Jul 2026· International Journal of Science, Strategic Management and Technology· 0 citations
Abstract
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.
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
An intelligent Metaverse communication infrastructure that integrates 6G wireless networks, Multi-access Edge Computing, Software-Defined Networking (SDN), Network Function Virtualization (NFV), AI-driven resource management, and blockchain-enabled security to optimize communication performance in immersive environments is presented.
Sheelam Abhiram, N. Vishnuvardhan, P. K. Reddy· International Journal of Sci...· 0 citations
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
The increasing adoption of intelligent Android applications in areas such as healthcare, smart transportation, mobile commerce, and personalized services has created significant demand for secure, efficient, and low-latency computing solutions. Traditional cloud-based architectures often face challenges related to communication delays, bandwidth consumption, and user privacy exposure. To address these limitations, this paper proposes a Federated Learning-Based Secure Mobile Edge Computing (FLS-MEC) framework for intelligent Android applications. The proposed framework integrates federated learning with mobile edge computing to enable collaborative model training without transferring sensitive user data to centralized servers. Security mechanisms including secure aggregation, differential privacy, and trust-based validation are incorporated to protect model updates and enhance system reliability. The framework supports efficient edge intelligence by reducing computational latency and communication overhead while maintaining high prediction accuracy. Experimental evaluation demonstrates that the proposed approach significantly improves privacy preservation, resource utilization, and overall system performance compared to conventional cloud-centric solutions. The results indicate that the FLS-MEC framework provides a scalable, secure, and intelligent environment for deploying next-generation Android applications in dynamic mobile computing ecosystems.
Bhargava Chowdary Musunuru· 2026 International Conferenc...· 0 citations
In mobile cloud computing (MCC), ensuring secure and energy-efficient data offloading remains a key challenge for constrained IoT devices. We propose a lightweight, threshold-based distributed encryption scheme combining CP-ABE with delegated symmetric encryption across multiple assisting edge servers. This architecture preserves data confidentiality and fine-grained access control while significantly reducing on-device computational overhead. We experimentally evaluate the proposed scheme using realistic parameters derived from a LoRaWAN-based sensor, considering Data-Owner-centric metrics including energy consumption, end-to-end encryption time, bandwidth overhead, throughput, and scalability. Results show energy savings exceeding 98% at the sensor side, at the cost of increased latency due to coordination and communication overhead. The proposed scheme enables scalable, fault-tolerant, and secure data sharing for IoT applications where real-time constraints are relaxed.
Yosra Ben Saied· ACM Transactions on Modeling...· 0 citations
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.
Mahabala H.N· International Journal of Eme...· 0 citations