Cited by Multilabel Classification in IoT NIDS: A Proposed Cross Machine Learning Pipeline

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

Mohammad Zolghadri,

Parvaneh Asghari, Seyed Ebrahim Dashti

et al.

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

Published: April 28, 2024

Language: Английский

Citations

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

Razin Farhan Hussain,

Mohsen Amini Salehi

Future Generation Computer Systems, Journal Year: 2024, Volume and Issue: 154, P. 479 - 490

Published: Jan. 21, 2024

Language: Английский

Citations

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

Maram Fahaad Alumfareh,

Mamoona Humayun, Zulfiqar Ahmad

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 119072 - 119086

Published: Jan. 1, 2024

Language: Английский

Citations

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

Raja Marappan,

P A Harsha Vardhini,

Gaganpreet Kaur

et al.

Soft Computing, Journal Year: 2023, Volume and Issue: 27(16), P. 11853 - 11867

Published: June 14, 2023

Language: Английский

Citations

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

Alexandru A. Maftei, Adrian I. Petrariu, Valentin Popa

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2886 - 2886

Published: May 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.

Language: Английский

Citations

Machine Learning and Deep Learning Algorithms for Green Computing DOI

Rajashri Roy Choudhury,

Piyal Roy, Shivnath Ghosh

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 23

Published: Feb. 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.

Language: Английский

Citations

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

Syed Rizwan Hassan, Ateeq Ur Rehman, Naif Alsharabi

et al.

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e1986 - e1986

Published: April 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.

Language: Английский

Citations

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

Emmanuel Song Shombot,

Gilles Dusserre, Robert Bešťák

et al.

Health and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 8, 2025

Language: Английский

Citations

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

Muhammad Saad Sheikh,

Rabia Noor Enam,

Rehan Qureshi

et al.

Frontiers in Computer Science, Journal Year: 2023, Volume and Issue: 5

Published: Dec. 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.

Language: Английский

Citations

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

P. Prakasam,

R. Ujwala,

K. Srikar

et al.

Connection Science, Journal Year: 2024, Volume and Issue: 36(1)

Published: May 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.

Language: Английский

Citations