A Privacy-Preserving and Edge-Collaborating Architecture for Personalized Mobility by Weitao Jian, Junshu He, Jiatao Chen, Ming Cai

“…Since current PMS widely employs centralized approaches (CPMS) to process massive sensitive data from individuals and support diverse edge devices, resulting in high pressure in privacy protection and performance balancing, this paper presents a federated PMS (FPMS) and its design architecture in logical and physical views by adopting federated learning to provide multimodal, dynamic, and personalized travel options with system-saving safety and efficiency guaranteed. …”
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Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite by Fabrizio Maria Aymone, Danilo Pietro Pau

“…This benchmark aims to guide the development of efficient AI solutions for In-Sensor Machine Learning Computing, fostering innovation in the field of Edge AI benchmarking, such as the one conducted by the MLCommons-Tiny working group.…”
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Evaluation and optimization of carbon emission for federal edge intelligence network by Peng ZHANG, Yong XIAO, Jiwei HU, Liang LIAO, Jianxin LYU, Zegang BAI

“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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Real-Time Series Arc Fault Detection and Appliances Classification in AC Networks Based on Competing Convolutional Kernels by Francesco Ferracuti, Riccardo Felicetti, Luca Cavanini, Patrick Schweitzer, Andrea Monteriu

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A Scalable Hybrid Autoencoder–Extreme Learning Machine Framework for Adaptive Intrusion Detection in High-Dimensional Networks by Anubhav Kumar, Rajamani Radhakrishnan, Mani Sumithra, Prabu Kaliyaperumal, Balamurugan Balusamy, Francesco Benedetto

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A Matching Game for LLM Layer Deployment in Heterogeneous Edge Networks by Benedetta Picano, Dinh Thai Hoang, Diep N. Nguyen

“…With the growing demand for computational and storage capabilities of modern learning models, performing their computation exclusively in a centralized manner has become increasingly impractical. …”
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Privacy-Preserving Live Video Analytics for Drones via Edge Computing by Piyush Nagasubramaniam, Chen Wu, Yuanyi Sun, Neeraj Karamchandani, Sencun Zhu, Yongzhong He

“…By integrating a novel split-model architecture tailored for distributed deep learning through edge computing, our approach strikes a balance between operational efficiency and privacy. …”
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Innovating Automatic Grow Pots with Cutting-Edge Smart Hydroponic System by Mohamed Aezeden

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Provisioning of Live Container Migration in Edge/Cloud Environments: Techniques and Challenges by Radhwan Al-Bayram, Rawaa Qasha

“…Until today, there has been a lack of comprehensive research discussing live container migration in the IoT domain and investigating the challenges of representing them in the edge/cloud environment. This survey presents cutting-edge articles that involve a live container migration approach. …”
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Detecting infrared UAVs on edge devices through lightweight instance segmentation. by YuZhi Chen, HaoYue Sun, Liang Tian, Ye Yang, ShenYang Wang, TianYou Wang

“…<h4>Motivation</h4>Infrared unmanned aerial vehicle (UAV) detection for surveillance applications faces three conflicting requirements: accurate detection of pixel-level thermal signatures, real-time processing capabilities, and deployment feasibility on resource-constrained edge devices. Current deep learning approaches typically optimize for one or two of these objectives while compromising the third.…”
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Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data Augmentation by Tayyaba Riaz, Adeel Anjum, Madiha Haider Syed, Semeen Rehman

“…In the evolving landscape of biometric authentication, the integrity of face recognition systems against sophisticated presentation attacks (PAD) is paramount. This study set out to elevate the detection capabilities of PAD systems by ingeniously integrating a teacher–student learning framework with cutting-edge PAD methodologies. …”
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Hardware/Software Co-Design Optimization for Training Recurrent Neural Networks at the Edge by Yicheng Zhang, Bojian Yin, Manil Dev Gomony, Henk Corporaal, Carsten Trinitis, Federico Corradi

“…However, training RNNs on edge devices presents major challenges due to limited memory and computing resources. …”
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Tensor RT optimized driver drowsiness detection system using edge device by Chandramohan Dhasarathan, Sambasivam Gnanasekaran, Arnab Pattanayak, Gourav Kumar, Kartik Vig, Vaibhav Narain, K.M. Deva Narayan, Sunidhi Garg

“…The system uses transfer learning techniques for implementing CNN model algorithms to analyze live video from the camera module, allowing for real-time detection of driver behavior such as fatigue or distraction. …”
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A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges by Thi-Thu-Trang Do, Quyet-Thang Huynh, Kyungbaek Kim, Van-Quyet Nguyen

“…Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. …”
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Research on application of artificial intelligence safety production management and control platform in Shendong mining area by Yazhong CUI, Jianrong HE, Yanyan REN

“…The initial formation includes infrastructure, AI development framework, dataset, AI training, AI deployment The AI service capability and business application of the Shendong mining area's artificial intelligence platform architecture are bottom-up. Supervised learning, semi supervised learning, transfer learning and other technologies are applied to improve the efficiency and quality of model training. …”
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MEMS and IoT in HAR: Effective Monitoring for the Health of Older People by Luigi Bibbò, Giovanni Angiulli, Filippo Laganà, Danilo Pratticò, Francesco Cotroneo, Fabio La Foresta, Mario Versaci

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A weak edge estimation based multi-task neural network for OCT segmentation. by Fan Yang, Pu Chen, Shiqi Lin, Tianming Zhan, Xunning Hong, Yunjie Chen

“…Yet, these methods still encounter two primary challenges. Firstly, deep learning methods are sensitive to weak edges. Secondly, the high cost of annotating medical image data results in a lack of labeled data, leading to overfitting during model training. …”
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State of Charge Estimation for Li-Ion Batteries: An Edge-Based Data-Driven Approach by Sesidhar Dvsr, Chandrashekhar Badachi, Chandrashekar Nagawaram, Panduranga Chary Kondoju, C. Dhanamjayulu, Innocent Kamwa

“…Recently, the confluence of Edge computing with IoT has enabled resource constrained embedded devices to implement machine learning algorithms. …”
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Laplacian Pyramid Network With Hybrid Encoder and Edge Guidance for Remote Sensing Change Detection by Wenkai Yan, Yikun Liu, Mingsong Li, Ruifan Zhang, Gongping Yang

“…Many CD methods based on deep learning demonstrate strong performance, but their effectiveness is influenced by the choice of encoder and the challenge of accurately delineating the edges of change regions. …”
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Past, Present and Future Perspectives of Forensic Genetics by Itzae Adonai Gutiérrez-Hurtado, Mayra Elizabeth García-Acéves, Yolanda Puga-Carrillo, Mariano Guardado-Estrada, Denisse Stephania Becerra-Loaiza, Víctor Daniel Carrillo-Rodríguez, Reynaldo Plazola-Zamora, Juliana Marisol Godínez-Rubí, Héctor Rangel-Villalobos, José Alonso Aguilar-Velázquez

“…Emerging trends point to a future shaped by the integration of cutting-edge technologies, including CRISPR-Cas systems, artificial intelligence, and machine learning, which promise to further revolutionize the field. …”
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