AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Dronuum decomposes the wildfire detection pipeline into modular services, including image acquisition, preprocessing, inference, and alerting, and a lightweight YOLOv8n-based classifier is employed for fire detection.
This work introduces a HAP-native Agentic AI framework and identifies trustworthy control, collaborative multi-HAP orchestration, and digital-twin-assisted lifelong adaptation as key steps toward deployable, sustainable, and resilient SAGIN intelligence.
The experiments demonstrate the feasibility of real-time, data-driven optimization in dynamic aerial environments, while also revealing practical challenges in field deployments of networked UAV systems, including power constraints, sensing limitations, and deployment logistics.
Experimental evaluation demonstrates significant reductions in response time, network traffic, and service latency while improving throughput and resource utilization compared with conventional cloud-only approaches, and confirms that intelligent fog–cloud orchestration provides an effective solution for supporting rea...
The key message is that there is a new central design coordination of the cloud architecture that enables sustainable growth of the cloud, not merely increasing incremental capacity on hardware.
A comparative evaluation of four ROS 2 deployment patterns for cloud-edge robotic AI: monolithic containers, microservices, dynamic module loading and overlay workspaces reveals a clear trade-off between monolithic and microservices patterns, and shows that no pattern dominates.
This paper presents field-validated results from an edge-deployed Computer Vision Intelligence (VI) system for automated HSE compliance monitoring across multiple oilfield sites in Southeast Asia and West Africa. The solution combines advanced VI applications with state-of-the-art YOLOv8 and YOLOv11 object detection mo...
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