PAM: Predictive analytics and modules‐based computation offloading framework using greedy heuristics and 5G NR‐V2X DOI
Muhammad Ilyas Khattak, Hui Yuan, Ayaz Ahmad

et al.

Transactions on Emerging Telecommunications Technologies, Journal Year: 2024, Volume and Issue: 35(7)

Published: June 25, 2024

Abstract Recent advancements in distributed computing systems have shown promising prospects enabling the effective usage of many next‐generation applications. These applications include a wide range fields, such as healthcare, interactive gaming, video streaming, and other related technologies. Among solutions are evolving vehicular fog (VFC) frameworks that make use IEEE 3GPP protocols advanced optimization algorithms. However, these approaches often rely on outdated or computationally intensive mathematical techniques for solving representing their models. Additionally, some not thoroughly considered type application during evaluation validation phases. In response to challenges, we developed “predictive analytics modules” (PAM) framework, which operates time event‐driven basis. It utilizes up‐to‐date address inherent unpredictability VFC‐enabled required smart healthcare systems. Through combination greedy heuristic approach offloading architecture, PAM efficiently optimizes decisions task computation allocation. This is achieved through specialized algorithms provide support weaker devices, all within frame under 100 ms. To assess performance comparison three benchmark methodologies, pathways employed average time, probability density function, pareto‐analysis, algorithmic run complexity.

Language: Английский

Toward Secure and Trustworthy Vehicular Fog Computing: A Survey DOI Creative Commons
Ossama Nazih, Nabil Benamar, Hanane Lamaazi

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 35154 - 35171

Published: Jan. 1, 2024

The integration of fog computing in vehicular networks has led to significant advancements road safety, traffic control, entertainment, and comfort services. Vehicular Fog Computing (VFC) emerges as an optimistic solution, offering a pathway responsive service requests. VFC uses either onboard vehicle computers or vehicles infrastructure between the underlying cloud, addressing limitations centralized data processing traditional cloud computing. However, faces security vulnerabilities due open nature its deployment. In this survey, we explore threats confronting review existing solutions related their detection mitigation capabilities. This paper provides comprehensive overview foundational concept Computing, architectures, critical role various intelligent applications. Moreover, it spotlights trust concerns deploying real-time big analytics within environment. survey identifies pressing issues outlines potential research directions, insights for community address while designing secure architectures.

Language: Английский

Citations

10
A Joint Optimization of Resource Allocation Management and Multi-Task Offloading in High-Mobility Vehicular Multi-Access Edge Computing Networks DOI
Hong Min, Amir Masoud Rahmani,

Payam Ghaderkourehpaz

et al.

Ad Hoc Networks, Journal Year: 2024, Volume and Issue: 166, P. 103656 - 103656

Published: Sept. 6, 2024

Language: Английский

Citations

7
DRL-based Task Scheduling Scheme in Vehicular Fog Computing: Cooperative and mobility aware approach DOI
Mekala Ratna Raju, Sai Krishna Mothku, Manoj Kumar Somesula

et al.

Ad Hoc Networks, Journal Year: 2025, Volume and Issue: unknown, P. 103819 - 103819

Published: March 1, 2025

Language: Английский

Citations

0
PAM: Predictive analytics and modules‐based computation offloading framework using greedy heuristics and 5G NR‐V2X DOI
Muhammad Ilyas Khattak, Hui Yuan, Ayaz Ahmad

et al.

Transactions on Emerging Telecommunications Technologies, Journal Year: 2024, Volume and Issue: 35(7)

Published: June 25, 2024

Abstract Recent advancements in distributed computing systems have shown promising prospects enabling the effective usage of many next‐generation applications. These applications include a wide range fields, such as healthcare, interactive gaming, video streaming, and other related technologies. Among solutions are evolving vehicular fog (VFC) frameworks that make use IEEE 3GPP protocols advanced optimization algorithms. However, these approaches often rely on outdated or computationally intensive mathematical techniques for solving representing their models. Additionally, some not thoroughly considered type application during evaluation validation phases. In response to challenges, we developed “predictive analytics modules” (PAM) framework, which operates time event‐driven basis. It utilizes up‐to‐date address inherent unpredictability VFC‐enabled required smart healthcare systems. Through combination greedy heuristic approach offloading architecture, PAM efficiently optimizes decisions task computation allocation. This is achieved through specialized algorithms provide support weaker devices, all within frame under 100 ms. To assess performance comparison three benchmark methodologies, pathways employed average time, probability density function, pareto‐analysis, algorithmic run complexity.

Language: Английский

Citations

1