Building integrated assessment model for IoT technology deployment in the Industry 4.0 DOI Creative Commons
Yasir Ali, Habib Ullah Khan, Faheem Khan

et al.

Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: Nov. 14, 2024

Internet of Things (IoT) platforms have become the building blocks any automated system but they are more important in case industrial systems where sensitive data captured and handled by information system. Therefore, it is imperative to deploy right IoT platform perform computational operational tasks a better way. During last few years, an array technologies/platforms with different capabilities features were introduced markets. This abrupt rise created selection decision-making issues particularly for network engineers, designers, managers due lack technical understanding skill this area. we present integrated assessment model focusing on evaluating ranking environment. It encompasses multiple methods such as proposed leverages well-known collection technique Delphi related criteria features. adopts Analytic Hierarchy Process (AHP) giving weights The Order Preference Similarity Ideal Solution (TOPSIS) method has been applied evaluation top twenty (20) Industrial IoT(IIoT) alternatives according criteria. selects most rational choice that can be employed Industry 4.0 setting. produces accurate consistent outcomes. Hence, believed used guideline stakeholders like researchers, developers, policymakers deployment first kind multi-methods mode assessment, decision-making, prioritization technologies industry domain.

Language: Английский

Mobile User Traffic Generation Via Multi-Scale Hierarchical GAN DOI Open Access
Tong Li, Shuodi Hui, Shiyuan Zhang

et al.

ACM Transactions on Knowledge Discovery from Data, Journal Year: 2024, Volume and Issue: 18(8), P. 1 - 19

Published: May 10, 2024

Mobile user traffic facilitates diverse applications, including network planning and optimization, whereas large-scale mobile is hardly available due to privacy concerns. One alternative solution generate data for downstream applications. However, existing generation models cannot simulate the multi-scale temporal dynamics in on individual aggregate levels. In this work, we propose a hierarchical generative adversarial (MSH-GAN) containing multiple generators multi-class discriminator. Specifically, usage behavior exhibits mixture of patterns, which are called micro-scale patterns modeled by different pattern our model. Moreover, users strong clustering characteristics, with co-existence similar behaviors. Thus, model each cluster as class discriminator’s output, referred macro-scale clusters. Then, gap between clusters bridged introducing switch mode generators, describe switching patterns. All share generators. contrast, only shared specific users, structure massive users. Finally, urge MSH-GAN learn via combined loss function, loss, aggregated regularity terms. Extensive experiment results demonstrate that outperforms state-of-art baselines at least 118.17% critical fidelity usability metrics. observations show can

Language: Английский

Citations

21
Generative AI for Cyber Security: Analyzing the Potential of ChatGPT, DALL-E, and Other Models for Enhancing the Security Space DOI Creative Commons
Siva Sai,

Utkarsh Yashvardhan,

Vinay Chamola

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 53497 - 53516

Published: Jan. 1, 2024

This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact cyber threats have continued rise today's world. ever-evolving threat landscape poses challenges for organizations security professionals who continue looking better solutions tackle these threats. GAI technology provides an effective way them address issues automated manner with increasing efficiency. It enables work on more critical aspects which require human intervention, while systems deal general situations. Further, can detect novel malware threatening situations than humans. feature GAI, when leveraged, lead higher robustness system. Many tech giants like Google, Microsoft etc., are motivated by this idea incorporating elements their make efficient dealing tools Google Cloud Security Workbench, Copilot, SentinelOne Purple come into picture, leverage develop straightforward robust ways emerging perils. With advent domain, one also needs take account limitations drawbacks that such have. some periodically giving wrong results, costly training, potential being used malicious actors illicit activities etc.

Language: Английский

Citations

20
An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy DOI Creative Commons

Roqia Rateb,

Ahmed Adnan Hadi,

Venkata Mohit Tamanampudi

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 29, 2025

Today, with the increasing use of Internet Things (IoT) in world, various workflows that need to be stored and processed on computing platforms. But this issue, causes an increase costs for resources providers, as a result, system Energy Consumption (EC) is also reduced. Therefore, paper examines workflow scheduling problem IoT devices fog-cloud environment, where reducing EC MakeSpan Time (MST) main objectives, under constraints priority, deadline reliability. order achieve these combination Aquila Salp Swarm Algorithms (ASSA) used select best Virtual Machines (VMs) execution workflows. So, each iteration ASSA execution, number VMs are selected by ASSA. Then using Reducing (RMST) technique, MST reduced, while maintaining reliability deadline. Then, VM merging Dynamic Voltage Frequency Scaling (DVFS) technique output from RMST, static dynamic respectively. Experimental results show effectiveness proposed method compared previous methods.

Language: Английский

Citations

3
Techniques employed in distributed cognitive radio networks: a survey on routing intelligence DOI
Rahul Priyadarshi, Ravi Kumar, Ying Zhang

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: April 10, 2024

Language: Английский

Citations

6
A novel approach for energy consumption management in cloud centers based on adaptive fuzzy neural systems DOI
Hongwei Huang, Yu Wang,

Yue Cai

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(10), P. 14515 - 14538

Published: July 21, 2024

Language: Английский

Citations

4
AIoT-enabled service prioritization for mobile nodes using enhanced PMIPv6 extension protocol DOI
Habib Ullah Khan, Anwar Hussain, Shah Nazir

et al.

Peer-to-Peer Networking and Applications, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 17, 2025

Language: Английский

Citations

0
A new framework to assess the impact of new IT-based technologies on the success of quality management system DOI Creative Commons

Yiying Cao,

Farah Qasim Ahmed Alyousuf

Journal Of Big Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 20, 2025

Language: Английский

Citations

0
QoS-aware resource management in cloud computing based on fuzzy meta-heuristic method DOI

Guiling Long,

Shaorong Wang,

Cong Lv

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 26, 2025

Language: Английский

Citations

0
Towards an efficient scheduling strategy based on multi-objective optimization in fog environments DOI

Guangli Nie,

Elaheh Rezvani

Computing, Journal Year: 2025, Volume and Issue: 107(3)

Published: March 1, 2025

Language: Английский

Citations

0
A New Energy‐Aware Technique for Designing Resource Management System in the 5G‐Enabled Internet of Things Based on Kohonen's Self‐Organizing Neural Network DOI

Yan Zou,

Qun Cao, Habibeh Nazif

et al.

Transactions on Emerging Telecommunications Technologies, Journal Year: 2025, Volume and Issue: 36(3)

Published: March 1, 2025

ABSTRACT The Internet of Things (IoT) has accelerated the connectivity between physical objects and Internet. It become common to integrate IoT devices into our lifestyles, considering fact that they make traditional be more intelligent self‐sufficient. usage 5G‐enabled can one such improvement, as it integrates multiple allows for effective interaction data sharing. However, with growing extreme increase in number being connected, resource utilization efficiency emerged major challenge. Comparing existing management strategies current environment brought by even complex IoT, former have consistently failed, leading wastage too much energy. Resource allocation efficient IoTs encompass processing power, bandwidth, energy appropriate functioning networks. conventional designs are inherently inefficient cannot match pace nature structures, hence making difficult achieve any meaningful performance, resources also wasted process; thus, there exists necessity energy‐efficient approaches adaptable dynamic workloads. In consideration aforementioned factors, this paper proposes an entirely new approach employing a Kohonen neural network address issue focus on efficiency. first these steps is collection obtained from order detect important features; second step algorithm produce map indicating spatial distribution resources, final real‐time modification incoming promote allocation. analysis shows when using method provided, energy, costs, delays implementation process improved.

Language: Английский

Citations

0