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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： network topology

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Secrecy Rate Maximization for STAR-RIS-Assisted MIMO Cognitive Radio networks

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IEEE Wireless Communications Letters 2025年第5期14卷 1281-1285页

作者： Huang, Zihao Lei, Xianfu Shen, Meng Wu, Mingjiang Li, Xingwang Karagiannidis, George K. Southwest Jiaotong University School of Information Science and Technology Chengdu610031 China Chengdu Technological University School of Network and Communication Engineering Chengdu611730 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki541 24 Greece

In this letter, we study secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multiple-input multiple-output cognitive radio network, where a secondary transmitter (ST) sends confidential information to a secondary user while sharing the same frequency band with a primary user in the presence of an eavesdropper. We aim to maximize the secrecy rate of the secondary system by jointly optimizing the transmit precoding matrices at the ST, along with the transmission and reflection coefficients of the STAR-RIS. An efficient alternating optimization algorithm based on the penalty method and the generalized S-procedure is proposed to solve the formulated non-convex problem. Simulation results show that the proposed scheme significantly outperforms the conventional RIS in improving the secrecy rate. © 2012 IEEE.

关键词： MIMO Optimization Interference Reflection Eavesdropping Cognitive radio Vectors Security Radio transmitters Precoding

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Interval prediction strategy for the remaining useful life of lithium-ion battery pack considering cell inconsistency

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Journal of Power Electronics 2025年 1-13页

作者： Pang, Xiaoqiong Li, Xiao Kim, Jonghoon Lee, Miyoung Zhao, Zhen Wen, Jie Zeng, Jianchao School of Computer Science and Technology North University of China Taiyuan China Department of Electrical Engineering Chungnam National University Daejeon Korea Republic of Institute of Network Technology Yantai China School of Electrical and Control Engineering North University of China Shanxi Taiyuan China

Accurate and robust remaining useful life (RUL) prediction of lithium-ion battery packs is critical for ensuring system operation reliability and safety. However, the inconsistency accelerates battery pack degradation, making RUL prediction challenging. Furthermore, traditional prediction methods offering specific values cannot meet varying safety and urgency requirements. Aiming at the above problems, this study proposed an RUL interval prediction scheme considering cell inconsistency for lithium-ion battery packs. First, the degradation and inconsistency information of the battery pack is captured by extracting health indicators from the measured data. Second, fuzzy information granulation is introduced to transform the time series data into fuzzy granules, providing upper and lower bounds for interval prediction modeling. Then, long short-term memory is employed for modeling, generating interval prediction results that support decision adjustment for different safety and emergency levels. Finally, the experiment results show that the proposed strategy can effectively cover the real value and achieve reliable interval prediction. It also indicates that the impact of inconsistency on the prediction results becomes more remarkable as the starting point moves back. With 60% training data, the interval prediction evaluation criterion P of the model with inconsistency information is approximately 43% higher than the model without. © The Author(s) under exclusive licence to The Korean Institute of Power Electronics 2025.

关键词： Battery Pack

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TinyML Model for Solar Cell Defect Classification Based on Electroluminescence Images 4th

TinyML Model for Solar Cell Defect Classification Based on E...

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4th International Conference on Electronic engineering and Renewable Energy Systems, ICEERE 2024

作者： Boubaker, S. Moussaoui, C. Mellit, Adel Benghanem, M. Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah21959 Saudi Arabia Faculty of Sciences and Technology University of Jijel Jijel18000 Algeria Department of Physics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia

ISBN: (纸本)9789819606436

Being largely utilized as alternative sources of energy, photovoltaic (PV) panels are being deployed in many locations. However, those panels are extensively being prone to faults. A careful detection and diagnosis of those faults becomes a must for solar energy engineers, practitioners and decision-makers in order to extract the maximum of energy from them. The integration of artificial intelligence (AI) in the domain of solar energy management has become a trend in the few previous years because of the high potential provided by machine learning (ML) and deep learning (DL) algorithms in handling issues related to PV systems such as control and fault detection and isolation (FDI). In this paper, TinyML models for defect classification of photovoltaic modules based on electroluminescence (EL) images were developed. An open-source platform, Edge Impulse, was used to design and deploy the models. Two convolutional neural network models (mobileNetV1 and mobileNetV2) have been used and compared. A dataset of EL with three anomalies was used to develop the models. The validation test shows an accuracy of 88.44 and 76.38% for respectively MobileNet-V1 and MobileNet-V2. The quantized (Int8) model has been integrated into a microcontroller (Arduino Portenta H7) for real-time application. The obtained results can be considered as promising and can be extended to other case studies with more defects and larger datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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The Design of Portrait Photography Guiding System Using VGG16 Model 14th

The Design of Portrait Photography Guiding System Using VGG1...

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14th International Conference on Frontier Computing, FC 2024

作者： Chiu, Chuan-Feng Huang, Chun-Hong Tseng, Hsiang-Chieh Wu, Pei-Jung Lin, Wei-Sheng Peng, Hsiang-Min Department of Information Management Minghsin University of Science and Technology Hsinchu Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan City Taiwan

ISBN: (纸本)9789819623945

Because of the population of photography camera, human being could have photography equipment to take a picture became an easy task. However, to have a good photography is not an easy task. The basic of a good photo is the composition of the photography. A better photography composition could lead better visual effect. So, in this paper we intent to design a photography guiding system framework for users to have good photos especially for portrait photography. In our work, we design a system framework using VGG16 model based on Rule of Thirds and luminance distribution to guide user to select better photography composition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photographic equipment

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8141-8157页

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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Baby Suffocation Prediction System

Baby Suffocation Prediction System

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Rasha Jamal Atwah Mashael Alharthi Lamees Alharbi Jana Bashnini Nujoud Al-Sharif Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The number of suffocation cases among babies during their sleeping is increased due to the presence of blankets and sheets. Therefore, it is crucial to have a reliable system that can monitor babies during bedtime. In order to ensure the safety of children, we have developed an AI-based comprehensive smart system that provides real-time monitoring to predict suffocation incidents as soon as possible. The proposed system offers babies protection by promoting suffocation prediction within an appropriate time frame. The system is built based on the BlazeFace detection model to monitor the baby and send notifications and sound alarms if the baby's face is covered or missing for a certain amount of time. The notification is sent through the mobile application to alert the baby's parents or caregivers if there is a suffocation case that may occur. Additionally, an audible alarm is activated to grab the attention of the child's parents or caregivers and ensure quick intervention. We conducted an experiment to assess the system's effectiveness, and the prototype shows adequate accuracy and quick response. There is still space for further development, which we suggest as future work.

关键词： Pediatrics Technological innovation Accuracy Smart systems Real-time systems Sensor systems Internet of Things Biomedical monitoring Monitoring Intelligent sensors

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Smart Visual Pollution Detection System

Smart Visual Pollution Detection System

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Rothana Alsolami Rasha Atwah Ranad Alajlani Sara Alhajaili Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

As Saudi Arabia moves forward with its Vision 2030 initiative to improve the quality of life and services for its people, visual pollution continues to have a negative impact on the environment. However, assessing and addressing visual pollution in cities remains a challenging task due to its subjective nature and complexity. One of the main challenges is the lack of powerful and reliable intelligent systems for detecting visual pollution. To tackle this issue, we have invested our efforts in developing a cost-effective intelligent system using advanced technologies like the Internet of Things (IoT) and computer vision. This system will help detect and monitor visual pollution, including items such as water cans, paper, and plastic cups. Our smart system uses sensors like cameras to collect data and then analyzes it to display the information on a user-friendly control panel for administrators to monitor the area under study. The system has been successfully tested under various scenarios and has shown promising accuracy rates, reaching up to 87% in detecting recyclable materials. We believe that there are numerous opportunities to further enhance the performance of future designs.

关键词： Visualization Pollution Accuracy Smart cities Cameras Real-time systems Water pollution Internet of Things Intelligent systems Monitoring

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Multitask-Oriented Efficient Computational Offloading Orchestrator for IoT Applications in Mobile-Edge Computing

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IEEE Internet of Things Journal 2025年第10期12卷 14096-14108页

作者： Wang, Leilei Deng, Xiaoheng Zhang, Honggang Wan, Shaohua Min, Geyong Xi’an University of Technology School of Computer Science and Engineering Shanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China Central South University School of Computer Science and Engineering Changsha410075 China Central South University Shenzhen Research Institute Shenzhen518057 China University of Massachusetts at Boston Engineering Department BostonMA02125 United States University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile-edge computing (MEC) can accelerate computation-intensive applications and emerge as a promising technology for enabling Internet of Things (IoT). MEC improves the processing performance of tasks by assigning them to the edge nodes. However, with massive terminals contending for computation and communication resources simultaneously, how to develop a flexible computational offloading mechanism becomes the fundamental issue of MEC-enabled IoT systems. This article aims to develop an effective computational offloading decision scheme by jointly considering the computational resource and diverse user demands with two goals, i.e., minimizing both the latency and the energy consumption. Specifically, we develop a two-stage computational offloading mechanism, where the computational resources and offloading decisions can be allocated and coordinated with the variation of computation requirements. To achieve the two goals, this work introduces an edge node recommendation model within the cloud-edge–end architecture to reduce the offloading optimization search space. Furthermore, we propose a new computational offloading (CROCA) algorithm based on chemical reaction optimization (CRO) for optimizing offloading utility, which thoroughly considers the competition between mobile device requests and computational resources. Extensive evaluation results demonstrate that the proposed CROCA scheme can effectively improve the computational offloading performance. © 2014 IEEE.

关键词： Computation offloading

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network intrusion detection based on feature fusion of attack dimension

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Journal of Supercomputing 2025年第6期81卷 1-33页

作者： Sun, Xiaolong Gu, Zhengyao Zhang, Hao Gu, Jason Liu, Yanhua Dong, Chen Ye, Junwei College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou350116 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 1Z1 Canada

network traffic anomaly detection involves the rapid identification of intrusions within a network through the detection, analysis, and classification of network traffic data. The variety of cyberattacks encompasses diverse attack principles. Employing an indiscriminate feature selection strategy may lead to the neglect of key features highly correlated with specific attack types. This oversight could diminish the recognition rate for that category, thereby impacting the overall performance of the detection model. To address this issue, this paper proposes a network traffic anomaly detection model based on the fusion of attack-dimensional features. Firstly, construct binary classification datasets independently for each attack class and perform individual feature selection to extract positively correlated features for each class. The features are then fused by employing a combination methods. Subsequently, based on the fused sub-datasets, base classifiers are trained. Finally, an ensemble learning approach is introduced to integrate the predictions of individual classifiers, enhancing the robustness of the model. The proposed approach, validated on NSL-KDD and UNSW-NB15 benchmark datasets, outperforms the latest methods in the field by achieving a 2% and 7% increase in precision on weighted averages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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