Hello everyone,

I'm conducting research on dependable system design for wireless sensor networks (WSNs), with a particular focus on how TinyML can be leveraged to enhance system dependability.

In resource-constrained environments, ensuring reliable operation despite failures, noise, and changing conditions is critical. I'm investigating whether embedding TinyML models directly into sensor nodes can support:

• Fault prediction and early anomaly detection
• Local intelligent decision-making
• Improved self-healing or self-adaptive mechanisms

My goal is to frame this within established principles of dependable computing—including reliability, availability, safety, maintainability, and integrity.

I'm seeking:

• Research papers or frameworks that link TinyML to dependable systems
• Case studies of WSNs with built-in AI/ML for improved resilience
• Best practices for balancing accuracy and system resource constraints

Any input, experiences, or references would be greatly appreciated.

Thank you!