Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT
Liyu Zhang, Yejia Liu, Kwun Ho Liu, Runxi Huang, Xiaomin Ouyang

TL;DR
Chorus is a novel, data-free approach that learns context representations to adapt IoT sensor models to unseen deployment conditions, improving performance and reducing inference latency.
Contribution
It introduces a context-aware, data-free model customization method that learns shared sensor-context representations and employs a lightweight gated head for efficient adaptation.
Findings
Outperforms state-of-the-art baselines by up to 20.2% in unseen contexts.
Reduces inference latency close to sensor-only deployment.
Maintains stable performance under continuous context transitions.
Abstract
A key bottleneck toward scalable IoT sensing is how to efficiently adapt AI models to new deployment conditions. In real-world IoT systems, sensor data is collected under diverse contexts, such as sensor placements or ambient environments, which alter signal patterns and degrade downstream performance. Traditional domain adaptation and generalization methods often ignore such contextual information or incorporate it in overly simplistic ways, making them ineffective under unseen context shifts after deployment. In this paper, we propose Chorus, a context-aware, data-free model customization approach that adapts models to unseen deployment conditions without requiring target-domain data. The key idea is to learn context representations that capture how contextual factors influence sensor data, and then use these representations as structured priors for context-aware customization under…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · IoT and Edge/Fog Computing · Green IT and Sustainability
