The integration of machine learning on microcontrollers at the extreme edge of the network is gaining significance for various Internet of Things applications. This paradigm shift enables real-time data processing and decisionmaking directly on the devices, thereby reducing latency and reliance on cloud connectivity. Nevertheless, due to the computational and memory limitations of microcontrollers, training and deploying a neural network using existing libraries such as TinyML remains impractical for some devices. This paper proposes an approach where the model is trained in the cloud, with only the forward phase directly implemented on the microcontroller. We leverage the LittleBits platform for its user-friendly interface and suitability for educational purposes, aiming to introduce machine learning concepts to non-expert users. Our case study focuses on temperature prediction in smart greenhouses, demonstrating the practical utility of machine learning in timely interventions for plant disease prevention and optimal growth conditions. This work not only illustrates the feasibility of deploying machine learning on resource-constrained devices but also emphasizes the potential of LittleBits in making novel technologies accessible and comprehensible to a broader audience.

Climate change poses significant challenges, particularly in agriculture, where extreme weather events demand more efficient and resilient systems, such as smart greenhouses. These controlled environments require predictive solutions to optimize conditions such as temperature and humidity, which are critical for crop growth. While neural networks are widely employed for climate prediction, their complexity presents a barrier to implementation on edge devices, which are characterized by limited computational resources. In this work, we propose Fuzzy-Augmented Neural Network (FANN), a novel approach based on fuzzy sets, applied in cascade to regressive neural network models, to reduce complexity and energy consumption in greenhouse microclimate classification and prediction. The methodology was tested on four edge devices, including microcontrollers and microprocessors. We compare our FANN approach with standard models (FFNN, BNN, SimpleRNN, GRU, LSTM), highlighting significant reductions in inference time, energy consumption, and memory usage. FANN also offers practical advantages, such as the ability to adapt classification by modifying fuzzification parameters without retraining the model, and the potential to parallelize computations for simultaneously classifying the microclimate of multiple crops. These features make the system flexible and optimal for practical applications in dynamic agricultural contexts.

In teletraffic engineering, M/G/1 queues are pivotal for optimizing network performance and operational efficiency. Closed formulas like the mean-value Pollaczek-Khinchin (MV-PK) are highly valued by practitioners due to their simplicity, facilitating rapid and flexible system analyses. This formula aids in verifying the reliability of complex simulations concerning M/G/1 models, especially when applicable. The challenge intensifies when dimensioning M/G/1 systems with heavy-tailed service time distributions. Simulation of such queues becomes notably arduous, necessitating increased number of samples and longer simulation durations. Moreover, verification becomes more intricate as the MV-PK formula cannot be straightforwardly applied in such cases. This paper extends the MV-PK formula to heavy tailed service distributions with infinite/infinitesimal moments using Nonstandard Analysis. Additionally, it shows how recently introduced Bounded Algorithmic Numbers (BANs) enables numerical verification of the extended PK formula via discrete-event simulations of the M/G/1 queue, even when predicted delay values are infinite. The implemented approach tends to converge fast and exhibits remarkable numerical robustness. It adheres to the principle of “write once, run multiple times”, as the same source code for queue simulation handles both finite and infinite variance cases.