2026· International Conference on Software and Data Technologies· pp. 619-626· 0 citations· 30 references
Computer Science
TL;DR
Results demonstrate that integrating demand prediction into game-theoretic NFV resource sharing significantly enhances efficiency while preserving strong security guarantees.
Abstract
: Network Function Virtualization (NFV) enables network functions to be implemented as software through flexible deployment capabilities. However, static demand assumptions, centralized security dependencies, and reactive decision-making approaches hinder efficient resource allocation among VNFs. This paper addresses these challenges by proposing a hybrid framework that integrates LSTM-based demand prediction regression model for time series forecasting, game-theoretic resource allocation, and blockchain-based access control. The blockchain provides decentralized, tamper-proof storage of cryptographic keys and automated enforcement of sharing agreements through smart contracts, eliminating reliance on trusted third parties.The proposed predictive algorithm combines real-time demand data with LSTM forecasts to estimate effective demand, enabling proactive resource allocation. The framework is evaluated through extensive simulations, comparing the proposed method with greedy matching and diagonalization baseline approaches. Results show that the proposed method achieves a 10.3% improvement in social utility over greedy matching, while reaching a near-optimal solution with a cost 6.3 × lower than diagonalization. The LSTM model achieves a Mean Absolute Percentage Error (MAPE) of 8.91% and an R 2 score of 0.880.These results demonstrate that integrating demand prediction into game-theoretic NFV resource sharing significantly enhances efficiency while preserving strong security guarantees.
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.
Accurate energy consumption forecasting in smart grids requires privacy-preserving learning mechanisms that remain effective under heterogeneous data distributions and support real-time operation. Existing federated learning approaches remain limited by poor performance under data heterogeneity, unvalidated architectural assumptions, and blockchain consensus mechanisms that are too slow for real-time grid operations. This paper presents PureChain, a blockchain-integrated federated learning framework that combines federated averaging, Dirichlet partitioning, LSTM-based forecasting, and a permissioned blockchain for secure client isolation and model rollback. A partitioning strategy is introduced to improve training stability under extreme non-IID conditions ([Formula: see text]), revealing that the distributional impact of a given Dirichlet parameter is dataset-dependent. To support low-latency smart grid applications, a permissioned blockchain employing proof-of-authority and association (PoA[Formula: see text]) consensus achieves 2.0 s transaction latency and 20.88 TPS, outperforming Hyperledger Fabric and Quorum in the evaluated setting. Experimental results on two energy-consumption datasets show that LSTM consistently outperforms BiLSTM under high data heterogeneity, achieving an average R[Formula: see text] of 0.9184 across clients at [Formula: see text]. Smart contract security assessment further yields a threat score of 98.5/100, demonstrating the framework's suitability for privacy-sensitive smart grid deployments. The contribution lies in the integration and systematic validation of established federated learning, forecasting, and blockchain technologies within a unified smart grid architecture.
Adah Lubwama Nanteza, Love Allen Chijioke Ahakonye, Dong-Seong Kim et al.· Scientific Reports· 0 citations
A new hybrid bioinspired optimization framework for efficient blockchain mining is presented, integrating Genetic Algorithm, Firefly optimization, and Particle Swarm Optimization into a unified architecture to take advantage of their complementary strengths.
K. Jajulwar, Priya Dasarwar, Uma Shankar Yadav et al.· Engineering, Technology &...· 0 citations
This research proposes a lightweight blockchain framework that incorporates Hyperledger Fabric with Practical Byzantine Fault Tolerance (PBFT) consensus, ZigbeePro communication, and Long Short-Term Memory-based energy demand forecasting to facilitate secure and intelligent decentralised energy trading.
Aliyu Musa Kida, C. Ngene, Jafaru Usman et al.· International Journal of Inn...· 0 citations
The increasing number of Internet of Things (IoT) devices in smart cities creates several challenges of trust management, resource allocation, and secure offloading. This paper introduces BTM-IoT, a blockchain-based trust management solution to improve the multi-access IoT offloading networks in urban areas. The proposed framework employs a hybrid consensus system that combines Delegated Proof-of-Stake (DPoS) with Practical Byzantine Fault Tolerance (PBFT) to make offloading decisions efficiently and securely while keeping the latency and throughput low. A trust-aware offloading optimization model is proposed, which combines the direct and indirect trust evaluation to increase the accuracy of the decisions and attack resistance. Our extensive experiments show that the average reduction in energy consumption of BTM-IoT is 31% over the baseline models, whereas the average reduction in task completion time comes to 27%, and the average improvement in attack detection accuracy is 43%. Further, scalability tests show that it takes only 26% longer to complete when scaling up from 50 to 500 devices, a significant improvement over traditional method. It also provides optimal use of resources, such as CPU, memory, and bandwidth, while delivering an average of 20% improvement in efficiency. Even when analyzed from the overhead of the blockchain, it is light on the IOT device, requiring 0.5 MB of storage and 2% CPU usage. The study results confirm the energy efficiency, scalability and security of BTM-IoT as a solution for multi-access IoT offloading in smart city infrastructures.
Artificial Intelligence-Generated Content (AIGC) has developed rapidly, with Diffusion Models (DMs) gaining wide attention for their superior image generation capabilities. However, the high computational cost of inference limits their deployment in resource-constrained environments such as edge computing. Cloud-edge collaboration is considered a feasible solution to improve inference efficiency, but existing studies have not fully addressed the issue of trust propagation among multiple entities. To address these, we propose a blockchain-aided trusted inference framework for cloud–edge collaborative DMs. Smart contracts are designed to automate image generation task management and ensure traceability throughout the inference process. Leveraging the step-by-step denoising nature of DMs, we introduce a Siamese Network-based Semantic Matching (SNSM) model to identify whether a new task can reuse intermediate results from historical inferences, thereby reducing redundant computation and improving efficiency. We formulate an objective function to minimize total inference latency by jointly considering queuing, transmission, and computation delays, with image quality metrics as constraints. To solve these, we propose Diffusion-Attention integrated Multi-Agent Reinforcement Learning (DAMARL), which dynamically optimizes task partitioning and scheduling between cloud and edge to reduce latency while preserving generation quality. Extensive experiments show that SNSM achieves 85.3% accuracy in reuse decisions, and DAMARL improves average reward by 17.5% $\sim ~35$ % over existing methods, demonstrating the effectiveness of our approach in enhancing DM inference efficiency and performance.
Yu Song, Yin-Lin Ren, Shao-Yong Guo et al.· IEEE Transactions on Cogniti...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
