Aug 2026· International Journal of Advanced Artificial Intelligence Research· 0 citations
TL;DR
The analysis indicates that effective edge-cloud AI systems require adaptive workload placement, privacy-preserving distributed learning, security-aware inference, explainability, fault tolerance, and continuous resource optimization rather than simple physical distribution of computation.
Abstract
The rapid deployment of artificial intelligence (AI) across healthcare, industrial control, supply-chain management, and Internet of Medical Things (IoMT) environments has intensified the need for computing architectures that can simultaneously provide low-latency inference, scalability, security, and operational resilience. Conventional cloud-centric AI architectures offer substantial computational capacity but may introduce communication latency, bandwidth dependency, privacy exposure, and single-point operational dependencies. Edge-cloud integration addresses these limitations by distributing data processing and AI inference across resource-constrained edge nodes, intermediate fog layers, and centralized cloud infrastructures. This research and review paper examines the architectural principles required to develop resilient and real-time AI decision systems through intelligent edge-cloud integration. The study synthesizes the provided literature on fog-cloud security, federated learning, intrusion detection, machine learning, blockchain-enabled IoMT, serverless computing, and healthcare cybersecurity. A conceptual architecture is developed around five functional layers: data acquisition, edge intelligence, collaborative fog coordination, cloud intelligence, and resilient decision orchestration. The analysis indicates that effective edge-cloud AI systems require adaptive workload placement, privacy-preserving distributed learning, security-aware inference, explainability, fault tolerance, and continuous resource optimization rather than simple physical distribution of computation. The findings further indicate that federated and lightweight learning mechanisms can reduce centralized exposure, while fog-cloud coordination can improve responsiveness for latency-sensitive applications. However, heterogeneous hardware, communication failures, model synchronization overhead, adversarial threats, and resource constraints remain significant barriers. The paper positions intelligent edge-cloud integration as an architectural strategy in which resilience, security, and inference performance are jointly optimized rather than treated as independent system properties.
Findings indicate that compression and knowledge distillation can reduce communication burdens, while heterogeneous aggregation and adaptive learning mechanisms improve the practicality of distributed AI environments.
Doni Setiawan, Ditha Permata· International Journal of Com...· 0 citations
A research-driven conceptual framework for resilient edge-to-cloud AI architectures supporting distributed real-time decision making and identifies limitations associated with heterogeneous devices, uncertain ground truth, model drift, communication failures, and the absence of uniform evaluation criteria are identified.
Chinedu Eze, Fatima Bello· International Journal of Adv...· 0 citations
Real-time monitoring and smart decision-making are required in cyber-physical infrastructures such as smart grids, transportation systems, and industrial automation systems to ensure process efficiency and system resilience. However, higher latency, bandwidth constraints, and a lack of responsiveness to time-sensitive data streams haunt traditional cloud-based architectures. To address these challenges, a coherent framework for integrating AI-based cloud analytics with edge intelligent hardware for managing cyber-physical infrastructure is proposed in the following paper. The architecture is based on distributed edge nodes, with hardware accelerators for very low-latency inference, and cloud layers that perform large-scale analytics and optimisation of global models. A hybrid resource allocation strategy is a self-regulating plan for the allocation of computing workloads across cloud and edge environments. Also, an adaptive learning mechanism improves prediction accuracy in changing operational environments. The framework is mathematically designed to achieve optimal latency, throughput, and computational efficiency. The proposed system reduces latency by 145ms to 92 (≈36.5) units and increases prediction accuracy from 81.2% to 96.4% when applied to a dynamic workload. The convergence analysis shows that standardized quicker convergence occurs after 35 iterations as opposed to 60 iterations in the models at the baseline. Additionally, the throughput is proceeding at 520 requests to 780 requests and error rates are falling by a factor of around 41, ensuring better reliability. Relative performance analysis across various scenarios indicates consistent improvement in both edge-dominant and cloud-dominant setups. The results of this study demonstrate that, when combined with hardware-based edge intelligence, AI-powered cloud analytics can significantly enhance the responsiveness, scalability, and decision accuracy in cyber-physical infrastructure systems.
Naveen, Satyam Kumar Sainy· International Journal on Eng...· 0 citations
This paper explores hybrid cloud-edge infrastructures as a scalable solution for deploying AI in IIoT environments and presents an architectural framework that balances compute-intensive model training in the cloud with low-latency inference at the edge.
Jennifer Clark· International Journal of Mac...· 0 citations
The rapid expansion of the Internet of Things (IoT) has resulted in massive volumes of data being generated by interconnected devices across various domains. Traditional cloud-centric architectures often struggle with issues such as high latency, bandwidth constraints, and data privacy risks when processing this data. Edge computing has emerged as an effective solution by enabling data processing closer to the data source, thereby improving response time and reducing network dependency. In recent years, Generative Artificial Intelligence (GenAI) has gained significant attention for its ability to generate insights, predictions, and adaptive responses from complex and dynamic datasets. This paper examines the integration of Generative AI with IoT and edge computing to enhance intelligent edge systems capable of real-time analytics and autonomous decision-making. It explores architectural frameworks, potential applications in areas such as smart cities, healthcare, industrial automation, and autonomous systems, as well as the advantages of improved efficiency, scalability, and privacy preservation. Additionally, the paper discusses the technical challenges associated with deploying generative models at the edge, including resource constraints, model optimization, security, and data management. Finally, it outlines future research directions aimed at developing scalable, secure, and energy-efficient GenAI-enabled edge computing ecosystems.
Mr. L. S. Shendge, Mrs. S. N. Patel, Ms. D. V. Sharma· International Journal of Lat...· 0 citations
The growing dependence of governments, financial institutions, healthcare organizations, energy providers, transportation networks, and communication systems on “cloud-native” platforms has made it more important than ever that these digital services be delivered continuously and securely. Cloud-native architectures offer scalability, flexibility, and quick deployment using microservices, containers, orchestration, and DevSecOps practices, but they also present complex cybersecurity, operational, and supply chain threats to the country’s critical infrastructure and economic well-being. The existing research focuses on cloud resilience, artificial intelligence for IT operations (AIOps), cybersecurity or governance individually, causing approaches to be disjointed and suboptimal for critical service continuity during large-scale cyber incidents. In this research, the authors introduce the AI-powered Integrated Cyber-Economic Resilience (AICER) Framework, which is a conceptual framework that integrates cloud-native infrastructure, cybersecurity intelligence, AI-assisted operational analytics, secure software delivery, economic impact assessment, and governance into a comprehensive decision-support architecture. The framework is formulated on the basis of Design Science Research Methodology (DSRM) and supported by an integrative literature review encompassing the most recent literature, international cybersecurity standards, and cloud computing best practices. The proposed framework does not introduce new performance metrics but rather builds on existing metrics, such as Service Level Objectives (SLOs), availability and reliability metrics, Mean Time Between Failures (MTBF), Mean Time to Recovery (MTTR), DORA software delivery metrics, Common Vulnerability Scoring System (CVSS), Exploit Prediction Scoring System (EPSS), Software Levels for Supply Chain Security (SLSA), and NIST Cybersecurity Framework (CSF 2.0) and NIST AI Risk Management Framework (AI RMF). The framework also takes economic impact into account to inform decisions on recovery for nationally significant services. The proposed architecture provides a vision for a policy-aware and AI-driven approach to enhancing cyber resilience, bolstering critical infrastructure, and fortifying continuity of essential digital services. The study provides a strong foundation for further prototype implementation, experimental validation, and deployment in public and private critical sectors.
Unknown authors· American Journal of Innovati...· 0 citations