EAI Endorsed Transactions on Scalable Information Systems
type: channel
description: EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2024, the journal started to publish on a monthly frequency (12 issues per year). INDEXING: ESCI-WoS (IF: 1.3), Compendex, DOAJ, ProQuest, EBSCO, Crossref, Dimensions eISSN: 2032-9407
publisher: European Alliance for Innovation (EAI)
language: en
publication name: EAI Endorsed Transactions on Scalable Information Systems
issn: 2032-9407
copyright: This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
items: Intelligent and secure Federated Learning for data elements circulation in heterogeneous edge computation
items: Scalable Dynamic Trust Evolution Framework for Large-Scale IoT Information Systems with Asymmetric Evidence Weighting
items: Research on Interference Elimination-Based Energy Consumption of the Internet of Things
items: Correction: Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection
items: Correction: A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar
items: Digital Twin and Constraint-Guided Generative Decision Support for Human-Machine Collaborative Manufacturing under Industry 5.0
items: Optimization of Load Balancing and Real-Time Scheduling in Distributed Systems for Cross-Language Text Processing Tasks
items: Bearing Cross-Domain Fault Diagnosis Based on Wavelet Time-Frequency Map and Resnet Transfer Learning in Industrial IoT-Enabled Environments
items: Research on digital workshop dynamic scheduling technology based on blockchain
items: Optimization of Circular-Rail RGV Scheduling in a Tobacco Warehouse
items: A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar
items: Human-Centric Smart Manufacturing under Industry 5.0:IIoT-Enabled Machine Learning for Real-Time Fault Identification and Adaptive Workforce Scheduling
items: Intelligent Collaborative Resource Allocation for Mechanical Manufacturing Edge Networks: A Deep Reinforcement Learning Approach
items: A Cross-Domain Data Flow Security Governance and Privacy Protection Mechanism for Distributed Digital Art Platforms
items: Research on a Deep Learning-Based Hybrid Attention and Regularised Supervised Detection Model for Surface Defects in Precision Components
items: ARaNet: Attention and Residual Aware Network for Resilient Digital Twins in Rail Transit Equipment Manufacturing
items: AI-Based Optimization Method for Coverage of IoT Upload Link Communication
items: Data Security and Privacy Protection in Distributed Digital Economy: Economic Impacts and Governance Mechanisms
items: Real-Time Scheduling Mechanisms for Heterogeneous Distributed Systems in Edge-Enabled Urban Renewal Digital Twin Platforms
items: Uncertainty-Aware Decision-Making of Robust Bayesian Networks with Distributed Data Security in Adaptive Artificial Intelligence Systems
items: Research on Quantum Privacy Protection Framework and Scene Adaptation for Edge Identity Authentication in Distributed Cross Domain Networks
items: Optimization of Deep Learning-Based Patent Infringement Text Comparison and Retrieval Algorithms
items: Energy-Efficient Lightweight Edge Inference via MOSI-AirComp: Over-the-Air Convolution and Communication-Aware Dual-Branch Training
items: Generative AI-based privacy protection and security management mechanism for distributed financial systems
items: Edge Computing Communication Privacy Protection Method Based on Federated Learning Algorithm
items: A Predictive and Multi-Objective Cloud–Edge Scheduling Framework with Task-Aware Encoding for Real-Time Educational Services
items: Agricultural Supply Chain Security Management Based on Distributed Blockchain and Time Series Analysis: Focus on Cross-Domain Data Security and Privacy Computing
items: Retraction Notice: The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data
items: A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
items: Adaptive Scheduling and Compute Demand Prediction for Animation Rendering Tasks Using Machine Learning

Intelligent and secure Federated Learning for data elements circulation in heterogeneous edge computation
type: item
description: INTRODUCTION: Heterogeneous edge computing creates data islands due to privacy and environmental heterogeneity. Current solutions lack an overall approach. OBJECTIVES: This study constructs a federated learning framework for secure data element circulation, mitigating low-power node tailing, balancing communication with accuracy, and adapting to non-IID data, advancing intelligent systems and cybersecurity. METHODS: The framework fuses Dynamic Clustering, Adaptive Gradient Transmission, and Data Distribution Alignment. Validation uses simulations against existing technologies. RESULTS: The proposed method achieves 0.892±0.021 comprehensive performance, 11.1%–13.6% higher than existing technologies. Under 20% network interruption, attenuation is 3.3% vs. 8.6%–12.5%. Data flow reaches 18.6±0.7 MB/s; privacy leakage is 0.8±0.2 bit. CONCLUSION: This study provides reliable support for safe, efficient data element circulation, advancing intelligent systems and cybersecurity.
subject: Research articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: heterogeneous edge computing
  topic: keywords
subject: data element circulation
  topic: keywords
subject: dynamic clustering technology for device performance
  topic: keywords
subject: adaptive gradient transmission technology
  topic: keywords
subject: data distribution alignment technology
  topic: keywords
creator: Geng Cheng
creator: Jianbo Liu
rights: Copyright (c) 2026 Geng Cheng, Jianbo Liu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-28
publication date: 2026-07-28
doi: 10.4108/eetsis.14118

Scalable Dynamic Trust Evolution Framework for Large-Scale IoT Information Systems with Asymmetric Evidence Weighting
type: item
description: INTRODUCTION: The growth of Internet-of-Things (IoT) infrastructure being built pushes the envelope to giant scale, heterogeneous systems where autonomous devices keep communicating amongst themselves with less and less human intervention. Static or transient security mechanisms based on pure authentication fall short in this case, as every device that has nominally been put in a non-trust domain can push bad from there. OBJECTIVES: To guard against, we present in this paper a Scalable Dynamic Trust Evolution (SDTE) framework for large-scale IoT information systems. METHODS: We model trust as a dynamically changing latent state dependent on multiple sources of evidence, such as direct interaction, neighbour recommendation, context condition, and historical behaviour. A temporal update mechanism with exponential forget rate (λ) allows fast adaptation to changing behaviour, while asynchronous weighting (β⁻ > β⁺) indicates negative actions have larger weighting on trust than positive ones. An uncertainty-aware fusion of heterogeneous evidence sources mitigates conflicting observations. Overall, the framework is fully distributed and operates in lightweight computation (O(d)) without any need for sufficient computing resources, and can thus be deployed on low-resource edge devices. RESULTS: Our experimental results on publicly available IoT datasets (IoT-23 and Edge-IIoTset) show that SDTE achieves high detection performance (96.8% accuracy, 95.7% F1-score) across malicious ratio scenarios, good robustness against intermittent on–off attacks, and scaling as the network grows in size. CONCLUSION: The proposed SDTE framework provides an effective, lightweight, and scalable trust management solution for large-scale IoT information systems.
subject: Research articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: Trust Management
  topic: keywords
subject: Scalable Systems
  topic: keywords
subject: Dynamic Trust
  topic: keywords
subject: Uncertainty Fusion
  topic: keywords
subject: Distributed Security
  topic: keywords
creator: Tao Sun
rights: Copyright (c) 2026 Tao Sun https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-28
publication date: 2026-07-28
doi: 10.4108/eetsis.14117

Research on Interference Elimination-Based Energy Consumption of the Internet of Things
type: item
description: INTRODUCTION: During the uninterrupted operation of the Internet of Things, the continuous activity of a large number of terminal devices not only increases the energy consumption of the nodes, but also causes signal interference between devices, resulting in degraded channel conditions and Quality of Service of the Internet of Things, increased data transmission latency, and a further increase in energy consumption of the Internet of Things. OBJECTIVES: To mitigate the effects of communication conflict and signal interference on data transmission and reduce the energy consumption of the Internet of Things, an interference elimination-based energy consumption optimization algorithm for the Internet of Things is proposed. METHODS: According to the model of the Internet of Things, this paper divides the nodes of the Internet of Things into sink nodes and terminal nodes. The sink node dynamically allocates data transmission time-slots based on the Signal-to-Interference plus Noise Ratios of the terminal nodes. Meanwhile, the terminal node adaptively adjusts its power on the basis of its activity status while guaranteeing data transmission performance. RESULTS: The simulation results showed that the data transmission latency was reduced, signal interference mitigated, channel environments improved, and energy consumption of the Internet of Things lowered. CONCLUSION: The proposed algorithm can effectively reduce the impact of channel interference on the data transmission of terminal nodes, improve data transmission performance and Quality of Service of the Internet of Things, and reduce energy consumption of the Internet of Things.
subject: Research articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: energy consumption
  topic: keywords
subject: signal interference
  topic: keywords
subject: sink node
  topic: keywords
subject: terminal node
  topic: keywords
creator: Lianlian Song
creator: Chuanhong Song
creator: Yicheng Liu
rights: Copyright (c) 2026 Lianlian Song, Chuanhong Song, Yicheng Liu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-28
publication date: 2026-07-28
doi: 10.4108/eetsis.14111

Correction: Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection
type: item
description: Correction to: Kong S et al. (2026) Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection. https://doi.org/10.4108/eetsis.11781 This paper was inadvertently published without the funding information. The following information was added to the “Funding” section of the paper: This paper is supported by Dezhou Engineering Research Center for Big Data and Intelligent Perception Technology (Platform No. PT2025KJT002). The original article has been updated.
subject: Correction
  topic: section
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-13
publication date: 2026-07-13
doi: 10.4108/eetsis.14017

Correction: A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar
type: item
description: Correction to: Hongcai Chen et al. (2026) A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar. https://doi.org/10.4108/eetsis.13388 Due to typesetting errors, some of the formulas in sections 2.2.2 and 2.3.1 of this article did not display correctly. These errors were corrected and the original article has been updated.
subject: Correction
  topic: section
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-01
publication date: 2026-07-01
doi: 10.4108/eetsis.13848

Digital Twin and Constraint-Guided Generative Decision Support for Human-Machine Collaborative Manufacturing under Industry 5.0
type: item
description: INTRODUCTION: In Industry 5.0 manufacturing systems, adaptive decision support is required to handle demand fluctuations, machine degradation, and operational feasibility constraints under human supervision. However, existing digital twin and data-driven approaches remain primarily focused on monitoring or forecasting, with limited integration into decision-oriented production control and feedback adaptation. OBJECTIVES: This study aims to develop a human-in-the-loop adaptive production decision-support framework that transforms external demand signals into feasible, risk-aware, and reviewable production actions by integrating demand-production state fusion, digital twin-based constraints, structured generative decision decoding, and feedback-driven updates. METHODS: A constrained decision-making system is constructed using Online Retail II and AI4I 2020 datasets. A unified state representation is learned by fusing demand indicators with manufacturing digital twin variables. A constraint-guided policy decoder is designed to generate discrete production actions under feasibility constraints rather than performing demand forecasting. Human feedback is encoded into subsequent decision states to enable iterative adaptation. The model is trained via a unified optimization objective balancing demand alignment, risk control, and explanation consistency. RESULTS: Experimental results on two datasets show that the proposed framework achieves improved demand-production alignment while consistently reducing manufacturing risk exposure and human override rates compared with forecasting-based and decision-oriented baselines. CONCLUSION: The proposed framework demonstrates that integrating digital twin constraints, constrained generative decision-making, and human feedback enables a shift from demand prediction to feasibility-aware production decision optimization, improving safety, interpretability, and human acceptability in Industry 5.0 manufacturing systems.
subject: Resiliency and Adaptability for Future Manufacturing: AI Driven Recovery and Response Mechanisms
  topic: section
subject: industry 5.0
  topic: keywords
subject: human-machine collaboration
  topic: keywords
subject: digital twin
  topic: keywords
subject: generative artificial intelligence
  topic: keywords
subject: adaptive production decision support
  topic: keywords
creator: Chen Qian
rights: Copyright (c) 2026 Chen Qian https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-12
publication date: 2026-08-12
doi: 10.4108/eetsis.13777

Optimization of Load Balancing and Real-Time Scheduling in Distributed Systems for Cross-Language Text Processing Tasks
type: item
description: INTRODUCTION: With the rapid expansion of cross-language text processing applications, distributed systems must support large-scale multilingual workloads with high efficiency and responsiveness. Effective load balancing and real-time scheduling are therefore essential for maintaining system performance. However, many existing approaches rely on static or general-purpose scheduling strategies, which fail to capture language-dependent workload variations, resulting in limited scalability and inefficient resource utilization. OBJECTIVES: This study aims to improve load balancing efficiency and real-time scheduling performance in distributed systems for cross-language text processing. The focus is on addressing dynamic task variations, heterogeneous resource demands, and robustness challenges in multilingual, noisy, and fluctuating computing environments. METHODS: A cross-language-aware dynamic load balancing and scheduling optimization framework based on reinforcement learning is proposed. The framework incorporates language pair, task type, priority, input length, estimated resource demand, and real-time node status into an adaptive scheduling strategy. Denoising mechanisms and feature fusion modules are further introduced to enhance scheduling stability and decision robustness. RESULTS: Experimental results show that the proposed method outperforms existing approaches in response time, resource utilization, and task completion rate. The framework achieves a task completion rate of 96.5%. Under high-noise conditions, the reduction in task completion rate is approximately 50% lower than that of baseline methods, demonstrating improved robustness. CONCLUSION: The proposed framework provides an effective solution for dynamic scheduling and load balancing in cross-language text processing systems. By coupling multilingual task characteristics with distributed resource states, it offers scalable support for multilingual distributed computing and new insights into reinforc…
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Cross-language Text Processing
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Load Balancing
  topic: keywords
subject: Real-time Scheduling
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
creator: Hongying Pu
creator: Jixiang Wang
rights: Copyright (c) 2026 Hongying Pu, Jixiang Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-07
publication date: 2026-08-07
doi: 10.4108/eetsis.11807

Bearing Cross-Domain Fault Diagnosis Based on Wavelet Time-Frequency Map and Resnet Transfer Learning in Industrial IoT-Enabled Environments
type: item
description: To overcome the difficulties faced by current bearing fault diagnosis methods in industrial applications, such as the difficulty in obtaining sufficient labeled real fault training samples and the subpar performance of cross-domain fault diagnosis due to data distribution deviation, a new bearing cross-domain fault diagnosis method based on wavelet time-frequency maps and Resnet transfer learning (CDF-WTI-RTL) is proposed.In the context of the Industrial Internet of Things (IIoT) and Industry 5.0, real-time condition monitoring and intelligent predictive maintenance have become critical for ensuring manufacturing system resilience and sustainability. First, raw vibration signals are processed using continuous wavelet transform to obtain two-dimensional time-frequency maps. Second, a Resnet network adapted for bearing fault diagnosis is constructed to autonomously mine deep features from source and target domains. A random forest classifier is then employed to build a cross-domain fault pattern recognition classifier. Experimental analysis of training samples using two different bearing fault datasets shows that the accuracy of the CDF-WTI-RTL model reaches 98.38% and 91.5% with unbalanced training samples respectively, notably surpassing the other 5 comparative models. The results demonstrate that the proposed method can be effectively deployed in IIoT architectures for reliable, cross-condition bearing fault diagnosis.
subject: Resiliency and Adaptability for Future Manufacturing: AI Driven Recovery and Response Mechanisms
  topic: section
subject: Fault diagnosis
  topic: keywords
subject: time-frequency image
  topic: keywords
subject: Resnet; transfer learning
  topic: keywords
subject: domain adaptation
  topic: keywords
subject: Internet of Things (IIoT)
  topic: keywords
creator: Wanchuan Wang
rights: Copyright (c) 2026 Wanchuan Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-29
publication date: 2026-07-29
doi: 10.4108/eetsis.13528

Research on digital workshop dynamic scheduling technology based on blockchain
type: item
description: INTRODUCTION: As a critical process in high-end equipment manufacturing, dynamic scheduling in digital workshops plays a vital role in order delivery and project cost control. However, traditional scheduling frameworks suffer from weak data security, opaque multi-agent collaboration, and slow constraint checking. OBJECTIVES: To address these limitations, this study proposes an efficient and trustworthy dynamic scheduling solution that balances operational efficiency with data reliability. METHODS: A three-layer collaborative framework comprising cloud, edge, and blockchain layers is designed. The cloud layer employs a multi-strategy improved multi-objective genetic algorithm with three-stage encoding targeting processes, equipment, and job teams. This algorithm integrates tournament selection, adaptive repair, and particle swarm optimization to globally minimize maximum delivery time, total energy consumption, and equipment load fluctuation. The blockchain layer leverages consortium blockchain and smart contracts for tamper-proof data storage and automatic constraint verification, while the edge layer handles real-time on-site scheduling. RESULTS: The optimization algorithm converges to an optimal value of 55.1 in an average of only 3.9 iterations, outperforming three mainstream heuristic algorithms. In a real deployment across 57 workstations in a cruise ship laser sheet metal workshop, scheduling tasks are completed within 25 seconds. Blockchain technology achieves a 100% data tampering detection rate, improves data consistency from 96.2% to 99.8%, and reduces scheduling deviation from 4.39% to 3.82%, with only a 3-second processing overhead. CONCLUSION: The integrated architecture enhances both scheduling efficiency and data reliability, supporting the digital and intelligent transformation of shipbuilding enterprises.
subject: Resiliency and Adaptability for Future Manufacturing: AI Driven Recovery and Response Mechanisms
  topic: section
subject: Blockchain
  topic: keywords
subject: Dynamic Scheduling
  topic: keywords
subject: Cloud-Edge Collaboration
  topic: keywords
subject: Multi-objective Genetic Algorithm
  topic: keywords
subject: Smart Contract
  topic: keywords
creator: Ganlong Wang
creator: Jun Zhu
creator: Guoyin Zhang
creator: Jingjing Sun
creator: Wei Zheng
creator: Bin Xie
creator: Xiang Li
rights: Copyright (c) 2026 Ganlong Wang, Jun Zhu, Guoyin Zhang, Jingjing Sun, Wei Zheng, Bin Xie, Xiang Li https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-13
publication date: 2026-07-13
doi: 10.4108/eetsis.13503

Optimization of Circular-Rail RGV Scheduling in a Tobacco Warehouse
type: item
description: Continuous operation in tobacco warehouses places high requirements on circular-rail rail guided vehicle (RGV) dispatching, because task concentration, track interference, and equipment abnormalities may quickly cause vehicle waiting and material-flow delay. To improve the response capability of this system, this study develops a hybrid scheduling method based on an improved genetic algorithm and simulated annealing. According to the operating characteristics of the circular rail, a scheduling model is formulated to minimize the overall task completion time while considering rail operation rules and composite task requirements. In the solution process, the simulated annealing temperature is used to adjust parent selection and mutation behavior, so that the algorithm can maintain search diversity in the early stage and improve convergence in the later stage. Case results show that the proposed method reduces the average maximum completion time from 150.2 s to 143.4 s and improves the best result from 145.7 s to 139.6 s compared with the conventional genetic algorithm. These results indicate that the proposed method can improve RGV response efficiency and provide decision support for stable warehouse logistics under disturbances such as task fluctuation and path conflict.
subject: Resiliency and Adaptability for Future Manufacturing: AI Driven Recovery and Response Mechanisms
  topic: section
subject: rail guided vehicle scheduling
  topic: keywords
subject: circular-rail
  topic: keywords
subject: genetic algorithm
  topic: keywords
subject: task optimization
  topic: keywords
creator: Dingke Shi
creator: Zhihui Ye
creator: Nanzhe Ding
creator: Jie Gao
creator: Chao Cheng
creator: Wenwen Lin
rights: Copyright (c) 2026 Dingke Shi; Zhihui Ye, Nanzhe Ding, Jie Gao, Chao Cheng, Wenwen Lin https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-15
publication date: 2026-07-15
doi: 10.4108/eetsis.13463

A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar
type: item
description: INTRODUCTION: Roadside perception systems in intelligent transportation have stringent requirements for all-weather, high-precision, and real-time vehicle target tracking. However, there are inherent technical bottlenecks in such systems, including sparse millimeter-wave radar point clouds, severe noise interference, and the poor adaptability of traditional algorithms to distance-dependent density variations. OBJECTIVES: This study aims to propose a robust vehicle target tracking method based on roadside millimeter-wave radar to address the aforementioned technical bottlenecks and meet the stringent requirements of roadside perception systems for vehicle target tracking. METHODS: The research employs three key technical methods: Firstly, a point cloud distribution histogram is constructed to accurately localize the road area, and radar cross-section (RCS) characteristics are synergistically integrated with motion attributes to eliminate static clutter, multipath false plots, and electromagnetic noise. Secondly, a Distance-Zoned Multi-Frame DBSCAN (DZM-DBSCAN) clustering algorithm is proposed, which partitions the radar’s effective detection range into near-distance and far-distance intervals based on the physical principle of point cloud density attenuation with increasing detection distance, dynamically optimizes clustering parameters (Eps, MinPts) for each interval using local density statistics from k-nearest neighbor (KNN) analysis, and suppresses single-frame isolated false clusters through inter-frame motion continuity constraints. Finally, a multi-target tracking framework is established by combining a dynamically threshold-adjusted Kalman Filter (KF) with multi-feature weighted Hungarian matching. RESULTS: The main results obtained in this paper are the following: Experimental validation on the public RadarData dataset shows that the DZM-DBSCAN algorithm achieves a Silhouette Coefficient (SC) of 0.8456, representing a 10.09% improvement over the traditional …
subject: Research articles
  topic: section
subject: millimeter-wave radar
  topic: keywords
subject: point cloud clustering
  topic: keywords
subject: DZM-DBSCAN
  topic: keywords
subject: target tracking
  topic: keywords
subject: Kalman filtering
  topic: keywords
subject: hungarian algorithm
  topic: keywords
creator: Hongcai Chen
creator: Yu Cheng
creator: Yaheng Ren
creator: Yaoxing Kang
rights: Copyright (c) 2026 Hongcai Chen, Yu Cheng, Yaheng Ren, Yaoxing Kang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-08
publication date: 2026-06-08
doi: 10.4108/eetsis.13388

Human-Centric Smart Manufacturing under Industry 5.0:IIoT-Enabled Machine Learning for Real-Time Fault Identification and Adaptive Workforce Scheduling
type: item
description: This study addresses Industry 5.0's demand for human-machine collaboration and manufacturing resilience by developing machine learning models for rapid fault identification and adaptive personnel scheduling. When a production-line fault occurs, the proposed approach uses IIoT data to identify the fault category quickly, support targeted intervention, and facilitate the restoration of normal production. XGBoost achieved near-perfect fault identification accuracy in model evaluation and accuracy and recall of 1.0 in validation, significantly outperforming the Random Forest baseline. Analysis revealed that experienced operators increased product qualification rates by 15% but reduced output by 10% due to physical factors. Decision tree regression minimized scheduling errors with an MSE of 0.49 and an R2 of 0.83. These results demonstrate that integrating IIoT-driven rapid fault identification with intelligent staffing provides a practical basis for shortening the response cycle, improving recovery capability after disruptions, and strengthening the resilience of human-centric manufacturing systems.
subject: Resiliency and Adaptability for Future Manufacturing: AI Driven Recovery and Response Mechanisms
  topic: section
subject: rapid fault identification
  topic: keywords
subject: XGBoost
  topic: keywords
subject: personnel scheduling
  topic: keywords
subject: manufacturing resilience
  topic: keywords
subject: Industrial Internet of Things
  topic: keywords
creator: Yaocong Yaocong Xie
creator: Ning Wang
rights: Copyright (c) 2026 Yaocong Xie, Ning Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-21
publication date: 2026-07-21
doi: 10.4108/eetsis.13326

Intelligent Collaborative Resource Allocation for Mechanical Manufacturing Edge Networks: A Deep Reinforcement Learning Approach
type: item
description: Industry 4.0 is transforming mechanical manufacturing systems into edge-enabled, networked, and intelligent environments, where concurrent task execution, heterogeneous resource coordination, and dependency-aware scheduling have become critical requirements. In such scenarios, resource allocation must jointly consider computational demand, storage demand, business priority, deadline urgency, and inter-task dependencies, while enabling coordinated decisions among distributed edge agents. However, existing single-agent reinforcement learning methods have limited capability to model complex dependency relationships and heterogeneous resource collaboration under concurrent workloads, whereas conventional multi-agent systems often rely on coarse-grained task modeling and simplified cooperation mechanisms. To address these limitations, this paper proposes MIRA, a multi-agent deep reinforcement learning-based method for resource allocation in mechanical manufacturing edge networks. MIRA first decomposes tasks into fine-grained dependent subtasks, constructs a deadline-aware multi-metric priority function, and introduces a dynamic weight adjustment mechanism to balance computational demand, storage demand, normalized business priority, and deadline urgency. It then employs an adjacency matrix to characterize topology-aware agent interactions, enabling coordinated decision-making between computing agents and storage agents. Furthermore, MIRA incorporates an event-triggered state-exchange mechanism that updates subtask priorities and agent policies under changing workload, deadline, resource, and topology conditions. Experimental results on a PCB-derived simulated scheduling workload show that MIRA outperforms the selected baselines across multiple scheduling metrics.
subject: Resiliency and Adaptability for Future Manufacturing: AI Driven Recovery and Response Mechanisms
  topic: section
subject: Multi-agent deep reinforcement learning
  topic: keywords
subject: edge intelligence
  topic: keywords
subject: resource allocation
  topic: keywords
subject: intelligent manufacturing
  topic: keywords
creator: Kaile Xiao
creator: Xiajing Wang
creator: Jing Wang
rights: Copyright (c) 2026 Kaile Xiao, Xiajing Wang, Jing Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-07
publication date: 2026-07-07
doi: 10.4108/eetsis.13306

A Cross-Domain Data Flow Security Governance and Privacy Protection Mechanism for Distributed Digital Art Platforms
type: item
description: INTRODUCTION: Distributed digital art platforms face ownership ambiguity, privacy leakage, unauthorized access, and audit difficulties in cross-domain data flows. OBJECTIVES: To enhance trust, privacy, controllability, and auditability through a dedicated data security governance mechanism. METHODS: A closed-loop governance mechanism jointly models art data, AIGC models, copyrights, and transactions, integrating five coordinated modules: damage-tolerant ownership verification, privacy-preserving cross-chain auditing, dynamic access control, secure delivery with rollback, and efficient traceability. RESULTS: Ownership confirmation reaches 97.9%–98.7% (mild perturbations) and 85.9% (combined); authorization accuracy: 95.6%; illegal access interception: 94.9%; normal transaction audit pass rate: 99.3%; anomaly detection: up to 100%; on-chain storage compression: 99.99% (50 MB); traceability success: 94.8%. Access latency remains below 20 ms with 180+ transactions/s under concurrency. CONCLUSION: The mechanism effectively secures cross-domain data flows with high trustworthiness and low overhead, suitable for distributed digital art ecosystems.
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: cross-domain data flow
  topic: keywords
subject: distributed system security
  topic: keywords
subject: privacy protection
  topic: keywords
subject: dynamic access control
  topic: keywords
subject: cross-chain audit
  topic: keywords
subject: digital art platform
  topic: keywords
creator: Meixuan Lv
rights: Copyright (c) 2026 Meixuan Lv https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-24
publication date: 2026-06-24
doi: 10.4108/eetsis.13268

Research on a Deep Learning-Based Hybrid Attention and Regularised Supervised Detection Model for Surface Defects in Precision Components
type: item
description: To solve the challenges in detecting hundreds of minute surface defects (scratches, pits, material shortages) and their combinations on precision components (connecting rods, gears, bearings, etc.) ranging from 6 to 30 mm in size—notably identification difficulty and high misdetection rates—this research proposed a deep learning convolutional neural network detection model integrating a with dropout regularization supervision. This model aims to achieve rapid identification and precise judgment of micro-defects, combined defects, and other issues in precision components. This model extracted global fragmented features from precision component images through convolutional layers. Pooling layers incorporated a hybrid attention mechanism integrating maximum and average pooling. Fully-connected layers concurrently assembled hybrid attention dual-pooling filtered features, creating hybrid attention weights via hyperbolic tangent activation functions to obtain precise fusion of location feature and defect type extraction. The dropout regularization mechanism randomly deactivates relevant neurons during the feature fusion stage of the fully connected layer. Experimental results demonstrated that this model reduced false detection rate for similar defects (e.g., dents of varying shapes and scratches with different contours) to obtaining an average defect detection accuracy of 99.8%. with an average detection speed of 300 pcs/min. This represented improvements of 9.2% and 7.6% in detection accuracy and 15% and 10% in detection speed compared to traditional CNN and single-attention models, respectively. It also presented excellent robustness in random defect detection under field conditions, providing a novel solution for efficient and precise surface defect detection on precision components.
subject: Resiliency and Adaptability for Future Manufacturing: AI Driven Recovery and Response Mechanisms
  topic: section
subject: Deep learning
  topic: keywords
subject: Precision components
  topic: keywords
subject: Defect detection
  topic: keywords
subject: Hybrid attention mechanism
  topic: keywords
subject: Regularised supervision
  topic: keywords
subject: Convolutional neural networks
  topic: keywords
creator: Honggang Duan
rights: Copyright (c) 2026 Honggang Duan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-15
publication date: 2026-07-15
doi: 10.4108/eetsis.13233

ARaNet: Attention and Residual Aware Network for Resilient Digital Twins in Rail Transit Equipment Manufacturing
type: item
description: INTRODUCTION: In rail transit equipment manufacturing, which encompasses locomotive body welding, metro vehicle assembly, and high-speed rail carriage production, high-fidelity 3D point cloud data acquired by industrial sensors serves as the foundation for digital twin modeling, automated quality inspection, and robotic guidance. However, harsh production environments characterized by metallic dust, welding spatter, mechanical vibration, and frequent occlusions by fixtures and tooling inevitably introduce severe data corruption, including missing regions and non-uniform point density. Such degraded sensor data undermines the reliability of downstream manufacturing processes that depend on accurate 3D representations. OBJECTIVES: This paper presents an effective 3D sensor data recovery method that reconstructs complete geometric representations from corrupted partial scans, specifically targeting the data integrity challenges encountered in rail transit equipment manufacturing. The proposed approach aims to simultaneously restore global structural completeness and local geometric precision, thereby enabling resilient digital twin systems that maintain operational continuity despite sensor-induced data loss. METHODS:We propose an Attention and Residual Aware Network (ARaNet) featuring a Multi-Scale Channel-Aware Convolution encoder and a Hierarchical Residual-Aware Decoder. The encoder performs Farthest Point Sampling at multiple resolutions (2048, 1024, 512 points) and applies a channel attention mechanism to dynamically weight feature channels, emphasizing geometrically salient structures such as sharp edges, curved surfaces, and mechanical joints that are critical in rail transit component geometries, including bogie frames, car body shells, and coupler assemblies. The decoder progressively generates point clouds from coarse resolution (64 points) to medium resolution (128 points) to high resolution (2048 points), incorporating a residual refinement module that pre…
subject: Resiliency and Adaptability for Future Manufacturing: AI Driven Recovery and Response Mechanisms
  topic: section
subject: 3D sensor data recovery
  topic: keywords
subject: digital twin resilience
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subject: rail transit equipment manufacturing
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subject: attention mechanism
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subject: residual refinement
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subject: point cloud completion
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creator: Xi Chen
creator: Xiaolong Gao
creator: Hanyue Zhan
creator: Wanting Liu
rights: Copyright (c) 2026 Xi Chen, Xiaolong Gao, Hanyue Zhan, Wanting Liu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
doi: 10.4108/eetsis.13054

AI-Based Optimization Method for Coverage of IoT Upload Link Communication
type: item
description: To address the degradation in uplink communication quality of the Internet of Things (IoT) caused by long-distance signal attenuation, multipath interference, and dynamic environmental changes, this paper proposes an artificial intelligence (AI)-based optimization method for IoT uplink communication coverage. First, service sampling points, base stations, and antenna parameters are defined within a target area, and a signal propagation model is constructed. The Sigmoid function is then used to smoothly evaluate service quality, and the coverage optimization problem is formulated as an objective function that maximizes coverage. The Q-learning algorithm is employed to achieve dynamic optimization by designing the state space (e.g., coverage rate, number of devices), action space (e.g., antenna parameter adjustments), and reward function (e.g., coverage improvement). Through iterative Q-value updates, the base station controller adaptively adjusts antenna configurations to balance coverage and interference, thereby improving long-term communication performance. To further enhance optimization efficiency, prioritized experience replay and an adaptive exploration mechanism are introduced to dynamically adjust the exploration–exploitation trade-off during learning, accelerating convergence and improving adaptability in dynamic IoT environments. Experimental results show that the proposed method significantly improves the communication performance of the uplink: the number of complete sensor data upload packets increases by 20%–25%, the number of transmission interruptions decreases by 70%–80%, and the average communication duration is reduced by 30%–40%, verifying the effectiveness and robustness of the method.
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: artifical intelligence
  topic: keywords
subject: internet of things
  topic: keywords
subject: upload link
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subject: communication coverage
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subject: coverage optimization
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subject: reinforcement learning
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creator: Sheng Ye
rights: Copyright (c) 2026 Sheng Ye https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-10
publication date: 2026-06-10
doi: 10.4108/eetsis.12965

Data Security and Privacy Protection in Distributed Digital Economy: Economic Impacts and Governance Mechanisms
type: item
description: INTRODUCTION:The distributed digital economy, characterized by decentralization and cross-entity data flow, improves factor allocation efficiency but increasingly raises concerns over data security and privacy abuse. OBJECTIVES: Unlike the conventional digital economy, which often centers on centralized platforms (e.g., e-commerce, cloud computing), the distributed digital economy in this paper specifically refers to an economic system where data—as a production factor—is stored, computed, and circulated across multiple independent nodes without a central coordinating authority, relying on technologies such as blockchain, distributed ledger, edge computing, and peer-to-peer networks. Its core governance features include decentralized data control, consensus-based verification, and peer-to-peer economic activities. METHODS: This paper studies data security and privacy protection in the distributed digital economy from two aspects: economic impact and governance mechanism. Based on panel data from 30 provinces in China from 2018 to 2023, this paper uses the entropy weight-TOPSIS method, a two-way fixed effects model, a mediation effect model, and a spatiotemporal heterogeneity model to empirically test the economic impact and transmission mechanism of data security and privacy protection on the distributed digital economy. RESULTS: The empirical analysis results show that the level of data security and privacy protection significantly and positively promotes the development of the distributed digital economy, with each unit increase leading to a 0.412 unit increase in the development index. Blockchain smart contracts, privacy computing standards, and cross-border data flow rules play significant mediating roles, accounting for 93.7% of the total mediating effect. This positive economic effect exhibits significant spatiotemporal differences, increasing year by year, and is significantly higher in the eastern region than in the central and western regions. CONCLUSION: B…
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: distributed
  topic: keywords
subject: digital economy
  topic: keywords
subject: data security
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subject: privacy protection
  topic: keywords
subject: economic impact
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subject: governance mechanism
  topic: keywords
creator: Cuilan Wang
rights: Copyright (c) 2026 Cuilan Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-07
publication date: 2026-07-07
doi: 10.4108/eetsis.12557

Real-Time Scheduling Mechanisms for Heterogeneous Distributed Systems in Edge-Enabled Urban Renewal Digital Twin Platforms
type: item
description: INTRODUCTION: Urban renewal digital twin systems must support high-fidelity rendering and millisecond-level interactive control across geographically distributed, heterogeneous edge computing infrastructures. Conventional scheduling approaches, often assuming hardware homogeneity and static task graphs, struggle with dynamic service-chain reconfigurations driven by urban spatial entropy fluctuations, leading to resource misallocation and service instability in large-scale distributed environments. OBJECTIVES: This paper aims to develop a real-time, scalable scheduling framework for heterogeneous distributed edge systems that jointly accounts for hardware diversity, evolving directed acyclic graph (DAG)-based workloads, and decentralized decision-making while preserving data locality and model privacy. METHODS: We propose a hierarchical scheduling architecture integrating physical and logical coordination: (1) a resource affinity mask encodes hardware constraints as a prior to prune infeasible placements; (2) a spatio-temporal graph neural network captures critical path dynamics in non-stationary task DAGs; and (3) a federated policy distillation mechanism enables knowledge transfer across structurally diverse cloud-edge-end agents without sharing raw models or data. RESULTS: Experiments on the Alibaba Cluster Trace and Shanghai Telecom datasets show that the proposed method reduces average latency to 43.8 ms, achieving a 25.6% reduction compared with CO-MARL, sustains a 94.7% task completion rate under long-tail traffic surges, and achieves an edge inference latency of 2.1 ms. CONCLUSION: The “physical priors plus topology awareness” paradigm demonstrates that heterogeneous-aware, distributed coordination is essential for real-time digital twin services at scale.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Urban Digital Twin
  topic: keywords
subject: Distributed Task Scheduling
  topic: keywords
subject: Heterogeneous Edge Computing
  topic: keywords
subject: Deep Reinforcement Learning
  topic: keywords
creator: Xiang Li
creator: Xiang Wang
creator: Wei Peng
rights: Copyright (c) 2026 Xiang Li, Xiang Wang, Wei Peng https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-29
publication date: 2026-06-29
doi: 10.4108/eetsis.11900

Uncertainty-Aware Decision-Making of Robust Bayesian Networks with Distributed Data Security in Adaptive Artificial Intelligence Systems
type: item
description: INTRODUCTION: The reliability of decision-making in adaptive artificial intelligence (AI) systems is limited by uncertain factors such as noise in multi-source sensing data and conflicts in decision-making objectives. Furthermore, in distributed multi-node collaborative environments, challenges such as cross-node data leakage and edge-node privacy risks further exacerbate decision uncertainty. OBJECTIVES: To address this issue, an uncertainty-aware decision-making method for adaptive AI systems based on robust Bayesian networks is proposed, with a specific focus on distributed data security and privacy protection. METHODS: A robust Bayesian network-based model is proposed. Multi-source sensing data are fused using adaptive weighted minimum mean square error optimization. Secure distributed aggregation is achieved through encrypted aggregation and differential privacy mechanisms. Federated transfer learning is introduced to prevent raw data sharing, and minimax estimation is used to correct missing-data bias. RESULTS: Experimental results on distributed inspection robot clusters show that the proposed method effectively achieves uncertainty-aware decision-making under dynamic and obstacle-affected scenarios while maintaining privacy protection and data security. CONCLUSION: The proposed framework improves decision robustness and privacy preservation in distributed adaptive AI systems by integrating Bayesian inference, federated learning, and secure data fusion strategies.
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: distributed data security
  topic: keywords
subject: privacy protection
  topic: keywords
subject: secure distributed aggregation
  topic: keywords
subject: robust bayesian network
  topic: keywords
subject: adaptive artificial intelligence system
  topic: keywords
subject: uncertainty
  topic: keywords
subject: perception decision-making
  topic: keywords
subject: adaptive wighted fusion
  topic: keywords
subject: federated transfer learning
  topic: keywords
creator: Keli Wei
creator: Taihui Mao
creator: Yongchao Sun
creator: Xingyi Song
rights: Copyright (c) 2026 Keli Wei, Taihui Mao, Yongchao Sun, Xingyi Song https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-25
publication date: 2026-06-25
doi: 10.4108/eetsis.12388

Research on Quantum Privacy Protection Framework and Scene Adaptation for Edge Identity Authentication in Distributed Cross Domain Networks
type: item
description: This paper proposes a universal post quantum privacy protection edge identity authentication framework to address the challenges faced by edge identity authentication in distributed cross domain networks, such as quantum attack threats, cross domain data privacy breaches, and difficulties in coordinating anonymity protection and compliance supervision. The framework adopts an optimized lattice based linkable ring signature protocol to meet the lightweight operation requirements of edge nodes and prevent the risk of leakage in identity data interaction; Design traceability constraints and controllable cross domain traceability mechanisms based on the linkability feature of signatures, balancing user privacy and regulatory requirements. Prove that the scheme possesses unforgeability, strong anonymity, and quantum resistance under the random oracle model. After optimizing the algorithm and interaction logic, the authentication efficiency is improved by 8% to 15% compared to similar solutions, and it is adapted to the low computing power and low latency characteristics of edge nodes. Combining zero knowledge proof to build a lightweight data collection mechanism and achieve privacy protection throughout the entire data process. This article uses the integrated aviation tourism system as a typical application case to verify that the proposed framework can be widely applied to various distributed cross domain networks and identity authentication systems.
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: Distributed Cross-Domain Network
  topic: keywords
subject: Edge Identity Authentication
  topic: keywords
subject: Privacy Protection
  topic: keywords
subject: Post-Quantum Linkable Ring Signature
  topic: keywords
subject: Lattice-Based Cryptography
  topic: keywords
creator: Luo Ming
creator: Li Jinjun
rights: Copyright (c) 2026 Luo Ming, Li Jinjun https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-01
publication date: 2026-07-01
doi: 10.4108/eetsis.12354

Optimization of Deep Learning-Based Patent Infringement Text Comparison and Retrieval Algorithms
type: item
description: INTRODUCTION: Patent texts may present challenges in accurately identifying potential patent infringement risks due to subtle differences in technical features. OBJECTIVES: Deep learning effectively models the complex semantic structures and local feature correlations within patent texts by integrating deep semantic representations and deep matching mechanisms, thereby enhancing sensitivity to minute technical variations and improving decision support for infringement risk assessment. OBJECTIVES: this study explores deep learning-based optimization methods for patent infringement risk text comparison and retrieval algorithms. A macro-level patent text comparison and retrieval layer is constructed by integrating Word2vec and LDA topic models. This layer models patent text semantics, generates word-level semantic vectors for patents, and enables rapid comparison and retrieval of candidate patent sets related to the target patent at the technical feature semantic level. A COV-BiGRU twin neural network is introduced as the fine-grained comparison and retrieval layer. This layer optimizes the macro-level retrieval algorithm by performing granular semantic matching and Manhattan distance calculations between candidate patent texts and the target patent text. Based on these results, patent infringement risk retrieval are retrieved. RESULTS: Results demonstrate that this method reduces the candidate patent text set size to 0.457% of the full database during macro-level retrieval. CONCLUSION: In the fine-grained retrieval optimization phase, it achieves 100% recall of known high-risk patents while effectively excluding non-infringing patents that share similar technical themes but differ in specific technical features. This validates the preliminary effectiveness of the optimized dual-layer retrieval algorithm for texts with fine-grained differences on the tested patent corpus. This validates the preliminary effectiveness of the optimized dual-layer retrieval algorithm for t…
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: deep learning
  topic: keywords
subject: patent infringement
  topic: keywords
subject: text comparison
  topic: keywords
subject: retrieval algorithm optimization
  topic: keywords
subject: LDA topic model
  topic: keywords
subject: COV-BiGRU twin neural network
  topic: keywords
creator: Juan Zou
rights: Copyright (c) 2026 Juan Zou https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
doi: 10.4108/eetsis.12346

Energy-Efficient Lightweight Edge Inference via MOSI-AirComp: Over-the-Air Convolution and Communication-Aware Dual-Branch Training
type: item
description: Lightweight, energy-efficient edge intelligence underpins next-generation pattern recognition for IoT and wireless edge computing. Over-the-air computing (AirComp) is a promising communication-computation integration paradigm, yet its distributed inference deployment is severely hindered by signal phase misalignment and channel-induced performance degradation. This paper proposes a lightweight energy-efficient edge inference framework based on the novel Multiple-Output Single-Input AirComp (MOSI-AirComp) architecture, which inherently eliminates the phase alignment issue of traditional AirComp systems. A communication-aware dual-branch training strategy is introduced to boost robustness against wireless channel impairments without compromising inference efficiency, incorporating channel fading and noise in training while keeping inference model complexity unchanged for adaptive recognition in dynamic edge environments. Additionally, a weight-aware power control scheme enables over-the-air convolution, executing multiply–accumulate operations via wireless signal superposition. An improved TSP-based node selection and resource scheduling algorithm, considering model weights and path loss, achieves a desirable energy-accuracy trade-off for collaborative edge inference. Extensive simulations on MNIST/CIFAR-10 with LeNet-5/VGGNet-16 show the framework significantly improves inference accuracy and MSE performance under various SNRs and power constraints, while reducing edge device latency and computational load, providing an effective solution for lightweight energy-efficient pattern recognition in edge intelligence systems. The proposed design also provides quantization-friendly lightweight benefits: CNN weights and intermediate features can be mapped to bounded antenna-level power-control factors and low-bit transmitted amplitudes, thereby reducing high-precision multiply-accumulate operations, memory access, latency, and energy consumption on resource-constrained edge …
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
creator: YUSHUAI ZHAO
rights: Copyright (c) 2026 YUSHUAI ZHAO https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-11
publication date: 2026-06-11
doi: 10.4108/eetsis.12310

Generative AI-based privacy protection and security management mechanism for distributed financial systems
type: item
description: In distributed financial systems, cross-institutional data sharing faces significant security challenges, including sensitive data leakage risks, unauthorized access, and inadequate privacy protection mechanisms. To address these issues, this paper proposes a generative AI-based privacy protection and security management mechanism for distributed financial systems. The proposed method employs a Gradient Penalty Generative Adversarial Network (GP-GAN) to generate privacy-preserving synthetic data that retains the statistical distribution characteristics of original financial data without exposing sensitive information such as account details and transaction records. A blockchain-based fine-grained access control model is constructed to allocate data access permissions according to user roles (ordinary users, financial institution administrators, regulators) and standardize access processes through smart contracts. The Probabilistic Neural Network (PNN) is restructured from risk prediction to abnormal attack detection, enabling real-time identification of side-channel attacks and data injection attacks by analyzing node data transmission characteristics. Furthermore, a game-theoretic weighting method balances multi-node security collaboration and data sharing efficiency. Experimental results demonstrate that the proposed mechanism achieves a privacy protection strength of 92.6%, an access control accuracy of 96.3%, an attack detection recall rate of 89.7%, and an edge node processing latency below 50 ms, effectively ensuring data security in distributed financial systems.
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: distributed financial system
  topic: keywords
subject: privacy computing
  topic: keywords
subject: generative artificial intelligence
  topic: keywords
subject: access control
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: data security
  topic: keywords
subject: federated learning
  topic: keywords
creator: Xiaochuan Tian
rights: Copyright (c) 2026 Xiaochuan Tian https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-19
publication date: 2026-05-19
doi: 10.4108/eetsis.12262

Edge Computing Communication Privacy Protection Method Based on Federated Learning Algorithm
type: item
description: Data heterogeneity, the complexity of privacy budget allocation, and the imbalance between privacy and performance lead to a limited scope of privacy protection constraints and fail to ensure data integrity. Therefore, an edge computing communication privacy protection method based on federated learning algorithm is proposed. Participants use federated learning to locally train the sensing data to obtain a local model, avoiding the interaction of raw data with edge computing nodes and the perception platform. The parameter values of the trained model are perturbed by noise using the adaptive differential privacy technology and uploaded to the edge computing node. The edge computing node performs edge aggregation on the noisy model parameters and uploads them to the perception platform to complete the global aggregation operation, realizing edge computing communication privacy protection. A performance loss constraint mechanism suitable for federated learning is proposed and designed, and the performance loss of the adaptive differential privacy federated model is reduced by optimizing the constraint scope of the loss function, improving the privacy protection effect. Experiments show that this method can effectively add noise to the local model parameters and achieve privacy protection for edge computing communication; the performance loss of this method’s privacy protection is small, about 0.4; when transmitting different types of data in edge computing communication, the data integrity after privacy protection processing by this method is above 0.98, with excellent privacy protection effect.
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: federated learning algorithm
  topic: keywords
subject: edge computing
  topic: keywords
subject: communication privacy protection
  topic: keywords
subject: local model
  topic: keywords
subject: edge aggregation
  topic: keywords
subject: adaptive differential
  topic: keywords
creator: Jiamei Xue
creator: Wengang Yan
rights: Copyright (c) 2026 Jiamei Xue, Wengang Yan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/eetsis.12254

A Predictive and Multi-Objective Cloud–Edge Scheduling Framework with Task-Aware Encoding for Real-Time Educational Services
type: item
description: INTRODUCTION: With the rapid expansion of real-time interactive educational applications, including smart classrooms, online assessments, and learning behavior analytics, cloud–edge collaborative computing has become a critical infrastructure for ensuring low-latency and high-reliability service delivery. However, existing scheduling frameworks often rely on reactive or coarse-grained strategies that inadequately capture task-level latency sensitivity and system dynamics, resulting in inefficient resource utilization and unstable performance under high-concurrency educational workloads. OBJECTIVES: This study aims to design a latency-aware cloud–edge collaborative scheduling architecture capable of accommodating heterogeneous educational tasks, dynamically adapting to fluctuating resource and network conditions, and providing differentiated service prioritization to meet stringent real-time performance requirements. METHODS: The proposed architecture integrates three tightly coupled components: (1) a task feature encoder that models multimodal educational tasks in terms of computational load, data scale, and latency sensitivity; (2) a latency prediction model that leverages historical system states to anticipate future end-to-end latency trends; and (3) a multi-objective scheduling strategy that jointly optimizes latency, resource utilization, and migration cost through adaptive decision-making across cloud and edge nodes. RESULTS: Experimental evaluations conducted on real-world educational task datasets demonstrate that the proposed approach significantly outperforms mainstream reinforcement learning–based schedulers. Specifically, it reduces average end-to-end latency by 18.1%, improves average resource utilization by 8.0%, and achieves a task success rate of 96.8% under high-concurrency conditions, while maintaining lower latency jitter and migration overhead. CONCLUSION: The proposed latency-aware cloud–edge collaborative scheduling architecture provides an eff…
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: cloud-edge collaborative computing
  topic: keywords
subject: latency-aware scheduling
  topic: keywords
subject: distributed educational services
  topic: keywords
subject: task feature encoding
  topic: keywords
subject: multi-objective optimization
  topic: keywords
creator: Yao Sun
creator: Zhaozhen Liang
creator: Jingxin Liu
rights: Copyright (c) 2026 Yao Sun, Zhaozhen Liang, Jingxin Liu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-27
publication date: 2026-05-27
doi: 10.4108/eetsis.11568

Agricultural Supply Chain Security Management Based on Distributed Blockchain and Time Series Analysis: Focus on Cross-Domain Data Security and Privacy Computing
type: item
description: The agricultural supply chain is facing practical challenges such as data privacy breaches and insufficient cross domain data collaboration control in distributed scenarios. This article proposes a collaborative management mechanism that integrates distributed blockchain, time series analysis, and privacy computing to study data security in distributed agricultural supply chains. This article first elaborates on the theoretical basis of relevant technologies, with a focus on how blockchain achieves privacy isolation for cross node data transmission through asymmetric encryption technology, uses smart contracts to achieve dynamic permission control and operation tracing of distributed nodes, and adapts to the dynamic monitoring needs of distributed data streams with the real-time advantage of time series analysis; On this basis, a management mechanism covering overall architecture, node collaboration, anomaly monitoring, and cross domain data flow control was designed, and an algorithm model including data preprocessing, blockchain node feature extraction, privacy protection data processing, time series anomaly detection and analysis was constructed. Through experimental verification, accuracy, recall rate, RMSE, MAE and other indicators were evaluated using one-year operational data from a certain agricultural supply chain scenario as a sample. The results showed that the accuracy of the experimental group was 92%, the recall rate was 88%, the RMSE was 12.5, and the MAE was 9.8, all of which were better than the control group. Research has shown that this collaborative mechanism and algorithm model can effectively enhance the distributed data security protection, cross domain data flow control, and anomaly recognition capabilities of agricultural supply chains, solve the data security and privacy protection problems of agricultural supply chains in distributed scenarios, provide new methods for the safe and stable operation of agricultural supply chains, and have im…
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: Distributed Agricultural Supply Chain
  topic: keywords
subject: Data Security
  topic: keywords
subject: Privacy Protection
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Joint Learning
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Cross Domain Data Flow
  topic: keywords
creator: Yang Xuchang
creator: Hu Juhu
rights: Copyright (c) 2026 Yang Xuchang, Hu Juhu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-18
publication date: 2026-06-18
doi: 10.4108/eetsis.12166

Retraction Notice: The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data
type: item
description: We, the Publisher, have retracted the following article: Fan et al. (2025). The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data. EAI Endorsed Scal Inf Syst. https://doi.org/10.4108/eetsis.7015 The article has been retracted due to authorship dispute. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
subject: Retractions
  topic: section
creator: Xiaoxue Fan
creator: Bingxin Zhang
creator: Ping Zhang
rights: Copyright (c) 2026 Xiaoxue Fan, Ping Zhang, Bingxin Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-04
publication date: 2026-03-04
doi: 10.4108/eetsis.12091

A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
type: item
description: The rapid proliferation of rooftop photovoltaics and behind-the-meter battery storage is creating systemic risks in distribution networks. This includes local network constraint violations, insufficient outage resilience, and enlarged cybersecurity attack surfaces, leaving millions of demand-side distributed energy resources (DERs) to operate without coordinated oversight. Existing solutions (dynamic operating envelopes, virtual power plants, peer-to-peer trading, and single-household energy management systems) each address only partial aspects of this challenge. Based on multi-agent reinforcement learning, this paper proposes a Decentralised Coordination Framework (DCF) that organises the energy dispatch into a three-tier hierarchical architecture. Specifically, it has a user layer executing Proximal Policy Optimisation (PPO) for local dispatch, a feeder layer in which a dynamically elected L1 leader applies MADDPG to coordinate community flexibility via a Virtual Aggregation Unit (VAU); and a cross-feeder layer where an L2 leader manages inter-community balancing and market interfaces. It integrates a directed acyclic graph (DAG)-based verifiable execution ledger, Paillier homomorphic encryption, and LLM-based anomaly detection to enhance security. Potential market participation pathway and revenue distribution mechanism are proposed to align with the Australian National Electricity Market. The DCF provides a scalable, market-ready foundation for commercial demandside DER deployment under high renewable penetration.
subject: Research articles
  topic: section
subject: Distributed energy resources
  topic: keywords
subject: Multi-agent reinforcement learning
  topic: keywords
subject: Decentralised coordination
  topic: keywords
subject: Electricity market participation
  topic: keywords
creator: Bo Li
creator: Yong-Feng Ge
creator: Yuan Miao
creator: Ruoxuan Cui
creator: Xin Li
creator: Kate Wang
creator: Hua Wang
rights: Copyright (c) 2026 Bo Li, Yong-Feng Ge, Yuan Miao, Ruoxuan Cui, Xin Li, Kate Wang, Hua Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
doi: 10.4108/eetsis.12071

Adaptive Scheduling and Compute Demand Prediction for Animation Rendering Tasks Using Machine Learning
type: item
description: INTRODUCTION: The animation industry’s growing demand for high-resolution, multi-scene rendering has led to highly heterogeneous and dynamic workloads, challenging traditional scheduling systems. OBJECTIVES: This study aims to overcome the limitations of static or rule-based schedulers by developing an adaptive, learning-driven approach that dynamically matches rendering tasks to compute resources under fluctuating conditions. METHODS: We propose a machine learning-based scheduling and compute prediction model that integrates multi-dimensional task feature vectors with a lightweight sequence prediction network and a multi-objective optimization strategy. This enables real-time estimation of rendering duration, compute demand, and node load for dynamic resource allocation. The framework explicitly models temporal dependencies across frames and accounts for GPU heterogeneity through unified task representations. RESULTS: Evaluated on a cluster with 12 representative animation scene categories and 3 GPU architectures, our method shortens average task latency by 11.0%, improves node utilization by 3.9%, and reduces energy consumption by 7.8% over the best baseline, while maintaining robust performance even under severe (20%) state noise. CONCLUSION: The proposed model demonstrates strong adaptability and efficiency in complex rendering environments, offering a scalable foundation for intelligent, cross-platform scheduling in next-generation rendering infrastructures, particularly beneficial for cloud rendering, virtual production, and energy-constrained GPU clusters, while supporting real-time responsiveness and cost-effective resource management.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: animation rendering task scheduling
  topic: keywords
subject: machine learning prediction model
  topic: keywords
subject: compute demand forecasting
  topic: keywords
subject: adaptive resource allocation
  topic: keywords
subject: heterogeneous computing clusters
  topic: keywords
creator: Yi Yang
creator: Muchen Zhang
creator: Zhaopei Wang
rights: Copyright (c) 2026 Yi Yang, Muchen Zhang, Zhaopei Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-27
publication date: 2026-05-27
doi: 10.4108/eetsis.12068