Machine-Learning model implementation in Resource-Scarce Embedded Systems is becoming a standard in many systems and projects. This implementation allows systems to be less Cloud dependent and make decisions independently. As these systems' reasoning becomes intricate with the Machine Learning model's decision, an attack to change the Machine Learning model's data structure can make the entire system misbehave, which in some solutions can be critical. Therefore, it is necessary to create low-overhead tools to flag any miscalculation or wrongdoing during the model's inference phase. The following work presents a Machine Learning Health Monitoring system based on PCA and Control Charts to verify if the model's inference function runs properly. The solution presents a reasonable flag rate and is implemented e ciently in a Resource-Scarce Embedded System due to its reduced memory and processing footprints.

The continuous research on TinyML has increased the number of Machine Learning (ML) models capable of running their inference phase in a Resource-Scarce Embedded System (RSES). Therefore, part of the intelligence services run now in the devices at the end of the network. However, many ML models are too complex to run in such tiny devices, and a Cloud system is necessary to implement the network’s intelligence inference layer. Every communication between a RSES and the Cloud is expensive in terms of power consumption, money, and time. The following work tries to answer how to reduce the number of times a RSES communicates with the Cloud system while achieving the same ML inference rate, and without reducing the model’s accuracy. The results show how by building a cache system that allows the RSES to store previous samples and their predictions, the RSES can use this information to avoid Cloud communication. The solution has proven to work and to accomplish a communication reduction between the cloud system and the RSES by 30%.