Data Resilience in Cyber-Physical Systems: Overcoming MQTT Data Loss for Reliable AI Subsystems

The paper introduces and experimentally validates an end-to-end data-resilience layer for MQTT-based CPS that maintains AI subsystem reliability under adverse network conditions. Concretely, it contributes practical mechanisms (loss-aware buffering/retransmission, QoS/retention strategies, deduplication/back-pressure, and gap-tolerant ingestion for AI) and shows, through controlled tests with packet loss and instability, that operational continuity and model performance can be sustained despite communication faults.