A Scalable Hybrid Edge-Cloud Approach to Minimizing Latency in IoT Applications DOI Open Access

P. Radhakrishnan,

Smitha Kurian,

V. Balaji Vijayan

и другие.

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(2)

Опубликована: Апрель 13, 2025

The increasing reliance on IoT applications demands efficient, scalable solutions to address latency, a critical factor in time-sensitive operations. Hybrid Edge-Cloud approaches leverage the strengths of both edge and cloud computing optimize performance ensure seamless connectivity. However, existing methods often struggle with excessive latency due resource allocation inefficiencies, limited device capabilities, network congestion. This study proposes model based Scalable Approach (SHECA) framework, designed mitigate these challenges applications. SHECA integrates for real-time data processing storage, advanced analytics, long-term decision-making. By dynamically distributing computational loads leveraging intelligent allocation, framework significantly reduces enhances system responsiveness. findings demonstrate that average by 35% compared traditional cloud-only methods, ensuring faster response times, scalability, improved user experience hybrid solution offers robust approach minimization diverse scenarios.

Язык: Английский

Edge and Cloud Computing in Smart Cities DOI Creative Commons
Μαρία Τρίγκα, Ηλίας Δρίτσας

Future Internet, Год журнала: 2025, Номер 17(3), С. 118 - 118

Опубликована: Март 6, 2025

The evolution of smart cities is intrinsically linked to advancements in computing paradigms that support real-time data processing, intelligent decision-making, and efficient resource utilization. Edge cloud have emerged as fundamental pillars enable scalable, distributed, latency-aware services urban environments. Cloud provides extensive computational capabilities centralized storage, whereas edge ensures localized processing mitigate network congestion latency. This survey presents an in-depth analysis the integration cities, highlighting architectural frameworks, enabling technologies, application domains, key research challenges. study examines allocation strategies, analytics, security considerations, emphasizing synergies trade-offs between paradigms. present also notes future directions address critical challenges, paving way for sustainable development.

Язык: Английский

Процитировано

0
SustAI-SCM: Intelligent Supply Chain Process Automation with Agentic AI for Sustainability and Cost Efficiency DOI Open Access
Batin Latif Aylak

Sustainability, Год журнала: 2025, Номер 17(6), С. 2453 - 2453

Опубликована: Март 11, 2025

Sustainable supply chain management (SCM) demands efficiency while minimizing environmental impact, yet conventional automation lacks adaptability. This paper presents SustAI-SCM, an AI-powered framework integrating agentic intelligence to automate tasks with sustainability in focus. Unlike static rule-based systems, it leverages a transformer model that continuously learns from operations, refining procurement, logistics, and inventory decisions. A diverse dataset comprising procurement records, logistics data, carbon footprint metrics trains the model, enabling dynamic adjustments. The experimental results show 28.4% cost reduction, 30.3% lower emissions, 21.8% improved warehouse efficiency. While computational overhead real-time adaptability pose challenges, future enhancements will focus on energy-efficient AI, continuous learning, explainable decision making. advances sustainable automation, balancing operational optimization responsibility.

Язык: Английский

Процитировано

0
EnterpriseAI: A Transformer-Based Framework for Cost Optimization and Process Enhancement in Enterprise Systems DOI Creative Commons

Shyam Bhaskaran

Computers, Год журнала: 2025, Номер 14(3), С. 106 - 106

Опубликована: Март 16, 2025

Coordination among multiple interdependent processes and stakeholders the allocation of optimal resources make enterprise systems management a challenging process. Even for experienced professionals, it is not uncommon to cause inefficiencies escalate operational costs. This paper introduces EnterpriseAI, novel transformer-based framework designed automate system management. transformer model has been customized reduce manual effort, minimize errors, enhance resource allocation. Moreover, assists in decision making by incorporating all independent variables associated with matter. All these together lead significant cost savings across organizational workflows. A unique dataset derived this study from real-world scenarios. Using transfer learning approach, EnterpriseAI trained analyze complex dependencies deliver context-aware solutions related systems. The experimental results demonstrate EnterpriseAI’s effectiveness, achieving an accuracy 92.1%, precision 92.5%, recall 91.8%, perplexity score 14. These represent ability accurately respond queries. scalability utilization tests reflect astonishing factors that significantly consumption while adapting demand. Most importantly, reduces enhancing flow business.

Язык: Английский

Процитировано

0
A Scalable Hybrid Edge-Cloud Approach to Minimizing Latency in IoT Applications DOI Open Access

P. Radhakrishnan,

Smitha Kurian,

V. Balaji Vijayan

и другие.

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(2)

Опубликована: Апрель 13, 2025

The increasing reliance on IoT applications demands efficient, scalable solutions to address latency, a critical factor in time-sensitive operations. Hybrid Edge-Cloud approaches leverage the strengths of both edge and cloud computing optimize performance ensure seamless connectivity. However, existing methods often struggle with excessive latency due resource allocation inefficiencies, limited device capabilities, network congestion. This study proposes model based Scalable Approach (SHECA) framework, designed mitigate these challenges applications. SHECA integrates for real-time data processing storage, advanced analytics, long-term decision-making. By dynamically distributing computational loads leveraging intelligent allocation, framework significantly reduces enhances system responsiveness. findings demonstrate that average by 35% compared traditional cloud-only methods, ensuring faster response times, scalability, improved user experience hybrid solution offers robust approach minimization diverse scenarios.

Язык: Английский

Процитировано

0