Energy Management and Edge-Driven Trading in Fractal-Structured Microgrids: A Machine Learning Approach by Mostafa Pasandideh, Jason Kurz, Mark Apperley

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A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions by Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi, Mohammad N. Alanazi

“…However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. …”
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Federated Reinforcement Learning-Based Dynamic Resource Allocation and Task Scheduling in Edge for IoT Applications by Saroj Mali, Feng Zeng, Deepak Adhikari, Inam Ullah, Mahmoud Ahmad Al-Khasawneh, Osama Alfarraj, Fahad Alblehai

“…Similarly, on the IID EMNIST dataset, the proposed D4PG model attains an F1-score of 0.82 and an accuracy of 82%, surpassing other Reinforcement Learning-based approaches. Additionally, for edge server power consumption, the hybrid offloading algorithm reduces fluctuations compared to existing methods, ensuring more stable energy usage across edge nodes. …”
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Research on power efficient autonomous UAV navigation algorithm: an edge intelligence driven approach by Chunmin LIN, Liekang ZENG, Xu CHEN

“…Autonomous drone navigation has received growing attention in the recent community.Compared with traditional navigation approaches which rely on location-based services highly, deep learning based visual methods have showed superior performance in self-adaption and generalization, which are a promising solution for autonomous navigation.Running the resource-hungry deep learning execution in the resource-constrained unmanned aerial vehicle (UAV), however, significant challenges were presented in power efficiency.To tackle this challenge, following the idea of edge intelligence, a deep reinforcement learning approach was introduced to dynamically configure the computational scale of the deep learning model on UAV and hence realize the autonomous navigation with low latency and high energy efficiency.Evaluations based on both simulation and real prototype experiments show that the proposed approach has the less energy consumption, longer navigation trail and higher obstacle avoidance rate.…”
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Long-Range Wide Area Network Intrusion Detection at the Edge by Gonçalo Esteves, Filipe Fidalgo, Nuno Cruz, José Simão

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Engineering a multi model fallback system for edge devices by Gaurav Kadve, Abishi Chowdhury, Vishal Krishna Singh, Amrit Pal

“…The growing deployment of machine learning applications in edge environments, such as IoT devices and embedded systems, has highlighted the need for efficient, resource-aware systems for edge environments. …”
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The Double-Edged Sword: Benefits and Challenges that Artificial Intelligence Tools can Bring to EFL Teaching and Learning by Diana Alexandra Campoverde-Quezada, Esteban Arnoldo Valdiviezo-Ramírez

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Machine learning for handover decision with mobile edge computing in 6G mobile network: a survey by Soule Issa Loutfi, Ibraheem Shayea, Ufuk Tureli, Ayman A. El-Saleh, Waheeb Tashan, Ramazan Caglar

“…Moreover, machine learning (ML) and deep learning (DL) technologies are the key promising solutions for intelligent HOD-making in 6G networks with MEC. …”
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Enhancing DDoS Attacks Mitigation Using Machine Learning and Blockchain-Based Mobile Edge Computing in IoT by Mahmoud Chaira, Abdelkader Belhenniche, Roman Chertovskih

“…The widespread adoption of Internet of Things (IoT) devices has been accompanied by a remarkable rise in both the frequency and intensity of Distributed Denial of Service (DDoS) attacks, which aim to overwhelm and disrupt the availability of networked systems and connected infrastructures. In this paper, we present a novel approach to DDoS attack detection and mitigation that integrates state-of-the-art machine learning techniques with Blockchain-based Mobile Edge Computing (MEC) in IoT environments. …”
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EADRL: Efficiency-aware adaptive deep reinforcement learning for dynamic task scheduling in edge-cloud environments by J. Anand, B. Karthikeyan

“…This paper presents Efficiency-Aware Adaptive Deep Reinforcement Learning (EADRL), a framework that introduces two key mechanisms: an adaptive learning rate and a dynamic confidence-aware reward adjustment to enable intelligent context-aware scheduling decisions. …”
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Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms by Nian-Ze Hu, Bo-An Lin, Yen-Yu Wu, Hao-Lun Huang, You-Xin Lin, Chih-Chen Lin, Po-Han Lu

“…By applying tiny machine learning (TinyML) and machine vision algorithms, we integrated edge computing devices to control the robotic arms and identified objects precisely on the production line, with ultra-low energy consumption. …”
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Review of communication optimization methods in federated learning by YANG Zhikai, LIU Yaping, ZHANG Shuo, SUN Zhe, YAN Dingyu

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Towards a Decentralized Collaborative Framework for Scalable Edge AI by Ahmed M. Abdelmoniem , Mona Jaber , Ali Anwar , Yuchao Zhang , Mingliang Gao 

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EDRNet: Edge-Enhanced Dynamic Routing Adaptive for Depth Completion by Fuyun Sun, Baoquan Li, Qiaomei Zhang

“…The experimental results demonstrate that the method presented in this paper achieves significant performance improvements on the public datasets KITTI DC and NYU Depth v2, especially in the edge region’s depth prediction accuracy and computational efficiency.…”
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Integration of deep learning with edge computing on progression of societal innovation in smart city infrastructure: A sustainability perspective by Yasir Afaq, Shaik Vaseem Akram

“…This study highlight the importance of sustainability, with specific focus on SDG goals 3.8, 3.9, 9.1, 9.4, 11.3, and 11.6, providing a technical comparative analysis of the integration of deep learning and edge computing technologies in healthcare, unmanned aerial vehicles (UAVs,) and other smart city applications. …”
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HOODIE: Hybrid Computation Offloading via Distributed Deep Reinforcement Learning in Delay-Aware Cloud-Edge Continuum by Anastasios E. Giannopoulos, Ilias Paralikas, Sotirios T. Spantideas, Panagiotis Trakadas

“…HOODIE employs a model-free deep reinforcement learning (DRL) framework, where distributed DRL agents at each edge server autonomously determine offloading decisions without global task distribution awareness. …”
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EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks by Sakshi Patni, Joohyung Lee

“…The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. In this study, we present EdgeGuard, a novel decentralized architecture that combines blockchain technology, federated learning, and edge computing to address those challenges and coordinate medical resources across IoMT networks. …”
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Robust Semantic Segmentation of Wafer Transmission Electron Microscopy Image Using Multi-Task Learning With Edge Detection by Yongwon Jo, Jinsoo Bae, Hansam Cho, Sungsu Kim, Heejoong Roh, Kyunghye Kim, Munki Jo, Munuk Kim, Jaeung Tae, Seoung Bum Kim

“…Our approach includes three primary components: a pre-training phase using self-supervised representation learning to extract meaningful representations from unlabeled wafer TEM images, a multi-task learning-based fine-tuning phase incorporating both semantic segmentation and edge detection tasks, and a boundary-aware loss function to enhance boundary recognition accuracy. …”
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In-depth exploration of software defects and self-admitted technical debt through cutting-edge deep learning techniques. by Sajid Ullah, M Irfan Uddin, Muhammad Adnan, Ala Abdulsalam Alarood, Abdulkream Alsulami, Safa Habibullah

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A Task Offloading and Resource Allocation Strategy Based on Multi-Agent Reinforcement Learning in Mobile Edge Computing by Guiwen Jiang, Rongxi Huang, Zhiming Bao, Gaocai Wang

“…Then, in order to solve the joint optimization problem, this paper designs a decentralized offloading and scheduling scheme based on “task-oriented” multi-agent reinforcement learning. In this scheme, we present information synchronization protocols and offloading scheduling rules and use edge servers as agents to construct a multi-agent system based on the Actor–Critic framework. …”
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