EAI Endorsed Transactions on Scalable Information Systems
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
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.
Correction: Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection
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.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-sa/4.0
Correction: A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar
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.
Research on digital workshop dynamic scheduling technology based on blockchain
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.
Resiliency and Adaptability for Future Manufacturing: AI Driven Recovery and Response Mechanisms
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
Optimization of Circular-Rail RGV Scheduling in a Tobacco Warehouse
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.
Copyright (c) 2026 Dingke Shi; Zhihui Ye, Nanzhe Ding, Jie Gao, Chao Cheng, Wenwen Lin https://creativecommons.org/licenses/by-nc-sa/4.0
A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar
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 DBSCAN (0.7681), and a Davies-Bouldin Index (DBI) of 0.1936, which is 10.66% lower than that of the traditional DBSCAN (0.2167). This effectively resolves the long-distance target missing detection issue prevalent in conventional methods. Additionally, the proposed tracking method achieves an optimal balance between tracking accuracy and continuity, with an average single-frame processing time of only 1.08 ms, fully meeting the real-time requirements of roadside perception systems. CONCLUSION: The proposed robust vehicle target tracking method based on roadside millimeter-wave radar provides a reliable all-weather, high-precision, and real-time roadside vehicle perception solution for intelligent transportation systems. Thus, this work holds significant engineering application potential and theoretical value.
Copyright (c) 2026 Hongcai Chen, Yu Cheng, Yaheng Ren, Yaoxing Kang https://creativecommons.org/licenses/by-nc-sa/4.0
Human-Centric Smart Manufacturing under Industry 5.0:IIoT-Enabled Machine Learning for Real-Time Fault Identification and Adaptive Workforce Scheduling
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.
Copyright (c) 2026 Yaocong Xie, Ning Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Intelligent Collaborative Resource Allocation for Mechanical Manufacturing Edge Networks: A Deep Reinforcement Learning Approach
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.
Copyright (c) 2026 Kaile Xiao, Xiajing Wang, Jing Wang https://creativecommons.org/licenses/by-nc-sa/4.0
A Cross-Domain Data Flow Security Governance and Privacy Protection Mechanism for Distributed Digital Art Platforms
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.
Data Security and Privacy Protection in New Distributed Networks and System
Copyright (c) 2026 Meixuan Lv https://creativecommons.org/licenses/by-nc-sa/4.0
Research on a Deep Learning-Based Hybrid Attention and Regularised Supervised Detection Model for Surface Defects in Precision Components
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.
Copyright (c) 2026 Honggang Duan https://creativecommons.org/licenses/by-nc-sa/4.0
ARaNet: Attention and Residual Aware Network for Resilient Digital Twins in Rail Transit Equipment Manufacturing
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 predicts coordinate offsets to rectify local geometric errors. This capability is particularly valuable for precision metrology in component manufacturing. RESULTS: Experimental evaluation on the ShapeNet and ModelNet40 datasets demonstrates that ARaNet achieves substantial improvements over benchmark models PCN and PF-Net. Quantitative assessment shows average reductions of 12.9% and 23.8% in the Gt-to-Pre and Pre-to-Gt metrics, respectively. The generated point clouds exhibit more uniform distributions and superior detail restoration, particularly for complex mechanical geometries with curved surfaces and structural joints characteristic of rail transit equipment components. Conclusion:The effectiveness of integrating channel attention with residual refinement mechanisms for industrial 3D sensor data recovery is validated. ARaNet provides a robust data recovery foundation for resilient digital twin systems in rail transit equipment manufacturing, enabling sustained operational capability and rapid geometric reconstruction even when sensor data is compromised by production environment disruptions. However, it should be noted that ARaNet is currently validated exclusively on synthetic training data, and its performance under extremely high missing-region ratios or in real industrial deployment scenarios without domain adaptation warrants further investigation.
Copyright (c) 2026 Xi Chen, Xiaolong Gao, Hanyue Zhan, Wanting Liu https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Based Optimization Method for Coverage of IoT Upload Link Communication
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.
Copyright (c) 2026 Sheng Ye https://creativecommons.org/licenses/by-nc-sa/4.0
Data Security and Privacy Protection in Distributed Digital Economy: Economic Impacts and Governance Mechanisms
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: Based on empirical analysis results, optimization paths are proposed from four levels: collaborative governance, technology empowerment, regional balance, and institutional improvement, in order to improve the level of data security and privacy protection in the distributed digital economy.
Copyright (c) 2026 Cuilan Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Real-Time Scheduling Mechanisms for Heterogeneous Distributed Systems in Edge-Enabled Urban Renewal Digital Twin Platforms
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.
Scheduling optimization and load balancing in scalable distributed systems
Copyright (c) 2026 Xiang Li, Xiang Wang, Wei Peng https://creativecommons.org/licenses/by-nc-sa/4.0
Uncertainty-Aware Decision-Making of Robust Bayesian Networks with Distributed Data Security in Adaptive Artificial Intelligence Systems
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.
Copyright (c) 2026 Keli Wei, Taihui Mao, Yongchao Sun, Xingyi Song https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Quantum Privacy Protection Framework and Scene Adaptation for Edge Identity Authentication in Distributed Cross Domain Networks
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.
Copyright (c) 2026 Luo Ming, Li Jinjun https://creativecommons.org/licenses/by-nc-sa/4.0
Optimization of Deep Learning-Based Patent Infringement Text Comparison and Retrieval Algorithms
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 texts with fine-grained differences on the tested patent corpus.
Copyright (c) 2026 Juan Zou https://creativecommons.org/licenses/by-nc-sa/4.0
Energy-Efficient Lightweight Edge Inference via MOSI-AirComp: Over-the-Air Convolution and Communication-Aware Dual-Branch Training
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 devices.
Copyright (c) 2026 YUSHUAI ZHAO https://creativecommons.org/licenses/by-nc-sa/4.0
Generative AI-based privacy protection and security management mechanism for distributed financial systems
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.
Copyright (c) 2026 Xiaochuan Tian https://creativecommons.org/licenses/by-nc-sa/4.0
Edge Computing Communication Privacy Protection Method Based on Federated Learning Algorithm
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.
Copyright (c) 2026 Jiamei Xue, Wengang Yan https://creativecommons.org/licenses/by-nc-sa/4.0
A Predictive and Multi-Objective Cloud–Edge Scheduling Framework with Task-Aware Encoding for Real-Time Educational Services
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 effective and scalable solution for guaranteeing low-latency performance in real-time educational services. By combining task-aware representation, predictive latency modeling, and multi-objective optimization, the framework offers strong practical value for deployment in smart classrooms and large-scale online education platforms.
Copyright (c) 2026 Yao Sun, Zhaozhen Liang, Jingxin Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Agricultural Supply Chain Security Management Based on Distributed Blockchain and Time Series Analysis: Focus on Cross-Domain Data Security and Privacy Computing
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 important engineering practical value and promotion significance.
Copyright (c) 2026 Yang Xuchang, Hu Juhu https://creativecommons.org/licenses/by-nc-sa/4.0
Retraction Notice: The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data
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”.
Copyright (c) 2026 Xiaoxue Fan, Ping Zhang, Bingxin Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
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.
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
Adaptive Scheduling and Compute Demand Prediction for Animation Rendering Tasks Using Machine Learning
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.
Copyright (c) 2026 Yi Yang, Muchen Zhang, Zhaopei Wang https://creativecommons.org/licenses/by-nc-sa/4.0
A Scalable Cross-Chain Data Asset Rights Confirmation Framework for Distributed Systems Based on Hybrid Post-Quantum Zero-Knowledge Proofs
Abstract: In the era of the digital economy, establishing an efficient and compliant data asset rights confirmation system within scalable distributed infrastructures is of critical importance. However, under heterogeneous distributed ledger environments, data circulation is often trapped in a binary tension between privacy preservation and regulatory accessibility, while facing severe scalability bottlenecks. Existing studies lack a unified solution that simultaneously addresses cross-chain interoperability, post-quantum security, and low-cost verification. To this end, this paper proposes a data asset rights confirmation framework based on hybrid post-quantum zero-knowledge proofs. The framework designs a scalable recursive composition architecture combining Scalable Transparent Argument of Knowledge (STARKs) and Succinct Non-interactive Argument of Knowledge (SNARKs), leveraging off-chain compressed permutation to significantly reduce on-chain storage overhead. In parallel, a light-client-based distributed cross-chain state synchronization protocol and a regulation-friendly privacy auditing module (based on threshold encryption) are constructed to ensure transactional atomicity and conditional auditability during data circulation. Experimental evaluations conducted on two datasets, Ethereum NFT transactions and credit card fraud detection, demonstrate that, compared with cross-chain privacy-preserving solutions such as zkCross, the proposed framework reduces on-chain verification Gas costs by approximately 18.2%, compresses proof size to 0.28 kB, and achieves a peak throughput of 1,618 Transactions Per Second (TPS). Moreover, under controlled experimental conditions, the framework attains an audit success rate of 99.6% with only 14.0% performance overhead. Overall, this study alleviates the long-standing trade-offs among privacy protection, regulatory compliance, and computational scalability, and provides a verifiable technical solution for the interoperability and infrastructure development of next-generation distributed systems.
Copyright (c) 2026 Lila Zhang, Zhen Yan https://creativecommons.org/licenses/by-nc-sa/4.0
Numerical Index Modulation Aided Multi-Carrier DCSK System: A Secure Transmission Scheme for Physical Layer Security
This paper presents a novel Numerical Index Modulation-assisted Differential Chaos Shift Keying (NIMDCSK) system, architected on a multi-carrier framework where numerous information-bearing subcarriers are supported by a single common reference subcarrier. The proposed system introduces an additional dimension for data transmission by embedding information bits into the dynamic allocation of signal energy across subcarriers. This mechanism not only boosts the transmission rate but also significantly enhances physical layer security. Specifically, the NIM-DCSK scheme obfuscates its transmission characteristics through pseudo-random energy allocation patterns, where deliberately varying power levels create a complex and non-stationary signal signature. This fluctuating energy distribution effectively masks the underlying data structure, making it extremely difficult for unauthorized eavesdroppers to perform feature analysis or achieve successful decoding. Compared to conventional DCSK systems, comprehensive analyses demonstrate that the NIM-DCSK system achieves substantial gains in both energy efficiency (EE) and spectral efficiency (SE), alongside improved bit error rate (BER) performance under various channel conditions. These advantages are realized without a commensurate increase in structural complexity. The inherent security features, including inherent resistance to passive interception and a reduced vulnerability to statistical analysis, are key benefits. Consequently, this work facilitates the integration of index modulation with chaos-based communications, ensuring high-rate transmissions coupled with robust security. The proposed system is, therefore, highly suitable for applications demanding reliable and secure wireless links, such as secure sensor networks, tactical military communications, and confidential IoT deployments.
Multi-carrier differential chaos shift keying
Copyright (c) 2026 Wang Chen, Chao Duan https://creativecommons.org/licenses/by-nc-sa/4.0
Risk-Aware Secure Routing Mechanism for Power Communication Networks Based on GNNs
INTRODUCTION: As digital transformation progresses, power communication networks become exposed to increasingly complex security threats. Moreover, traditional routing algorithms cannot perceive or respond to dynamic security threats, making reliable data transmission difficult to achieve under network attack conditions. OBJECTIVES: Therefore, this study proposes a risk-aware secure routing (RASR) mechanism for power communication networks based on graph neural networks (GNNs). METHODS: The mechanism first introduces an autoencoder-graph neural network (AGNN) architecture for determining critical nodes, thus establishing a scoring prediction model customized to the structure of power communication backbone networks. It then integrates the essential scores of the nodes, historical failure rates, and traffic load factors to devise a path node risk quantification model. Finally, it incorporates risk quantification results into routing policies to enable proactive routing avoidance and thus bypass high-risk nodes. RESULTS: Experimental results show that the RASR algorithm effectively meets the differentiated service requirements of heterogeneous traffic—including delay-sensitive, bandwidth-sensitive, and reliability-sensitive traffic. Compared with traditional routing algorithms, it demonstrates greater stability and fault tolerance under high-risk attack scenarios while eliminating the need for redundant backup strategies, thereby markedly reducing resource overhead. CONCLUSION: Therefore, the proposed mechanism offers important theoretical support and a novel technical approach for establishing secure, reliable power communication networks.
Copyright (c) 2026 Yanjun Zhao, Yue Zhang, Xiaowei Zhao, Liyu Liu, Xiang Wang, Kaiyue An https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum Communication Networks for Secure Foundation Model Distribution Across Shanxi Power Grid Substations
INTRODUCTION: Foundation models deployed in power grid infrastructure face escalating security threats from advancing quantum computing capabilities, which can compromise classical encryption protecting multi-gigabyte model parameters during distributed transmission and storage across geographically dispersed substations. OBJECTIVES: This research develops and validates a quantum-secured communication framework integrating quantum key distribution with hierarchical encryption mechanisms specifically designed for protecting foundation model parameters in operational power grid environments. METHODS: A dual-channel quantum communication architecture was deployed across five substations in Shanxi Province spanning 580 kilometers, implementing BB84 protocol with decoy state techniques for quantum key generation, dynamic key-data mapping algorithms for parameter encryption, and Ceph-based distributed storage with blockchain audit trails. The system underwent 30-day continuous operational validation protecting a 500-million-parameter Transformer model under real-world conditions including temperature variations (-5°C to 35°C), grid maintenance events, and concurrent SCADA traffic. RESULTS: The framework achieved 99.2% system availability with distance-dependent quantum key distribution rates ranging from 4.5 kbps (50 km) to 0.5 kbps (180 km), quantum bit error rates maintained between 3.2-11.4% within operational thresholds, hierarchical AES encryption throughput of 85 MB/s for model parameters, and storage system performance delivering 8,500-10,800 read IOPS with 1.05 ms average latency. CONCLUSION: This work validates the practical viability of quantum communication networks for securing distributed foundation models in critical power infrastructure, demonstrating information-theoretic security under operational network conditions while establishing integration protocols between quantum key distribution channels and encrypted data transmission pathways for large-scale AI model protection.
Copyright (c) 2026 Jiwu Liu, Kai Xue, Yachen Wang, Chunguang Ren, Xiaojian Zhang, Kai Han https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum Communication-Assisted Synchronization Framework for Distributed 3D Power Grid Models Across Multi-Regional Control Centers
INTRODUCTION: The synchronization of three-dimensional digital twin models across geographically distributed power grid control centers represents a critical challenge in modern power system operations. Traditional synchronization methods including Network Time Protocol (NTP) and Precision Time Protocol (PTP) achieve only millisecond-level accuracy, which proves fundamentally insufficient for monitoring and responding to critical power grid transients that occur on sub-millisecond timescales. The proliferation of renewable energy sources and distributed generation resources further intensifies synchronization requirements. OBJECTIVES: This research develops a comprehensive quantum communication-assisted synchronization framework designed to achieve unprecedented temporal accuracy for distributed 3D power grid models spanning multi-regional control centers while maintaining compatibility with existing infrastructure. METHODS: A hierarchical quantum-classical hybrid architecture is proposed that strategically utilizes quantum entanglement channels for time-critical synchronization signals and classical communication channels for bulk data transmission. An adaptive fault-tolerance mechanism dynamically adjusts quantum error correction strategies based on real-time quantum channel quality assessments, with graceful degradation to classical protocols when necessary. RESULTS: Extensive simulations utilizing 128 IEEE 118-bus system models demonstrate sub-10 nanosecond synchronization precision—representing 10,000-fold improvement over NTP—with end-to-end latencies maintained below 10 microseconds. The framework exhibits robust performance with synchronization success rates exceeding 90% under 15% node failure conditions and demonstrates logarithmic O(log M) time complexity compared to O(M²) scaling for classical consensus protocols. CONCLUSION: The quantum communication-assisted framework delivers superior accuracy, scalability, and fault tolerance compared to classical synchronization protocols, establishing viability for next-generation smart grids with extensive distributed energy resources.
Copyright (c) 2026 Youhui Chen, Zhonghua Lv, Ruixue Hu, Xinying Zhao, Dongxue Li https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum Communication Technology for Optimizing 3D Cost Estimation and Secure Data Transmission in Substation Engineering
INTRODUCTION: The highly sensitive 3D cost estimation data in substation projects faces significant security challenges during distributed transmission, as conventional encryption approaches prove inadequate against quantum computing threats for large-scale engineering data protection. OBJECTIVES: This study aims to develop a quantum communication-based secure transmission framework for substation 3D cost estimation data, employing a three-tier quantum architecture to achieve information-theoretic security levels with robust defense against network attacks. METHODS: The proposed solution employs a three-tier quantum architecture encompassing physical, network, and application layers, with optimized quantum channels for engineering data flow. The framework utilizes the BB84 protocol with the decoy state method, implementing quantum key distribution and classical encryption integration to maintain data integrity through a distributed optimization strategy. RESULTS: Simulation validation across 10-50 node networks demonstrates quantum key generation rates of 8-40 kbps and 23% improvement in network throughput compared to star topology over traditional methods. The system achieves 99.99% reduction in key usage compared to pure quantum approaches while maintaining information-theoretic security levels. CONCLUSION: This research establishes fundamental principles and implementation pathways for quantum communication applications in engineering domains, offering significant advancement in secure data transmission for sensitive industrial applications and providing a reliable solution for protecting critical infrastructure engineering data.
Copyright (c) 2026 Junchao Li, Fei You, Dan Chen https://creativecommons.org/licenses/by-nc-sa/4.0