## 1 Introduction  
To address ubiquitous computing, edge computing, and distributed sensor networks, as well as a significant increase in device density and sensor deployment towards smart and self-aware sensors, sophisticated and dependable data processing architectures are required. The field of tiny machine learning (ML) is an emerging field posing challenges that are only partially addressed [1]. Floating-point arithmetic, with a high dynamic range and sufficient precision, is frequently used to compute machine learning models. Only integer arithmetic (8-32 bit) is supported by very low-resource tiny embedded systems, e.g., ARM Cortex M0-based systems; hence, training with integer arithmetic must be completed directly on the target device [2] or by model modification and freezing [3]. The computation of complex deep learning (DL) models is further limited by memory and computing power constraints of ultra-low-power devices [4]. To overcome software limitations and limited computability, hardware designs are becoming more popular [5]. We focus on the software processing of ML models on low-resource and low-power devices by model transformation fitting of low-resource devices. The present work addresses virtualization on the programming level in IoT and sensor networks using very low-resource computers, typically with less than 64 kB of available RAM, with a particular focus on machine learning (ML) provided as a virtualized service. Compared with [6], we provide new algorithms and ML applications, including ANN/CNN-based regression and classification tasks, with a rigorous evaluation of discretization errors. The set of discretized non-linear transfer functions is extended, optimized, and evaluated rigorously. A new unified data set derived from physical simulation is used to demonstrate the capability and accuracy of the ML VM service, which can be deployed on any very low-resource micro-controller providing integer arithmetic only, as well as on desktop computers. We evaluate the VM MLISA and the model transformation process with respect to constraints and its application in Structural Health Monitoring. A functional prediction or regression model is composed of linear and non-linear functions. The most critical part in the transformation and scaling process of ML models towards integer arithmetic is the non-linear function, e.g., the _tanh_ transfer function. A chained composition can introduce high non-linearity, which must be handled carefully to avoid exploding model errors. For this purpose, we will train ANN models with data from highly non-linear analytical functions for the implementation of surrogate models. There is an extended evaluation of computational complexity and requirements for most relevant applications. The advantage of using a textual programming language instead of binary code is demonstrated by extended examples. This work focuses on a universal VM suitable for implementation on very low-resource embedded systems and providing ML as a programmable service. Although \"as a service\" is a common cloud-based paradigm, we use this term in terms of virtualization on node and single computer level. There is ongoing work to implement the computation of ML models with 8 or less bit integer arithmetic (and storage data size) on micro-controllers [7; 8], commonly called Tiny ML [9] or ML on commodity devices [1]. We will relax this hard constraint by assuming a 32-bit microprocessor, e.g., the widely used Arm Cortex M series with chip die areas below 0.1 mm\({}^{2}\) and power consumption about 10 mW (active mode). Finally, we implement ML with 16 bit data-size storage (input, intermediate, and output data as well as model parameters). Overflow issues are relaxed by using 32-bit arithmetic internally. In [9], the authors outlined the benefits of Tiny ML in the context of sensor networks. Tiny ML enables the local processing of sensor data without extensive periodic communication to external serves, especially in the context of real-time capable structural health monitoring (SHM) systems.