Edge computing task scheduling method based on user’s social relations: a construction and solution for Smart City Library DOI Creative Commons

Yun Teng,

Zijia Liu

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2457 - e2457

Published: Oct. 31, 2024

In the realm of development a Smart City Library, integration robust edge computing is vital. The research suggests novel task-scheduling model for computing, leveraging user’s social relationships. Analyzing these connections involves constructing relationship graph by implementing mathematical convolution and Jaccard similarity ratio. This precise quantification ties ensures secure reliable task scheduling. An equipment connection user service also crafted based on Euclidean distance, aligning scheduling with device-to-device (D2D) communication conditions. Combining device-service device creates task-device bipartite graph. On other hand, calculation execution cost weight determination finalize model. Implementing proposed method utilizing Kuhn–Munkres (KM) algorithm demonstrates positive impacts, which are few delays less energy consumption, For instance, when threshold score changes from 02. To 0.6, total delay time increases 23 to 32, best compared algorithms. approach strengthens security reliability while decreasing consumption. advances Libraries, promising transformative implications.

Language: Английский

Edge AI for Internet of Energy: Challenges and perspectives DOI
Yassine Himeur, Aya Nabil Sayed, Abdullah Alsalemi

et al.

Internet of Things, Journal Year: 2023, Volume and Issue: 25, P. 101035 - 101035

Published: Dec. 15, 2023

Language: Английский

Citations

29
MuHoW: Distributed protocol for resource sharing in collaborative edge-computing networks DOI Creative Commons
Joaquin Alvarez‐Horcajo, Isaías Martínez-Yelmo, Elisa Rojas

et al.

Computer Networks, Journal Year: 2024, Volume and Issue: 242, P. 110243 - 110243

Published: Feb. 10, 2024

The incorporation of end devices in the edge-to-cloud continuum yields substantial benefits to conventional cloud computing frameworks, expediting communication between and computational resources, resulting new use cases, particularly field mobile networks. However, few related works leverage full potential resource sharing at far edge, as most proposals require that nodes rely on a higher-capacity node. This manuscript presents Multi-Hop Wireless Resource Sharing Protocol (MuHoW), lightweight protocol tailored for multi-hop wireless MuHoW enables within collaborative edge-computing networks by facilitating discovery neighbours subsequently establishing confluence tree directed towards edge/fog infrastructure. serves conduit aggregating essential information, ensuring establishment seamless environment. empirical findings highlight scalability MuHoW, due its linear control message growth with network size. Moreover, efficiency is very high even lossy environments evidenced fact messages are successfully delivered expected.

Language: Английский

Citations

5
Scalable and energy-efficient task allocation in industry 4.0: Leveraging distributed auction and IBPSO DOI Creative Commons

Qingwen Li,

Tang Wai Fan,

Siew-Kei Lam

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0314347 - e0314347

Published: Jan. 16, 2025

Industry 4.0 has transformed manufacturing with the integration of cutting-edge technology, posing crucial issues in efficient task assignment to multi-tasking robots within smart factories. The paper outlines a unique method decentralizing auctions handle basic tasks. It also introduces an improved variant Binary Particle Swarm Optimization (IBPSO) algorithm manage complicated tasks that require multi-robot collaboration. main contributions we make are: design auction decentralization (AOCTA) which allows for and flexible distribution dynamic contexts, optimization coalition formation complex jobs by using IBPSO improves efficiency energy decreases cost computation as well thorough simulations show our proposed significantly surpasses conventional methods efficiency, completion rates terms usage, rate, scaling system. This research contributes development through providing effective solution aligns sustainability objectives addresses operational environmental impacts. Addressing challenges posed allocation distributed systems, these advanced technologies provide comprehensive solution, facilitating evolution innovative systems.

Language: Английский

Citations

0
A Flawless QoS Aware Task Offloading in IoT Driven Edge Computing System using Chebyshev Based Sand Cat Swarm Optimization DOI Creative Commons

V. V. R. Maheswara Rao,

Shiva Shankar Reddy,

N Silpa

et al.

Journal of Grid Computing, Journal Year: 2025, Volume and Issue: 23(1)

Published: Jan. 20, 2025

Language: Английский

Citations

0
Distributed Optimisation of Mobile Robots in a Mobile Edge Computing Environment DOI

Dejing Zhang

Smart innovation, systems and technologies, Journal Year: 2025, Volume and Issue: unknown, P. 221 - 234

Published: Jan. 1, 2025

Language: Английский

Citations

0
A novel task offloading model for IoT: enhancing resource utilization with actor-critic-based reinforcement learning DOI

G. Saranya,

K Kumaran,

M. Vivekanandan

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 17, 2025

Language: Английский

Citations

0
Efficient Task Offloading and Resource Allocation in MEC DOI Open Access

Yinghui Yang,

Qunting Yang

Journal of Cases on Information Technology, Journal Year: 2025, Volume and Issue: 27(1), P. 1 - 22

Published: March 22, 2025

This paper proposes a novel optimization method for task offloading in Multi-Access Edge Computing (MEC) environments. The combines Ant Colony Optimization (ACO) and Genetic Algorithms (GA) to minimize total execution latency. ACO explores the solution space potential optimal solutions, while GA refines these solutions through evolutionary processes. Simulation experiments validate effectiveness of this approach, showing significant reductions overall latency compared conventional single-algorithm methods. also discusses key factors influencing strategies, providing practical insights real-world deployments. proposed hybrid ACO-GA strategy offers high-efficiency adaptable allocation problem MEC, enhancing system's performance quality.

Language: Английский

Citations

0
Intelligent Resource Orchestration for 5G Edge Infrastructures DOI Creative Commons
Rafael Moreno‐Vozmediano, Rubén Montero, Eduardo Huedo

et al.

Future Internet, Journal Year: 2024, Volume and Issue: 16(3), P. 103 - 103

Published: March 19, 2024

The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands latency-sensitive and data-intensive applications. This research paper presents comprehensive study on intelligent orchestration computing infrastructures. proposed Smart Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates ONEedge5G experimental component, which offers workload forecasting automation capabilities, for optimal allocation virtual resources across diverse locations. evaluated different models, based both traditional statistical techniques machine learning techniques, comparing their accuracy CPU usage prediction dataset machines (VMs). Additionally, integer linear programming formulation was to solve optimization problem mapping VMs physical servers distributed infrastructure. Different criteria such minimizing server usage, load balancing, reducing latency violations were considered, along with constraints. Comprehensive tests experiments conducted evaluate efficacy architecture.

Language: Английский

Citations

3
Scheduling in Fog, Edge and Cloud Computing: A Review DOI

Hadhemi Ben khalifa,

Sahar Kallel,

Ismael Bouassida Rodriguez

et al.

Lecture notes on data engineering and communications technologies, Journal Year: 2025, Volume and Issue: unknown, P. 390 - 398

Published: Jan. 1, 2025

Language: Английский

Citations

0
Adaptive heuristic edge assisted fog computing design for healthcare data optimization DOI Creative Commons

Syed Sabir Mohamed S,

R. Gopi,

Thiruppathy Kesavan

et al.

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

Published: Aug. 5, 2024

Patient care, research, and decision-making are all aided by real-time medical data analysis in today's rapidly developing healthcare system. The significance of this research comes the fact that it has ability to completely change system relocating computing resources closer source, hence facilitating more rapid accurate data. Latency, privacy concerns, inability scale common traditional cloud-centric techniques. With their process close where is created, edge fog have potential revolutionize analysis. industry unique opportunities problems for application computing. There must be an emphasis on security privacy, workload flexibility, interoperability, resource optimization, integration without any interruptions. In suggested Adaptive Heuristic Edge assisted Fog Computing design (AHE-FCD) solve these issues using a novel architecture meant improve Together, devices nodes may perform distributed processing analytics with help AHE-FCD. algorithms often employed optimization establishing optimum solution standard approaches difficult impossible. utilize search explore space identify result. Improved patient efficiency possible AHE-FCD real-time, low-latency at layers. minimal latency, high reliability, likely emerge from study's findings. As result, rather being centralized, operations sophisticated occur several end points. That helps situation quicker detect dangers prior propagate across network. promising breakthrough moves us realization advanced systems, prompt well-informed essential providing excellent healthcare.

Language: Английский

Citations

3