The main challenges include network handoffs when sensors lose connection, requiring AI to decide between caching locally or degrading gracefully. Real-world conditions like cellular dead zones and Wi-Fi shadows differ from lab testing. Additionally, data drift at scale becomes a major risk - models that work on 100 lab devices may fail with 10,000 units in the wild due to environmental variations and hardware tolerances.
The main challenges include network handoffs when sensors lose connection, requiring AI to decide between caching locally or degrading gracefully. Real-world conditions like cellular dead zones and Wi-Fi shadows differ from lab testing. Additionally, data drift at scale becomes a major risk - models that work on 100 lab devices may fail with 10,000 units in the wild due to environmental variations and hardware tolerances.
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The main challenges include network handoffs when sensors lose connection, requiring AI to decide between caching locally or degrading gracefully. Real-world conditions like cellular dead zones and Wi-Fi shadows differ from lab testing. Additionally, data drift at scale becomes a major risk - models that work on 100 lab devices may fail with 10,000 units in the wild due to environmental variations and hardware tolerances.
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