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DSCR: A Dynamic Secure Clustering Routing Scheme for UANETs Based on Reputation Mechanism

IEEE Internet of Things Journal 2025年第10期12卷 14109-14123页

作者： Gong, Yinyan Chen, Yuxiang Li, Kuanching Liang, Wei Li, Xiong Wang, Jin Xiang, Yang Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Sanya Research Institute Hunan University of Science and Technology Sanya572024 China School of Computer Science and Engineering Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Swinburne University of Technology School of Software and Electrical Engineering HawthornVIC3122 Australia

In UAV ad hoc networks (UANETs), rapid movement of nodes leads to frequent changes in network topology, increasing the risk of packet loss and affecting data transmission. Furthermore, current research on drone clustering lacks security considerations, which reduces the reliability of data transmission. Due to this, improving the stability and reliability of network data transmission in dynamically changing UANETs remains a challenge. In this work, we propose the reputation mechanism dynamically secure cluster routing scheme (DSCR) for UANETs, a design for cluster head election in UANETs using the evaluated value and reputation value of drones and forms clusters through this reputation mechanism to avoid malicious nodes from interfering with the clusters to improve the security of UANETs. We apply a residual link survival time-based intra- and inter-cluster algorithm based on residual link survival time for data forwarding of nodes, optimizing the dynamic routing strategy and reducing the packet loss rate of nodes. In addition, reinforcement learning is used in UANETs to achieve cluster decisions based on the current network state and cluster dynamics, effectively improving the stability of clusters. Experiments were conducted on the proposed method to verify its efficiency and stability. Compared to the ICRA, RICR, and DCA algorithms, the stability of the cluster is improved by 11.92%, 28.12%, and 75.2%, respectively, and the packet loss rate reduced by 41.48%, 44.17%, and 55.74%, demonstrating that DSCR is a compelling dynamic routing solution applicable to UANETs. © 2014 IEEE.

关键词： Drones

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Imbalanced multilabel retinal disease classification using threshold moving and ensemble learning

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Multimedia Tools and Applications 2025年 1-19页

作者： Pendharkar, Gaurav Balaji, Sudharshanan Kumar, B. Muhesh Malathi, G. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

Billions of people worldwide are affected by vision impairment majorly caused due to age-related degradation and refractive errors. Diabetic Retinopathy(DR) and Macular Hole(MH) are among the most prevalent senescent retinal diseases. Machine Intelligence can assist ophthalmologists and clinicians in fast and accurate disease diagnosis by identifying patterns in disease progression for a better healthcare system. In this paper, the Retinal Fundus Multi-disease Image Dataset (RFMiD) is used to design a machine intelligence system with two chief components namely a disease risk classifier, and a multi-label classifier. The disease risk classifier predicts whether the retinal fundus image is infected or not. Based on the prediction of the disease risk classifier, a multi-label classifier can be applied to obtain probabilities for the susceptibility to DR and MH. Finally, an ensemble is employed with the best of 3 models for each classifier. The disease riskpredictor attained a peak F1-score of 88%, while the multi-label classifier achieved an Area Under the Curve(AUC) score of 86%. However, the individual binary classifiers for DR and MH reached maximum F-scores of 91% and 93%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Risk perception

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On the Stability of Random Access with Congestion Control 14th

On the Stability of Random Access with Congestion Control

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14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

作者： Xu, Yu Cui, Chen Wang, Zhenyong Guo, Qing School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China Songjiang Laboratory Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9783031861956

The random access protocol is a critical technique widely used in various wireless networks. A significant challenge is analyzing the stability of the random access system when a large number of users transmit packets independently. Although the congestion control policy can enhance the stability of the system, there is currently a lack of quantitative analysis on the stability of random access with congestion control. In this paper, we analyze the random access system with congestion control and quantify its stability. Specifically, we consider a congestion control scheme to enhance the stability of the random access system and present the graphical analytical model. Based on this graphical model, we qualitatively discuss the behavioral variations of the system as it works and quantify its stability. © ICST institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Congestion control (communication)

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Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems

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Energy Informatics 2025年第1期8卷 1-43页

作者： Rajaperumal, T.A. Columbus, C. Christopher School of Electrical Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

The evolution of the electrical grid from its early centralized structure to today’s advanced "smart grid" reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in efficiency, reliability, and scalability. Over time, the grid has transformed into a decentralized network driven by innovative technologies, particularly artificial intelligence (AI). AI has become instrumental in enhancing efficiency, security, and resilience by enabling real-time data analysis, predictive maintenance, demand-response optimization, and automated fault detection, thereby improving overall operational efficiency. This paper examines the evolution of the electrical grid, tracing its transition from early limitations to the methodologies adopted in present smart grids for addressing those challenges. Current smart grids leverage AI to optimize energy management, predict faults, and seamlessly integrate electric vehicles (EVs), reducing transmission losses and improving performance. However, these advancements are not without limitations. Present grids remain vulnerable to cyberattacks, necessitating the adoption of more robust methodologies and advanced technologies for future grids. Looking forward, emerging technologies such as Digital Twin (DT) models, the Internet of Energy (IoE), and decentralized grid management are set to redefine grid architectures. These advanced technologies enable real-time simulations, adaptive control, and enhanced human–machine collaboration, supporting dynamic energy distribution and proactive risk management. Integrating AI with advanced energy storage, renewable resources, and adaptive access control mechanisms will ensure future grids are resilient, sustainable, and responsive to growing energy demands. This study emphasizes AI’s transformative role in addressing the challenges of the early grid, enhancing the capabilities of the present smart grid, and shaping a secure

关键词： Risk management

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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A Diffusion Model for Traffic Data Imputation

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 606-617页

作者： Bo Lu Qinghai Miao Yahui Liu Tariku Sinshaw Tamir Hongxia Zhao Xiqiao Zhang Yisheng Lv Fei-Yue Wang the School of Artificial Intelligence University of Chinese Academy of Sciences Baidu Inc. IEEE the State Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Meituan Guangdong University of Technology the Department of Transportation Engineering School of Transportation Science and Technology Harbin Institute of Technology

Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS) in the real world. As a state-of-the-art generative model, the diffusion model has proven highly successful in image generation, speech generation, time series modelling *** now opens a new avenue for traffic data imputation. In this paper, we propose a conditional diffusion model, called the implicit-explicit diffusion model, for traffic data imputation. This model exploits both the implicit and explicit feature of the data simultaneously. More specifically, we design two types of feature extraction modules, one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series. This approach not only inherits the advantages of the diffusion model for estimating missing data, but also takes into account the multiscale correlation inherent in traffic data. To illustrate the performance of the model, extensive experiments are conducted on three real-world time series datasets using different missing *** experimental results demonstrate that the model improves imputation accuracy and generalization capability.

关键词： Data imputation diffusion model implicit feature time series traffic data

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Experimental research on the cold storage and release performance of air conditioning systems using phase change tubes 3

Experimental research on the cold storage and release perfor...

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2024 3rd International Conference on Energy and Power Engineering, EPE-AEIC 2024

作者： Jia, Rongrong Qi, Hong School of Energy Science and Engineering Harbin Institute of Technology Harbin150006 China

A phase-change cold storage air conditioning system incorporates phase-change rods to facilitate thermal energy storage and subsequent release for temperature regulation. Water and PCM systems can be utilized as thermal storage media to accumulate cold energy during the nocturnal period, thereby facilitating the release of this stored cold energy throughout daylight hours to accomplish the intended cooling objective. This work presents an innovative approach involving phase change materials, which investigates the methodologies for load balancing through peak shaving and valley filling to achieve equilibrium in power distribution. The phase-change material with a phase-change temperature of 6 ., is filled into an aluminium rod with a diameter of 50mm. 950 PCM aluminium rods are put vertically in the space of about 4.3 m3 tank. The duration for the system to achieve the cooling of the storage compartment, along with its resultant impact on investigations into the differential effects of various cooling storage and thermal release conditions on the water-based and Phase-Change Composite (PCCS) systems, were conducted. © Published under licence by IOP Publishing Ltd.

关键词： Air conditioning

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Analyzing User Needs of Autonomous Transportation Systems with Activity Theory and Knowledge Graph

Analyzing User Needs of Autonomous Transportation Systems wi...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zheng, Lai Bao, Chunrong Wei, Wei School of Transportation Science and Engineering Harbin Institute of Technology Harbin150000 China

ISBN: (纸本)9789819639724

Thanks to the progress of emerging technologies such as autonomous driving and vehicle networking, China’s transportation system has been continuously and rapidly developing towards autonomy. Autonomous Transportation System (ATS) will integrate the new concepts of modern transportation on a basis of traditional Intelligent Transportation System (ITS), and is committed to achieving the great transportation concepts of collaborative services, intelligent travel and comprehensive transportation. This paper uses the decomposition model of activity theory to elicit the user needs of ATS from multiple dimensions and constructs a knowledge graph network of ATS user needs to understand and analyze. Combining the content of activity theory and the structure results of the knowledge graph for user needs, the core components of the transportation system and complex interaction among them are studied, which helps to expand the user needs of ATS from two dimensions of technology promotion and policy guidance, thereby promoting the development of ATS. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Autonomous vehicles

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Hyperspectral Image Reconstruction for Interferometric Spectral Imaging System with Degradation Synthesis

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Journal of Beijing institute of technology 2025年第1期34卷 42-56页

作者： Yuansheng Li Xiangpeng Feng Siyuan Li Geng Zhang Ying Fu School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Xi’an Institute of Optics and Precision Mechanics CASXi’an 710119China

Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep ***, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based *** addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.

关键词： hyperspectral imaging degradation modeling data synthesis spectral reconstruction

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Hyperbolic Sine Function-Based Full-State Feedback Attitude Tracking Control for Rigid Spacecraft

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3412-3424页

作者： Dong, Rui-Qi Wu, Ai-Guo Li, Bin Duan, Guang-Ren Department of Automation Shenzhen518055 China Sichuan University School of Aeronautics and Astronautics Chengdu610207 China Southern University of Science and Technology Center for Control Science and Technology Shenzhen518055 China Harbin Institute of Technology Center for Control Theory and Guidance Technology Harbin150001 China

The attitude tracking control with unwinding-free performance for rigid spacecraft is studied in this article. A full-state feedback control law based on a hyperbolic sine function is developed such that the resulted closed-loop system can achieve two stable equilibria. By Lyapunov stability theory and Barbalat's Lemma, it is proven that the obtained closed-loop system is almost globally asymptotically stable, and achieves unwinding-free performance. Further, by constructing a strict Lyapunov function, it is demonstrated that the two stable equilibria are exponentially stable. Moreover, subsets of attraction regions corresponding to each stable equilibrium are characterized. The simulation results illustrate that the proposed attitude control scheme can effectively avoid the unwinding problem during attitude tracking. © 2025 IEEE.

关键词： Lyapunov functions

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