Design a Robust DDoS Attack Detection and Mitigation Scheme in SDN-Edge-IoT by Leveraging Machine Learning by Habtamu Molla Belachew, Mulatu Yirga Beyene, Abinet Bizuayehu Desta, Behaylu Tadele Alemu, Salahadin Seid Musa, Alemu Jorgi Muhammed

“…However, the complexity of IoT networks, with their resource-constrained devices, presents substantial security challenges, particularly Distributed Denial of Service (DDoS) attacks. …”
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Key Considerations for Real-Time Object Recognition on Edge Computing Devices by Nico Surantha, Nana Sutisna

“…It covers the key considerations of employing deep learning on edge computing devices, such as selecting edge devices, deep learning frameworks, lightweight deep learning models, hardware optimization, and performance metrics. …”
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Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing by Xiting Peng, Yandi Zhang, Xiaoyu Zhang, Chaofeng Zhang, Wei Yang

“…This paper proposes a task-offloading scheme based on deep reinforcement learning to optimize the strategy between vehicles and edge computing resources. …”
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Cloudlet Federation Based Context-Aware Federated Learning Approach by Sana Latif, Muhammad Ziad Nayyer, Imran Raza, Syed Asad Hussain, M. Hasan Jamal, Soojung Hur, Imran Ashraf

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A Hierarchical Dispatcher for Scheduling Multiple Deep Neural Networks (DNNs) on Edge Devices by Hyung Kook Jun, Taeho Kim, Sang Cheol Kim, Young Ik Eom

“…This paper presents a hierarchical dispatcher architecture designed to efficiently schedule the execution of multiple deep neural networks (DNNs) on edge devices with heterogeneous processing units (PUs). …”
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Efficient Edge AI for Next Generation Smart Mirror Applications by Nils Kucza, Florian Porrmann, Christian Stollenwerk, Jens Hagemeyer

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The Cutting Edge in Print by Miente Pietersma

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Digital Presentation and Interactive Learning for Intangible Cultural Heritage Preservation Using Artificial Intelligence by Liuxun Zhang, Zhouluo Wang, Rulan Yang, Qiang Yi

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On the Deployment of Edge AI Models for Surface Electromyography-Based Hand Gesture Recognition by Andres Gomez-Bautista, Diego Mendez, Catalina Alvarado-Rojas, Ivan F. Mondragon, Julian D. Colorado

“…Methods: The present study details the implementation of four cutting-edge feature engineering techniques (random forest, minimum redundancy maximum relevance (MRMR), Davies–Bouldin index, and <i>t</i>-tests) in the context of machine learning algorithms (neuronal networks and bagged forests) deployed within a resource-constrained autonomous embedded system. …”
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Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems by Fabio Liberti, Davide Berardi, Barbara Martini

“…Federated Learning (FL) represents a promising distributed learning methodology particularly suitable for dynamic and heterogeneous environments characterized by the presence of Internet of Things (IoT) devices and Edge Computing infrastructures. …”
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Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling by V. Vinoth Kumar, K. M. Karthick Raghunath, Iyappan Perumal, K. Manikandan

“…To solve these issues, we present Enhanced Smart Data-Driven Modeling (ESDDM), which combines Smart Data-Driven Modeling (SDDM) with modern Deep Learning (DL). …”
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EDGE DETECTION TECHNIQUE BASED ON BILATERAL FILTERING AND ITERATIVE THRESHOLD SELECTION ALGORITHM AND TRANSFER LEARNING FOR TRAFFIC SIGN RECOGNITION by Milind PARSE, Dhanya PRAMOD

“…The proposed edge detection algorithm and transfer learning is used to train the Convolutional Neural Network (CNN) models and recognize the traffic signs. …”
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Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum by Mohammed Maray

“…When integrated with the Internet of Things (IoT), this system enables individuals to live independently while ensuring their well-being. The IoT-edge-cloud framework enhances this by processing data as close to the source as possible—either on edge devices or directly on the IoT devices themselves. …”
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Protocol-Agnostic and Packet-Based Intrusion Detection Using a Multi-Layer Deep-Learning Architecture at the Network Edge by Rodolphe Picot, Felipe Gohring de Magalhaes, Ahmad Shahnejat Bushehri, Maroua Ben Atti, Gabriela Nicolescu, Alejandro Quintero

“…This paper presents a novel approach to ID in network traffic within edge computing environments using a Neural Network (NN) model. …”
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Survey on Replay-Based Continual Learning and Empirical Validation on Feasibility in Diverse Edge Devices Using a Representative Method by Heon-Sung Park, Hyeon-Chang Chu, Min-Kyung Sung, Chaewoon Kim, Jeongwon Lee, Dae-Won Kim, Jaesung Lee

“…Consequently, designing strategies that balance the preservation of past knowledge with rapid and cost-effective updates of model parameters has become a critical consideration in on-device continual learning. This paper presents an empirical survey of replay-based continual learning studies, considering the nearest class mean classifier with replay-based sparse weight updates as a representative method for validating the feasibility of diverse edge devices. …”
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Hand Gesture Recognition on Edge Devices: Sensor Technologies, Algorithms, and Processing Hardware by Elfi Fertl, Encarnación Castillo, Georg Stettinger, Manuel P. Cuéllar, Diego P. Morales

“…This encompasses determining whether the application presented is suitable for edge integration, their real-time capability, whether continuous learning is implemented, which robustness was achieved, whether ML is applied, and the accuracy level. …”
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AI and IoT-powered edge device optimized for crop pest and disease detection by Jean Pierre Nyakuri, Celestin Nkundineza, Omar Gatera, Kizito Nkurikiyeyezu, Gervais Mwitende

“…This study presents the development of a portable smart IoT device that integrates a lightweight convolutional neural network (CNN), called Tiny-LiteNet, optimized for edge applications with built-in support of model explainability. …”
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A unified learning framework for by K. Seetharaman, S. Sathiamoorthy

“…This paper presents a unified learning framework for heterogeneous medical image retrieval based on a Full Range Autoregressive Model (FRAR) with the Bayesian approach (BA). …”
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MAARS: Multiagent Actor–Critic Approach for Resource Allocation and Network Slicing in Multiaccess Edge Computing by Ducsun Lim, Inwhee Joe

“…This paper presents a novel algorithm to address resource allocation and network-slicing challenges in multiaccess edge computing (MEC) networks. …”
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Prioritizing Research for Enhancing the Technology Readiness Level of Wind Turbine Blade Leading-Edge Erosion Solutions by Sara C. Pryor, Rebecca J. Barthelmie, Jacob J. Coburn, Xin Zhou, Marianne Rodgers, Heather Norton, M. Sergio Campobasso, Beatriz Méndez López, Charlotte Bay Hasager, Leon Mishnaevsky

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