2025· International Journal of Emerging Trends in Multidisciplinary Research· Vol 8, pp. 01-18· 0 citations
TL;DR
This paper highlights key challenges, including interoperability, security, heterogeneous resource management, and sustainable computing, while demonstrating the potential of Edge–Cloud collaboration to improve computational efficiency, response time, energy efficiency, scalability, and privacy for next-generation smart applications.
Abstract
The rapid growth of IoT, 5G, AI, and cyber-physical systems has accelerated the development of smart applications that require low-latency, reliable, and intelligent computing. Traditional cloud computing faces challenges in meeting these demands due to latency, bandwidth, and privacy limitations. Intelligent Edge–Cloud collaboration addresses these issues by combining edge computing with cloud resources for efficient workload distribution, AI-driven resource management, and adaptive service orchestration. This paper reviews recent advances in collaborative architectures, distributed AI, intelligent orchestration, and resource optimization. It also highlights key challenges, including interoperability, security, heterogeneous resource management, and sustainable computing, while demonstrating the potential of Edge–Cloud collaboration to improve computational efficiency, response time, energy efficiency, scalability, and privacy for next-generation smart applications.
The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
Alan Bundy· International Journal of Mod...· 0 citations
This review examines emerging trends in Artificial Intelligence (AI)-driven resource management within this continuum, with a focus on three directions: the transition from centralized to distributed and collaborative intelligence, cross-domain adaptation and knowledge transfer for heterogeneous IoT applications, and the nascent integration of foundation models into edge environments.
Zhi-Yu Wang, Nilotpal Kapri, L. Bittencourt et al.· Frontiers in The Internet of...· 0 citations
This paper presented an Intelligent Edge Computing Framework for Secure, Low-Latency, and Energy-Efficient Smart Devices that integrates edge intelligence, adaptive task scheduling, resource-aware computation, secure communication, and cloud-assisted services to address the limitations of conventional cloud-centric architectures. By processing data closer to smart devices, the proposed framework significantly reduces latency, minimizes network bandwidth consumption, improves resource utilization, and enables faster real-time decision-making while ensuring data privacy and security. The experimental results demonstrate superior performance in terms of classification accuracy, ROC-AUC, Average Precision, execution time, and computational efficiency compared with existing cloud-based and edge computing approaches. The proposed framework provides a scalable, reliable, and energy-efficient solution for diverse Internet of Things (IoT) applications, including smart healthcare, industrial automation, intelligent transportation, and smart homes. Future work will focus on integrating federated learning, blockchain-enabled security, and next-generation 6G edge intelligence to further enhance scalability, privacy preservation, and autonomous decision-making in large-scale smart device ecosystems.
Kolipaka Vinay, Valusa Venkat Sai Kumar, D. A. Kumar· International Journal of Sci...· 0 citations
This review explores the recent approaches of the state of the art focused on service orchestration in IoT edge-cloud environments, concentrating on architectures and methodologies that enable resource allocation and service management.
: The combination of IoT (Internet of Things), edge computing, and AI (Artificial Intelligence) has brought intelligent and adaptive smart campuses. The present paper proposes an AI-based context-aware IoT that is based on the edge and cloud computing to simplify real-time campus operations, enhance the utilization of the available resources, and provide intelligent decision-making. The proposed architecture will address the main problem of current smart campus systems, such as high latency, reduced computational power at the edges, and absence of services integration. The architecture also supports edge computing, where local processing and context inference with AI are done on an edge node, and long-term optimization and cross-campus analysis, which is done by cloud computing through a hybrid approach of edge-cloud architecture. The architecture enables real time solutions to the services such energy management, safety monitoring and optimization of mobility of people, making the campus sustainable and adaptable to the requirements of dynamic users. A group of performance metrics, including latency, accuracy, energy efficiency, and scalability is used to test the recommended system. Experimentally, it is proved that the hybrid model is more responsive and resource-efficient when compared with the solutions based on clouds and edges only. This paper is a supplement to the smart campus systems by providing a scalable, adaptive, and efficient architecture that may be applied to different campus services. Future directions of this work will encompass enhancement of privacy saving mechanisms, integration of the digital twin technologies and a multi-campus architecture.
Abhijeet Kaiwade, Neeta Bendre· Proceedings of the 1st Inter...· 0 citations
This paper introduced an Intelligent Portable Edge – Cloud Computing Architecture (IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture.
Pradeep Kachakayala, Akshith Kachakayala· International Journal for Re...· 0 citations
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