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   Articles with "cloud collaborative" as a keyword



YOMO TF based edge cloud collaborative surveillance framework for tobacco warehouse safety management

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-26011-2

Abstract: Tobacco warehousing requires continuous surveillance to mitigate risks like unauthorized access, fire hazards, and moisture-induced decay. To address these challenges, this paper proposes an edge-cloud collaborative surveillance framework with adaptive deep learning, termed YOMO-TF (YOLO + MobileOne + Transformer + Federated… read more here.

Keywords: edge cloud; cloud collaborative; framework; surveillance ... See more keywords

Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network

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recommendations! Published in 2024 at "IEEE Transactions on Cloud Computing"

DOI: 10.1109/tcc.2024.3361858

Abstract: With the rapid development of artificial intelligence and Unmanned Aerial Vehicle (UAV) technology, AI-based UAVs are increasingly utilized in various industrial and civilian applications. This paper presents a distributed Edge-Cloud collaborative framework for UAV object… read more here.

Keywords: edge cloud; object detection; cloud collaborative; edge ... See more keywords

A Cloud Collaborative-Based Intrusion Detection and Prevention System for IVN

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recommendations! Published in 2025 at "IEEE Transactions on Cognitive Communications and Networking"

DOI: 10.1109/tccn.2024.3516052

Abstract: With the increasing intelligence and convenience of modern vehicles, Internet-of-Vehicle (IoV) technology plays a pivotal role in driving these advancements. While IoV enhances user services, it also introduces security threats, particularly intrusions into the In-Vehicle… read more here.

Keywords: detection; system; cloud collaborative; vehicle ... See more keywords

Online Management for Edge-Cloud Collaborative Continuous Learning: A Two-Timescale Approach

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recommendations! Published in 2024 at "IEEE Transactions on Mobile Computing"

DOI: 10.1109/tmc.2024.3451715

Abstract: Deep learning (DL) powered real-time applications usually need continuous training using data streams generated over time and across different geographical locations. Enabling data offloading among computation nodes through model training is promising to mitigate the… read more here.

Keywords: edge cloud; cloud collaborative; long term; two timescale ... See more keywords