Relevant bibliographies by topics / Intelligent Edge Networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intelligent Edge Networks'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Intelligent Edge Networks"

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Li, Qian, Heng Liu, and Xiaoming Zhao. "IoT Networks-Aided Perception Vocal Music Singing Learning System and Piano Teaching with Edge Computing." Mobile Information Systems 2023 (April 28, 2023): 1–9. http://dx.doi.org/10.1155/2023/2074890.

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The research on Internet of Things (IoT) network and edge computing has been a research hotspot in both industry and academia in recent years, especially for the ambient intelligence and massive communication. As a typical form of IoT network and edge computing, the intelligent perception vocal music singing learning system has attracted the attention of researchers in education and academia. Piano teaching is an important course for music majors in higher education. Strengthening piano teaching can cultivate outstanding piano talents for the country and promote the development of music art. T
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Zhang, Jiaxin, Xing Zhang, Peng Wang, Liangjingrong Liu, and Yuanjun Wang. "Double-edge intelligent integrated satellite terrestrial networks." China Communications 17, no. 9 (2020): 128–46. http://dx.doi.org/10.23919/jcc.2020.09.011.

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Zeydan, Engin, Josep Mangues-Bafalluy, and Yekta Turk. "Intelligent Service Orchestration in Edge Cloud Networks." IEEE Network 35, no. 6 (2021): 126–32. http://dx.doi.org/10.1109/mnet.101.2100214.

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Engin, Zeydan, Mangues Josep, and Turk Yekta. "Intelligent Service Orchestration in Edge Cloud Networks." IEEE Netw. 35, no. 6 (2021): 126–32. https://doi.org/10.1109/MNET.101.2100214.

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The surge in data traffic is challenging for network infrastructure owners coping with stringent service requirements (e.g., high bandwidth, ultralow latency) as well as shrinking per-gigabyte revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In parallel, there is an increasing demand for network virtualization and container-based services. In this study, we investigate the management of software defined networking (SDN)-based transport network and edge cloud service orchestration. To this end, we use a machine learning (ML)-based design to m
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Musa, Salahadin Seid, Marco Zennaro, Mulugeta Libsie, and Ermanno Pietrosemoli. "Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions." Future Internet 14, no. 7 (2022): 192. http://dx.doi.org/10.3390/fi14070192.

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Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN)
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Mani Sai Kamal Darla. "Edge AI: Revolutionizing IoT Data Processing." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 258–64. https://doi.org/10.32996/jcsts.2025.7.7.26.

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Edge AI represents a transformative computational paradigm that embeds artificial intelligence algorithms directly within Internet of effects bias, barring reliance on remote pall structure for real- time decision- making processes. The distributed intelligence frame unnaturally alters traditional IoT infrastructures by incorporating cognitive capabilities at network boundaries, enabling independent data processing and immediate response generation at the point of data origin. Edge AI infrastructures address critical challenges in wireless hindrance networks through deep learning optimization
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Rahmadika, Sandi, Muhammad Firdaus, Seolah Jang, and Kyung-Hyune Rhee. "Blockchain-Enabled 5G Edge Networks and Beyond: An Intelligent Cross-Silo Federated Learning Approach." Security and Communication Networks 2021 (March 27, 2021): 1–14. http://dx.doi.org/10.1155/2021/5550153.

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Edge networks (ENs) in 5G have the capability to protect traffic between edge entry points (edge-to-edge), enabling the design of various flexible and customizable applications. The advantage of edge networks is their pioneering integration of other prominent technologies such as blockchain and federated learning (FL) to produce better services on wireless networks. In this paper, we propose an intelligent system integrating blockchain technologies, 5G ENs, and FL to create an efficient and secure framework for transactions. FL enables user equipment (UE) to train the artificial intelligence m
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Guo, Hongzhi, Jiajia Liu, Ju Ren, and Yanning Zhang. "Intelligent Task Offloading in Vehicular Edge Computing Networks." IEEE Wireless Communications 27, no. 4 (2020): 126–32. http://dx.doi.org/10.1109/mwc.001.1900489.

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Yang, Yang, Rui Lyu, Zhipeng Gao, Lanlan Rui, and Yu Yan. "Semisupervised Graph Neural Networks for Traffic Classification in Edge Networks." Discrete Dynamics in Nature and Society 2023 (July 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/2879563.

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Edge networking brings computation and data storage as close to the point of request as possible. Various intelligent devices are connected to the edge nodes where traffic packets flow. Traffic classification tasks are thought to be a keystone for network management; researchers can analyze packets captured to understand the traffic as it hits their network. However, the existing traffic classification framework needs to conduct a unified analysis, which leads to the huge bandwidth resources required in the process of transferring all captured packet files to train a global classifier. In this
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Alam, Tanweer, Baha Rababah, Arshad Ali, and Shamimul Qamar. "Distributed Intelligence at the Edge on IoT Networks." Annals of Emerging Technologies in Computing 4, no. 5 (2020): 1–18. http://dx.doi.org/10.33166/aetic.2020.05.001.

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The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recentl
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Dissertations / Theses on the topic "Intelligent Edge Networks"

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Mestoukirdi, Mohamad. "Reliable and Communication-Efficient Federated Learning for Future Intelligent Edge Networks." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS432.

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Dans le domaine des futurs réseaux sans fil 6G, l'intégration de la périphérie intelligente grâce à l'avènement de l'IA représente un bond en avant considérable, promettant des avancées révolutionnaires en matière de communication sans fil. Cette intégration favorise une synergie harmonieuse, capitalisant sur le potentiel collectif de ces technologies transformatrices. Au cœur de cette intégration se trouve le rôle de l'apprentissage fédéré, un paradigme d'apprentissage décentralisé qui préserve la confidentialité des données tout en exploitant l'intelligence collective des appareils interconn
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Sigwele, Tshiamo, Yim Fun Hu, M. Ali, Jiachen Hou, M. Susanto, and H. Fitriawan. "An intelligent edge computing based semantic gateway for healthcare systems interoperability and collaboration." IEEE, 2018. http://hdl.handle.net/10454/17552.

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Yes<br>The use of Information and Communications Technology (ICTs) in healthcare has the potential of minimizing medical errors, reducing healthcare cost and improving collaboration between healthcare systems which can dramatically improve the healthcare service quality. However interoperability within different healthcare systems (clinics/hospitals/pharmacies) remains an issue of further research due to a lack of collaboration and exchange of healthcare information. To solve this problem, cross healthcare system collaboration is required. This paper proposes a conceptual semantic based health
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Friji, Hamdi. "Graph neural network-based intrusion detection for secure edge networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS030.

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Face à l'escalade de la complexité et à la fréquence des cyberattaques, cette thèse propose des approches innovantes pour la détection d'intrusion dans les réseaux, en exploitant les capacités avancées des réseaux de neurones en graphe (Graph Neural Networks, GNNs) et de nouvelles représentations sous forme de graphes. Nous commençons par une analyse critique des jeux de données et des représentations de réseaux actuels, en abordant des questions clés sur leur efficacité. Nous introduisons une nouvelle représentation des flux de communication sous forme de graphes, offrant une plus grande robu
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Hasanaj, Enis, Albert Aveler, and William Söder. "Cooperative edge deepfake detection." Thesis, Jönköping University, JTH, Avdelningen för datateknik och informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-53790.

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Deepfakes are an emerging problem in social media and for celebrities and political profiles, it can be devastating to their reputation if the technology ends up in the wrong hands. Creating deepfakes is becoming increasingly easy. Attempts have been made at detecting whether a face in an image is real or not but training these machine learning models can be a very time-consuming process. This research proposes a solution to training deepfake detection models cooperatively on the edge. This is done in order to evaluate if the training process, among other things, can be made more efficient wit
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Kattadige, Chamara Manoj Madarasinghe. "Network and Content Intelligence for 360 Degree Video Streaming Optimization." Thesis, The University of Sydney, 2023. https://hdl.handle.net/2123/29904.

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In recent years, 360° videos, a.k.a. spherical frames, became popular among users creating an immersive streaming experience. Along with the advances in smart- phones and Head Mounted Devices (HMD) technology, many content providers have facilitated to host and stream 360° videos in both on-demand and live stream- ing modes. Therefore, many different applications have already arisen leveraging these immersive videos, especially to give viewers an impression of presence in a digital environment. For example, with 360° videos, now it is possible to connect people in a remote meeting in an
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Abernot, Madeleine. "Digital oscillatory neural network implementation on FPGA for edge artificial intelligence applications and learning." Electronic Thesis or Diss., Université de Montpellier (2022-....), 2023. http://www.theses.fr/2023UMONS074.

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Au cours des dernières décennies, la multiplication des objets embarqués dans de nombreux domaines a considérablement augmenté la quantité de données à traiter et la complexité des tâches à résoudre, motivant l'émergence d'algorithmes probabilistes d'apprentissage tels que l'intelligence artificielle (IA) et les réseaux de neurones artificiels (ANN). Cependant, les systèmes matériels pour le calcul embarqué basés sur l'architecture von Neuman ne sont pas efficace pour traiter cette quantité de données. C'est pourquoi des paradigmes neuromorphiques dotés d'une mémoire distribuée sont étudiés, s
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Ben, ameur Ayoub. "Artificial intelligence for resource allocation in multi-tenant edge computing." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS019.

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Dans cette thèse, nous considérons le Edge Computing (EC) comme un environnement multi-tenant où les Opérateurs Réseau (NOs) possèdent des ressources en périphérie déployées dans les stations de base, les bureaux centraux et/ou les boîtiers intelligents, les virtualisent, et permettent aux Fournisseurs de Services tiers (SPs) - ou tenants - de distribuer une partie de leurs applications en périphérie afin de répondre aux demandes des utilisateurs. Les SPs aux besoins hétérogènes coexistent en périphérie, allant des Communications Ultra-Fiables à Latence Ultra-Basse (URLLC) pour le contrôle des
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Laroui, Mohammed. "Distributed edge computing for enhanced IoT devices and new generation network efficiency." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7078.

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Dans le cloud computing, les services et les ressources sont centralisés dans des centres de données auxquels l’utilisateur peut accéder à partir de ses appareils connectés. L’infrastructure cloud traditionnelle sera confrontée à une série de défis en raison de la centralisation de calcul, du stockage et de la mise en réseau dans un petit nombre de centres de données, et de la longue distance entre les appareils connectés et les centres de données distants. Pour répondre à ce besoin, l’edge computing s’appuie sur un modèle dans lequel les ressources de calcul sont distribuées dans le edge de r
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Minerva, Roberto. "Will the Telco survive to an ever changing world ? Technical considerations leading to disruptive scenarios." Thesis, Evry, Institut national des télécommunications, 2013. http://www.theses.fr/2013TELE0011/document.

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Le secteur des télécommunications passe par une phase délicate en raison de profondes mutations technologiques, principalement motivées par le développement de l'Internet. Elles ont un impact majeur sur l'industrie des télécommunications dans son ensemble et, par conséquent, sur les futurs déploiements des nouveaux réseaux, plateformes et services. L'évolution de l'Internet a un impact particulièrement fort sur les opérateurs des télécommunications (Telcos). En fait, l'industrie des télécommunications est à la veille de changements majeurs en raison de nombreux facteurs, comme par exemple la b
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PELUSO, VALENTINO. "Optimization Tools for ConvNets on the Edge." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2845792.

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Books on the topic "Intelligent Edge Networks"

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Chaudhari, Bharat S., Sheetal N. Ghorpade, and Rytis Paskauskas. Tinyml for Edge Intelligence in IoT and LPWAN Networks. Elsevier Science & Technology, 2024.

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Chaudhari, Bharat S., Sheetal N. Ghorpade, and Rytis Paskauskas. TinyML for Edge Intelligence in IoT and LPWAN Networks. Elsevier Science & Technology Books, 2024.

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Groscurth, Chris R. Future-Ready Leadership. ABC-CLIO, LLC, 2018. http://dx.doi.org/10.5040/9798400655357.

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Provides executive leadership teams with information, tools, and advice they need to lead their organizations into the "future of work," characterized by transformative, smart, and connected technologies already under way, including artificial intelligence, the Internet of things, and automation. The technological and economic forces of the fourth industrial revolution (4IR) are shifting organizations in radical new directions. Automation is taking place not only in factories but in retail environments, and it is not just powerful or precise: it is intelligent, and it learns. Leaders must lear
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Jantsch, Axel, Amir M. Rahmani, Pasi Liljeberg, and Jürgo-Sören Preden. Fog Computing in the Internet of Things: Intelligence at the Edge. Springer, 2018.

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Jantsch, Axel, Amir M. Rahmani, Pasi Liljeberg, and Jürgo-Sören Preden. Fog Computing in the Internet of Things: Intelligence at the Edge. Springer, 2017.

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Stachnio, Konrad. Civilization in Overdrive: Conversations at the Edge of the Human Future. Clarity Press, Inc., 2020.

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Zhang, Liang-Jie, Bedir Tekinerdogan, Shijun Liu, and Mikio Aoyama. Edge Computing – EDGE 2018: Second International Conference, Held as Part of the Services Conference Federation, SCF 2018, Seattle, WA, USA, June ... Springer, 2018.

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Muggleton, Stephen, and Nicholas Chater, eds. Human-Like Machine Intelligence. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862536.001.0001.

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In recent years there has been increasing excitement concerning the potential of Artificial Intelligence to transform human society. This book addresses the leading edge of research in this area. The research described aims to address present incompatibilities of Human and Machine reasoning and learning approaches. According to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars) this new area represents the Third Wave of Artificial Intelligence (3AI, 2020s–2030s), and is being actively investigated in the US, Europe and China. The EPSRC’s UK network on Hu
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Civilization in Overdrive: Conversations at the Edge of the Human Future. Clarity Press, Inc., 2020.

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Taylor, Brian L. Machine Learning: A Quick Guide to Artificial Intelligence, Neural Network and Cutting Edge Deep Learning Techniques for Beginners. Independently Published, 2019.

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Book chapters on the topic "Intelligent Edge Networks"

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Yao, Haipeng, and Mohsen Guizani. "Mobile Edge Computing Enabled Intelligent IoT." In Wireless Networks. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26987-5_6.

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Tao, Xiaoming, Yiping Duan, Zhijin Qin, Danlan Huang, and Liting Wang. "Cloud-Edge-End Intelligent Coordination and Computing." In Wireless Networks. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64155-8_7.

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Thiruvasagam, Prabhu Kaliyammal, and Manikantan Srinivasan. "Intelligent edge computing for B5G networks." In AI in Wireless for Beyond 5G Networks. CRC Press, 2023. http://dx.doi.org/10.1201/9781003303527-7.

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Jin, Wenquan, Minh Quang Hoang, Luong Trung Kien, and Le Anh Ngoc. "Continuous Deep Learning Based on Knowledge Transfer in Edge Computing." In Intelligent Systems and Networks. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4725-6_59.

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Michail-Alexandros, Kourtis, Christinakis Dimitris, Xilouris George, Thanos Sarlas, Soenen Thomas, and Kourtis Anastasios. "Evaluation of Edge Technologies Over 5G Networks." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40690-5_40.

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Jin, Wenquan, Vijender Kumar Solanki, Anh Ngoc Le, and Dohyeun Kim. "Real-Time Inference Approach Based on Gateway-Centric Edge Computing for Intelligent Services." In Intelligent Systems and Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2094-2_44.

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To, Hai-Thien, Trung-Kien Le, and Chi-Luan Le. "Real-Time End-to-End 3D Human Pose Prediction on AI Edge Devices." In Intelligent Systems and Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2094-2_31.

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Nguyen, Thuong H. N., Quy C. Nguyen, Viet H. H. Ngo, Fabien Ferrero, and Tuan V. Pham. "Edge AI Implementation for Recognizing Sounds Created by Human Activities in Smart Offices Design Concepts." In Intelligent Systems and Networks. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3394-3_70.

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Dong, Hoang-Nhu, Nguyen-Xuan Ha, and Dang-Minh Tuan. "A New Approach for Large-Scale Face-Feature Matching Based on LSH and FPGA for Edge Processing." In Intelligent Systems and Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2094-2_42.

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Kisters, Philipp, Leonie van der Veen, and Janick Edinger. "Privacy-Preserving Edge Processing in Decentralized Citizen-Centric Sensor Networks." In Intelligent Distributed Computing XVI. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60023-4_16.

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Conference papers on the topic "Intelligent Edge Networks"

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Fang, Juan, Yaxin An, Ziyi Teng, and Shen Wu. "Partitioning Intelligent Applications Based Inception Networks in Mobile Edge Computing." In 2024 20th International Conference on Mobility, Sensing and Networking (MSN). IEEE, 2024. https://doi.org/10.1109/msn63567.2024.00141.

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Qi, Shuang, Bin Lin, Xiaoyu Zhang, and Lue Yang. "Partitionable Task Offloading for Intelligent Ship-assisted Maritime Edge Computing Networks." In 2024 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2024. http://dx.doi.org/10.1109/iccc62479.2024.10681943.

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Cheng, Wenge. "Intelligent Assisted Navigation for Ships Using Edge Computing and Neural Networks." In 2024 International Conference on Intelligent Computing and Robotics (ICICR). IEEE, 2024. http://dx.doi.org/10.1109/icicr61203.2024.00036.

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Golcarenarenji, Gelayol, Rinat Khusainov, Alexander Gegov, and Ignacio Martinez-Alpiste. "Detecting Violent Behaviour on Edge Using Convolutional Neural Networks." In 2024 IEEE 12th International Conference on Intelligent Systems (IS). IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705272.

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Koshika, Manoj, Prabhakar Kandukuri, and Shyam Kumar Moppey. "Enhanced Edge Attention-Based SRCNN for Retinal Image Super-Resolution." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934489.

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Telçeken, Merve, and Elif Bozkaya-Aras. "A Trustworthy Federated Learning Model: Client Selection for IoT Edge Networks." In 2024 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2024. https://doi.org/10.1109/asyu62119.2024.10756968.

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Wang, Zhengli, Fengzhao Su, and Lingyi Meng. "A method for predicting accident risks based on edge networks." In Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024), edited by Hao Chen and Wei Shangguan. SPIE, 2025. https://doi.org/10.1117/12.3050662.

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Han, Jie, Weijie Bao, and Qian Liu. "Reconfigurable Intelligent Surface-Assisted Offloading Strategy for UAV-Enabled Vehicle Edge Computing Networks." In 2023 International Conference on Information Processing and Network Provisioning (ICIPNP). IEEE, 2023. https://doi.org/10.1109/icipnp62754.2023.00021.

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Ye, Yutian, and Quan Yu. "Joint Optimization of Cooperative Caching and Recommendation in Wireless Edge Networks." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743470.

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Dalgitsis, Michail, Godfrey M. Kibalya, Maria A. Serrano, Jordi Serra, and Angelos Antonopoulos. "Exploiting 6G RAN and Core Network Information for Intelligent Edge-Cloud Service Orchestration." In 2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit). IEEE, 2025. https://doi.org/10.1109/eucnc/6gsummit63408.2025.11037032.

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Reports on the topic "Intelligent Edge Networks"

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa
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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computat
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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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Ruvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu, and Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42169.

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Managing the ever-growing volume and velocity of data across the battlefield is a critical problem for warfighters. Solving this problem will require a fundamental change in how battlefield analyses are performed. A new approach to making decisions on the battlefield will eliminate data transport delays by moving the analytical capabilities closer to data sources. Decision cycles depend on the speed at which data can be captured and converted to actionable information for decision making. Real-time situational awareness is achieved by locating computational assets at the tactical edge. Acceler
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Ramakrishnan, Aravind, Fangyu Liu, Angeli Jayme, and Imad Al-Qadi. Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-030.

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For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic–Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effecti
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