An adaptive DNN partitioning framework for heterogeneous edge-cloud systems reduces energy consumption by 27-36% and end-to-end latency by 6-23% versus static baselines on real hardware with VGG16, AlexNet, and MobileNetV2.
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A contextual fusion model for vehicle predictive maintenance detects all six wear-driven events in real multi-country data with 12.2-day mean error and improves F1 by 2.6 points when context is added.
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Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum
An adaptive DNN partitioning framework for heterogeneous edge-cloud systems reduces energy consumption by 27-36% and end-to-end latency by 6-23% versus static baselines on real hardware with VGG16, AlexNet, and MobileNetV2.
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AI-Driven Predictive Maintenance with Environmental Context Integration for Connected Vehicles: Simulation, Benchmarking, and Field Validation
A contextual fusion model for vehicle predictive maintenance detects all six wear-driven events in real multi-country data with 12.2-day mean error and improves F1 by 2.6 points when context is added.