Distributed model cache-driven device-cloud collaboration in adaptive video analytics by LIAO Tianjun, ZHAO Maozhe, LIU Shengzhong, WU Fan

Subjects: “…device-cloud collaboration…”
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Cloud native robot system based on edge computing by Dawei WANG, Zhuo WANG, Peng WANG, Zhigang WANG, Haitao WANG

“…With the rapid development of artificial intelligence,the global robot market continues to grow quickly,and the capabilities of robots have evolved from performing fixed operations to the ability to autonomously sense,understand and make decisions.However,to achieve large-scale application of robots,robots need to have powerful computing capabilities and low deployment costs under the constraints of limited power consumption.Using edge computing to provide more cost-effective services,enhance the computing power of the robot body,and achieve large-scale deployment is the key to achieving this goal.The challenges faced by robot systems with the edge enhancement were analyzed,the concept of cloud-native robot systems based on edge computing was proposed,and four feasible technical solutions for implementing the system were discusses.The cloud-native robot system is the inevitable direction for the development of robot systems from intelligent systems based on robot ontology to cloud-edge-end fusion multi-robot collaborative intelligent systems and the key technology for promoting the large-scale application of robots.…”
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Evaluating QoS in Dynamic Virtual Machine Migration: A Multi-Class Queuing Model for Edge-Cloud Systems by Anna Kushchazli, Kseniia Leonteva, Irina Kochetkova, Abdukodir Khakimov

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Application of edge-cloud collaborative intelligence technologies in power grids by Qing HAN, Kunlun GAO, Ting ZHAO, Jiangqi CHEN, Xinyu YANG, Shusen YANG

“…With the continuous development of the Internet of things on electricity (IoTE) and large-scale deployment of intelligent edge devices, an explosively increasing amount of data are being generated at the network edge.The efficient, fast and secure processing and analysis of the massive edge located data brings great challenges for the traditional cloud computing-based intelligence technologies.Instead, edge-cloud collaborative intelligence (ECCI) technologies can significantly outperform the cloud computing-based intelligence in terms of the network bandwidth saving, delay reduction and privacy protection, and therefore have shown a great potential in boosting the development of power grids.To investigate the application of ECCI in power grids, the concept and research progress of ECCI were firstly introduced.The characteristics and advantages of ECCI were summarized and its applicability in the power grids were discussed.Secondly, the key technologies of ECCI applications for power grids were discussed and the solutions based on ECCI technologies for two typical scenes were proposed respectively.Finally, a brief discussion of future work was given.…”
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Assurance in Advanced 5G Edge Continuum by Filippo Berto, Claudio A. Ardagna, Massimo Banzi, Marco Anisetti

“…The implementation of distributed applications is more frequently achieved through the configuration of service-oriented workflows, which are then deployed within the Edge-Cloud Continuum. This approach facilitates the support of distributed processing pipelines. …”
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Cloud-edge hybrid deep learning framework for scalable IoT resource optimization by Umesh Kumar Lilhore, Sarita Simaiya, Yogesh Kumar Sharma, Anjani Kumar Rai, S. M. Padmaja, Khan Vajid Nabilal, Vimal Kumar, Roobaea Alroobaea, Hamed Alsufyani

“…Abstract In the dynamic environment of the Internet of Things (IoT), edge and cloud computing play critical roles in analysing and storing data from numerous connected devices to produce valuable insights. …”
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Automatic Edge Detection From Point Clouds Collected by Terrestrial Laser Scanners by Anh Thu Thi Phan, Thi Ngoc Huynh, Mai Pham Huynh

“…In this study, an automated method for extracting edge points from 3D point clouds collected by terrestrial laser scanners is introduced. …”
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Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning by Zhenwu Lei, Yue Zhang, Jing Wang, Meng Zhou

“…Through the incorporation of these modules, the model notably enhances feature extraction and computational efficiency while reducing the model size and computational load, making it more conducive for deployment on edge devices. Furthermore, a cloud-edge collaborative defect detection system is constructed to improve detection accuracy and efficiency through initial detection by edge devices, followed by additional inspection by cloud servers. …”
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Cloud-edge collaborative high-frequency acquisition data processing for distribution network resilience improvement by Sanlei Dang, Jie Zhang, Tao Lu, Yongwang Zhang, Peng Song, Jun Zhang, Rirong Liu

“…To realize transparent monitoring and resilience improvement of low-voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server’s real-time response capabilities and the cloud server’s robust data processing power to enhance the performance of high-frequency data acquisition processing. …”
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A cloud-edge collaborative optimization control strategy for voltage in distribution networks with PV stations by Guoqing Li, Wei Wang, Dan Pang, Zhipeng Wang, Weixian Tan, Zhenhao Wang, Jinming Ge

“…In order to meet the requirements of voltage optimization and adjustment, the optimization problem is divided into cloud front precomputation and edge computing device cooperative optimization computation with the framework of cloud-edge cooperation. …”
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Computer Vision System for Multi-Robot Construction Waste Management: Integrating Cloud and Edge Computing by Zeli Wang, Xincong Yang, Xianghan Zheng, Daoyin Huang, Binfei Jiang

“…Most construction waste recycling robotic systems are developed based on a client-server framework, which means that all robots need to be continuously connected to their respective cloud servers. …”
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Research on Key Technology of Cloud-edge Coordinated Digital Twin Manufacturing Platform Towards Customized Production by HU Tianliang, ZHOU Shuaichang, MENG Qi, DONG Lili, ZHOU Tingting, LIU Xiaojun

“…This study targets the perception, decision making, and execution stages of manufacturing and proposes a cloud-edge collaborative digital-twin platform architecture together with an implementation roadmap. …”
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Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework by Kaushik Sathupadi, Sandesh Achar, Shinoy Vengaramkode Bhaskaran, Nuruzzaman Faruqui, M. Abdullah-Al-Wadud, Jia Uddin

“…A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud. …”
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Resource Management Techniques for the Internet of Things, Edge, and Fog Computing Environments by Koushik Chakraborty, Manmohan Sharma, Krishnaveni Kommuri, Voore Subrahmanyam, Pratap Patil, Manmohan Singh Yadav

“…The center points discontinuously send logical summary information to the cloud. An example of an edge computer is a smartphone connected to a cloud system. …”
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Enhancing DevOps Practices in the IoT–Edge–Cloud Continuum: Architecture, Integration, and Software Orchestration Demonstrated in the COGNIFOG Framework by Kostas Petrakis, Evangelos Agorogiannis, Grigorios Antonopoulos, Themistoklis Anagnostopoulos, Nasos Grigoropoulos, Eleni Veroni, Alexandre Berne, Selma Azaiez, Zakaria Benomar, Harry Kakoulidis, Marios Prasinos, Philippos Sotiriades, Panagiotis Mavrothalassitis, Kosmas Alexopoulos

“…This paper presents COGNIFOG, an innovative framework under development that is designed to leverage decentralized decision-making, machine learning, and distributed computing to enable autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. The work emphasizes Continuous Integration/Continuous Deployment (CI/CD) practices, development, and versatile integration infrastructures. …”
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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

“…The limitations of replay-based continuous learning are (1) the limited amount of historical training data that can be stored due to limited memory capacity, and (2) the computational resources of on-device systems are significantly lower than those of servers or cloud infrastructures. …”
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Enhancing offloading with cybersecurity in edge computing for digital twin‐driven patient monitoring by Ahmed K. Jameil, Hamed Al‐Raweshidy

“…In this study, a new method was introduced, combines edge computing with sophisticated cybersecurity solutions. …”
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LightSTATE: A Generalized Framework for Real-Time Human Activity Detection Using Edge-Based Video Processing and Vision Language Models by Anik Debnath, Yong-Woon Kim, Yung-Cheol Byun

“…This research introduces a new approach named LightSTATE, a lightweight hybrid framework designed for real-time activity detection by integrating edge-based preprocessing with cloud-hosted Vision Language Models (VLMs). …”
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What If VEC Is Moving: Probabilistic Model of Task Execution Through Offloading in Vehicular Computing Environments by Asmaa Ibrahim, Bassem Mokhtar

“…Various computing approaches within vehicular networks, such as vehicular edge computing (VEC) and cloud computing, have been suggested to facilitate task offloading, aiming to improve user satisfaction. …”
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Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks by R. Augustian Isaac, P. Sundaravadivel, V. S. Nici Marx, G. Priyanga

“…Abstract As the number of service requests for applications continues increasing due to various conditions, the limitations on the number of resources provide a barrier in providing the applications with the appropriate Quality of Service (QoS) assurances. …”
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