series, Год журнала: 2024, Номер unknown, С. 1 - 23

Опубликована: Фев. 27, 2024

Green computing is an innovative approach to making computer systems environmentally friendly, energy-efficient, and low in carbon emissions. It uses advanced techniques from machine learning deep optimize real-time resource allocation, reducing energy consumption. This enhances workload patterns methods like convolutional recurrent neural networks enhance architectural efficiency. The integration of ML DL allows for accurate temperature forecasting alternative cooling strategies. Despite challenges, the synergistic fusion algorithmic software with green holds great promise consumption enhancing environmental sustainability.

Язык: Английский

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Design of load-aware resource allocation for heterogeneous fog computing systems

PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e1986 - e1986

Опубликована: Апрель 18, 2024

The execution of delay-aware applications can be effectively handled by various computing paradigms, including the fog computing, edge and cloudlets. Cloud offers services in a centralized way through cloud server. On contrary, paradigm dispersed manner providing computational facilities near end devices. Due to distributed provision resources paradigm, this architecture is suitable for large-scale implementation applications. Furthermore, reduction delay network load as compared architecture. Resource distribution balancing are always important tasks deploying efficient systems. In research, we have proposed heuristic-based approach that achieves consumption delays efficiently utilizing according generated clusters nodes. algorithm considers magnitude data produced at while allocating resources. results evaluations performed on different scales confirm efficacy achieving optimal performance.

Язык: Английский

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ERAM-EE: Efficient resource allocation and management strategies with energy efficiency under fog–internet of things environments

Connection Science, Год журнала: 2024, Номер 36(1)

Опубликована: Май 6, 2024

Due to technological advancements, most devices are generating a significant amount of data which needs appropriate technology handle the generated by IoT devices. Fog computing addresses this challenges in decentralised manner. This paper proposes an efficient resource allocation and management strategies with energy efficiency (ERAM-EE) effectively allocate available resources Fog-enabled networks. The ERAM-EE algorithm utilises channel gain matrix interconnected network assign nodes (FNs) through blocks (RBs) three stages. In initial stage, one FN is assigned each device single RB calculating maximum value gain. subsequent remaining RBs unassigned FNs for future task-offloading processes. Finally, allocated Fog–IoT Simulated results indicate that scheme confirms mapped minimum effective task scheduling management. Analysis reveals method achieved increase EE up 7, 8, 18 Mbit/J compared existing schemes varying devices, respectively.

Язык: Английский

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Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

Frontiers in Computer Science, Год журнала: 2023, Номер 5

Опубликована: Дек. 14, 2023

Fog Computing has emerged as a pivotal technology for enabling low-latency, context-aware, and efficient computing at the edge of network. Effective task scheduling plays vital role in optimizing performance fog systems. Traditional algorithms, primarily designed centralized cloud environments, often fail to cater dynamic, heterogeneous, resource-constrained nature nodes. To overcome these limitations, we introduce sophisticated machine learning-driven methodology that adapts allocation ever-changing environment's conditions. Our approach amalgamates K-Means clustering algorithm enhanced with fuzzy logic, robust unsupervised learning technique, efficiently group nodes based on their resource characteristics workload patterns. The proposed method combines capabilities K-means adaptability logic dynamically allocate tasks By leveraging techniques, demonstrate how can be intelligently allocated nodes, resulting reducing execution time, response time network usage. Through extensive experiments, showcase effectiveness our dynamic environments. Clustering proves time-effective identifying groups jobs per virtual (VM) efficiently. model evaluate approach, have utilized iFogSim. simulation results affirm showcasing significant enhancements reduction, minimized utilization, improved when compared existing non-machine methods within iFogSim framework.

Язык: Английский

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