Jul 2026· International Conference Computing Methodologies and Communication· pp. 739-746· 0 citations· 11 references
Abstract
Mobile Cloud Computing (MCC) enhances resource-constrained mobile devices by enabling task offloading to edge and cloud environments, but faces challenges in security, scalability, and dynamic resource management. This study proposes a secure, blockchain-enabled federated deep learning framework integrated with Hierarchical Adaptive Scalable Spiking Reinforcement Learning (HASSRL) for intelligent and privacy-preserving task offloading. A hybrid optimization approach combining Revolution Optimization Algorithm (ROA) and Proximal Policy Optimization (PPO) is introduced to achieve adaptive scheduling, efficient resource allocation, and real-time decision-making. The framework incorporates continuous monitoring and a feedback-driven self-optimization mechanism to improve latency, energy consumption, and overall system performance. Experimental evaluation using standard datasets demonstrates that the proposed model achieves high accuracy and efficiency, making it suitable for scalable and real-time MCC applications in next-generation networks. The proposed Blockchain-enabled Federated Deep Learning with HASSRL and ROA–PPO achieved a latency of 48.689 ms, energy consumption of 63.222 mJ, and CPU utilization of 95.705%, while maintaining a throughput of 42.067 Mbps and task completion time of 2.781 s. Through experimental evaluation on the UNSW-NB15 and CSE-CIC-IDS2018 datasets, the framework demonstrated efficient task execution, improved resource utilization, and enhanced adaptability in dynamic MCC environments, ensuring secure, scalable, and real-time intelligent task offloading
Path planning for cloud based autonomous systems such as smart transportation, Internet of Things (IoT) installations and robot fleets need to be secure, energy efficient, time efficient and fulfil privacy constraints. Current reinforcement learning (RL) techniques mainly consider optimisation of single objective, centralised or loosely secured model updates which are susceptible to data poisoning, privacy breach and adversarial model updates. We present Blockchain enabled Energy and Time efficient Multi Objective Reinforcement Learning (BlockE2T MORL) a new decentralised approach for secure, cloud assisted path planning. BlockE2T MORL has three main components: (i) a dynamic multi objective reward function that reduces energy, travel time and security threat; (ii) a lightweight blockchain inspired trust mechanism that assigns continuous trust values to agents, and is incorporated in the reward function to punish dishonest or malicious agents; and (iii) a hybrid actor critic learning strategy that facilitates exploration and exploitation in dynamic environments. Unlike conventional blockchain systems, our approach incurs low computational overhead ([Formula: see text] instead of [Formula: see text] for validation) and operates without heavy consensus protocols. We evaluate BlockE2T-MORL on a simulated grid-based cloud environment with up to 100 agents and varying adversarial ratios. After 200 training episodes (5 independent runs), the proposed framework achieves: (i) energy consumption = 124.3 ± 8.7 J (23.4% reduction vs. standard RL, p < 0.01), (ii) latency = 45.2 ± 3.8 ms (17.4% improvement, p < 0.05), and (iii) trust score = 0.87 ± 0.04 (67% improvement, p < 0.001). The framework converges faster (210 ± 25 episodes in the final optimized configuration, compared with ≥ 520 episodes for baseline methods). BlockE2T-MORL offers a scalable, privacy-preserving, and computationally lightweight solution for next-generation intelligent path planning in cloud-based autonomous systems.
Revati Raman Dewangan, D. Thombre, Vivek Parganiha et al.· Scientific Reports· 0 citations
The Energy-Aware Hierarchical Green Fog framework is presented, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity, and task deadlines into hierarchical fog-cloud decision-making.
M. Harandi, Afshin Yaghoobi· Scientific Reports· 0 citations
The proposed OIBTO framework employs a lightweight Proof-of-Authority consensus within a two-tier architecture consisting of a vehicle layer and an edge layer, and proposes an Improved Starfish Optimization Algorithm (ISFOA) that utilizes chaotic mapping and genetic mutation to optimize offloading decisions and task partitioning ratios, aiming to minimize a priority-weighted combination of latency and energy consumption.
The ubiquitous deployment of Internet of Things (IoT) in smart building ecosystems generates massive volumes of multi-dimensional data, rendering secure storage and efficient retrieval paramount challenges. Although blockchain technology ensures data integrity and traceability, applying it to resource-constrained IoT networks exposes a fundamental “storage trilemma” among cost, latency, and scalability. Conventional approaches, relying on either static local retention or full cloud offloading, fail to reconcile these conflicting objectives. In this paper, we propose a Heat-Driven Hybrid Storage (HDHS) architecture that addresses limitations of existing hybrid storage systems-which rely on static parameters and reactive policiesthrough three key innovations: predictive heat modeling, dynamic redundancy adaptation, and multi-objective optimization. Specifically, HDHS incorporates a time-decay model with cost-aware uncertainty estimation to forecast block access “heat” under noisy conditions. Based on these predictions, the system dynamically tunes redundancy rates and utilizes rateless fountain codes to optimize the trade-off between storage footprint and data durability. Furthermore, we design a cloud-window optimizer that addresses a multi-objective trade-off to determine an effective boundary for local-cloud data migration. Extensive experiments on real-world datasets demonstrate that our scheme achieves a 40.7% reduction in storage costs, maintains sub-3ms query latency for 74.7% of queries, and ensures 99% + data reliability in permissioned blockchain environments.
Wei Yang, Xiaohua Wu, Yichang Chen et al.· Annual International Compute...· 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
The Metaverse is emerging as a next-generation digital ecosystem that integrates immersive virtual environments with real-time communication, artificial intelligence (AI), extended reality (XR), Internet of Things (IoT), blockchain, and cloud-edge computing technologies. However, supporting seamless interaction among millions of concurrent users requires a robust communication infrastructure capable of delivering ultra-low latency, high bandwidth, reliable connectivity, and secure data exchange. This paper presents an intelligent Metaverse communication infrastructure that integrates 6G wireless networks, Multi-access Edge Computing (MEC), Software-Defined Networking (SDN), Network Function Virtualization (NFV), AI-driven resource management, and blockchain-enabled security to optimize communication performance in immersive environments. The proposed architecture dynamically allocates network resources using machine learning-based traffic prediction, deploys edge intelligence for latency-sensitive applications, and employs blockchain for decentralized identity management and secure data transactions. Furthermore, digital twin-assisted network monitoring continuously analyzes communication conditions and enables proactive optimization to improve service reliability and Quality of Experience (QoE). Experimental evaluation demonstrates that the proposed framework significantly reduces communication latency, improves network throughput, enhances resource utilization, and strengthens security compared with conventional cloud-centric communication architectures. The proposed infrastructure provides a scalable, intelligent, and secure communication framework for future Metaverse applications, including virtual collaboration, digital healthcare, industrial automation, online education, and immersive entertainment.
Sheelam Abhiram, N. Vishnuvardhan, P. K. Reddy· International Journal of Sci...· 0 citations