Lightweight terminal cross-domain authentication protocol in edge computing environment by Hongying ZHU, Xinyou ZHANG, Huanlai XING, Li FENG

“…Edge computing has gained widespread usage in intelligent applications due to its benefits, including low latency, high bandwidth, and cost-effectiveness.However, it also faces many security challenges due to its distributed, real-time, multi-source and heterogeneous data characteristics.Identity authentication serves as the initial step for terminal to access to the network and acts as the first line of defense for edge computing.To address the security issues in the edge computing environment, a terminal cross-domain authentication protocol suitable for the edge computing environment was proposed based on the ＆quot;cloud-edge-end＆quot; three-level network authentication architecture.Access authentication was implemented between terminals and local edge nodes based on the SM9 algorithm, and session keys were negotiated.The secret key was combined with symmetric encryption technology and hash function to achieve cross-domain authentication for the terminal.The pseudonym mechanism was used in the authentication process to protect the privacy of end users.The terminal only needs to register once, and it can roam randomly between different security domains.BAN logic was used to prove the correctness of the protocol and analyze its security.The results show that this protocol is capable of resisting common attacks in IoT scenarios, and it features characteristics such as single sign-on and user anonymity.The performance of the cross-domain authentication protocol was evaluated based on computational and communication costs, and compared with existing schemes.The experimental results show that this protocol outperforms other schemes in terms of computational and communication costs, making it suitable for resource-constrained terminal devices.Overall, the proposed protocol offers lightweight and secure identity authentication within edge computing environments.…”
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Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning by Alfredo Petrella, Marco Miozzo, Paolo Dini

“…To do so, we propose a novel prediction framework based on edge computing and Deep Transfer Learning (DTL) techniques, using datasets obtained at the edge through a large measurement campaign. …”
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EDGE STATE METHOD IN MECHANICS PROBLEMS CONCERNING ANISOTROPIC THIN PLATES by D. A. Ivanychev

“…The isomorphism of spaces allows the process of finding the internal state to be reduced to the study of the edge state isomorphic to it. …”
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Investigation of Hf/Ti bilayers for the development of transition-edge sensor microcalorimeters by Victoria Y. Safonova, Anna V. Gordeeva, Anton V. Blagodatkin, Dmitry A. Pimanov, Anton A. Yablokov, Andrey L. Pankratov

“…The bridges were formed by a photolithographic lift-off process and are intended to be used as the main sensing element of a microcalorimeter based on a transition-edge sensor (TES) in experiments to determine the magnetic moment of neutrinos. …”
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Mobile detection of cataracts with an optimised lightweight deep Edge Intelligent technique by Dipta Neogi, Mahirul Alam Chowdhury, Mst. Moriom Akter, Md. Ishan Arefin Hossain

“…Its quick and straightforward testing process has become an essential component in our efforts to prevent blindness. …”
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Edge-Driven Multiple Trajectory Attention Model for Vehicle Routing Problems by Dapeng Yan, Bei Ou, Qingshu Guan, Zheng Zhu, Hui Cao

“…Our model is built upon the encoder–decoder architecture, incorporating an edge-driven multi-head attention (EDMHA) block within the encoder to better utilize edge information. …”
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Enhancing Incentive Schemes in Edge Computing through Hierarchical Reinforcement Learning by Gowtham R, Vatsala Anand, Yadati Vijaya Suresh, Kasetty Lakshmi Narasimha, R. Anil Kumar, V. Saraswathi

“… Edge learning is a distributed approach for training machine learning models using data from edge devices. …”
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Hyperledger Fabric Blockchain for Securing the Edge Internet of Things: A Review by Muhammad Haziq Zulhazmi Hairul Nizam, Muhammad Afiq Ahmad Nizam, Muhammad Hadi Husaini Jummadi, Nik Nor Muhammad Saifudin Nik Mohd Kamal, Ahmad Anwar Zainuddin

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Overview of Modern Technologies for Acquiring and Analysing Acoustic Information Based on AI and IoT by Sabina Szymoniak, Łukasz Kuczyński

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Comparing Geodesic Filtering to State-of-the-Art Algorithms: A Comprehensive Study and CUDA Implementation by Pierre Boulanger, Sadid Bin Hasan

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Bridge Displacements Monitoring Method Based on Pixel Sequence by Zimeng Shen, Weizhu Zhu, Tong Wu, Xianghao Luo, Zhixiang Zhou

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A near-threshold memristive computing-in-memory engine for edge intelligence by Linfang Wang, Weizeng Li, Zhidao Zhou, Junjie An, Wang Ye, Zhi Li, Hanghang Gao, Hongyang Hu, Jing Liu, Xiaoming Chen, Ling Li, Qi Liu, Mingoo Seok, Chunmeng Dou, Ming Liu

“…Abstract Memristive computing-in-memory and near-threshold computing are two unconventional computing paradigms that can potentially enhance the energy efficiency and real-time performance of edge devices. However, their scalability faces challenges, primarily due to process variation. …”
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Efficient Anomaly Detection for Edge Clouds: Mitigating Data and Resource Constraints by Javad Forough, Hamed Haddadi, Monowar Bhuyan, Erik Elmroth

“…Anomaly detection plays a vital role in ensuring the security and reliability of edge clouds, which are decentralized computing environments with limited resources. …”
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Experimental identification of I-mode characteristics at the edge of FIRE mode in KSTAR by Chweeho Heo, SangJin Park, GyungJin Choi, Jaewook Kim, Eun-jin Kim, YoungMu Jeon, Minjun J. Choi, Hyunsun Han, Choongki Sung, TaikSoo Hahm, Yong-Su Na

“…This coupling typically occurs as the H-mode transition approaches, and its potential link to a regulatory process is discussed. When the coupling manifests, intermittent bursts are observed concurrently, not just at the radial location where the WCM amplitude peaks, but throughout the edge region. …”
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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

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Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing by Akinbode A. Olawole, Fambirai Takawira, Chabalala S. Chabalala

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Edge Computing for Real-Time Climate Data Analysis in Smart Farming by Shnain Ammar Hameed, Abed Z., Annapoorna Errabelli

“…By doing data processing at the edge, latency is reduced and the accuracy of the predictions is improved at the same time, so the farmers are able to take data driven decision when it matters most. …”
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Federated learning based intelligent edge computing technique for video surveillance by Yu ZHAO, Jie YANG, Miao LIU, Jinlong SUN, Guan GUI

“…With the explosion of global data,centralized cloud computing cannot provide low-latency,high-efficiency video surveillance services.A distributed edge computing model was proposed,which directly processed video data at the edge node to reduce the transmission pressure of the network,eased the computational burden of the central cloud server,and reduced the processing delay of the video surveillance system.Combined with the federated learning algorithm,a lightweight neural network was used,which trained in different scenarios and deployed on edge devices with limited computing power.Experimental results show that,compared with the general neural network model,the detection accuracy of the proposed method is improved by 18%,and the model training time is reduced.…”
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Cutting-edge AI tools revolutionizing scientific research in life sciences by Katarzyna Lorenc-Kukula

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GreedySlide: An Efficient Sliding Window for Improving Edge-Object Detectors by To Hai Thien, Tung-Lam Duong, Chi-Luan Le

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