One of the most important challenges in modern digital systems, especially regarding autonomous embedded systems, is energy efficiency. This work studies an energy consumption optimization approach on a microcontroller that implements IoT-like applications, featuring Dynamic Voltage and Frequency Scaling (DVFS) capabilities, by dynamically changing the supply voltage and clock frequency. The proposed approach categorizes tasks according to their demands on timing requirements and analyzes speed–energy efficiency trade-offs. Results strongly indicate that energy performance is improved due to the proper adjustment of configurations towards required tasks. The findings are verified within a set of scenarios that highlight the potential balance between energy economy and operational demands for specialized IoT contexts.

Honey bees play a vital role in ecosystem stability, and the need to monitor colony health has driven the development of IoT-based systems in beekeeping, with recent studies exploring both empirical and machine learning approaches to detect and analyze key hive conditions. In this study, we present an IoT-based system that leverages sensors to record and analyze the acoustic signals produced within a beehive. The captured audio data is transmitted to the cloud, where it is converted into mel-spectrogram representations for analysis. We explore multiple data pre-processing strategies and machine learning (ML) models, assessing their effectiveness in classifying queenless states. To evaluate model generalization, we apply transfer learning (TL) techniques across datasets collected from different hives. Additionally, we implement the feature extraction process and deploy the pre-trained ML model on a deep edge IoT device (Arduino Zero). We examine both memory consumption and execution time. The results indicate that the selected feature extraction method and ML model, which were identified through extensive experimentation, are sufficiently lightweight to operate within the device’s memory constraints. Furthermore, the execution time confirms the feasibility of real-time queenless state detection in edge-based applications.

This work introduces a light harvesting system with battery management. In contrast to relevant solutions that operate in limited ranges, the proposed system covers a wide operating input power range from 10 uW up to 300 mW. Specifically, experimental results highlight that, combined with a 73 × 94 mm flexible light harvester, it can harness light in a range from 50 LUX (indoor lighting) up to 120,000 LUX (outdoor lighting). The introduced system consists of a boost converter and an ultra-low power microcontroller (MCU). The MCU performs Global Maximum Power Point Tracking (GMPPT), using a resistor-free time-based input power sensing method, to calculate the input power of the converter, which does not interfere with the operation of the boost converter. The efficiency of the GMPPT system was evaluated with detailed experimentation, where we achieved 99.75% average GMPPT tracking efficiency while consuming only 73.5 uW at 4.2 V.

In the era of IoT and mobile computing an important factor for the lifespan of related devices is current consumption. In this paper the current consumption of an audio monitoring IoT system that incorporates an RP2040 MCU and a PDM MEMS microphone, and performs recording, processing, and transmitting of data is explored. A variety of measurement sets were conducted for current profiling purposes. The MCU was monitored during different operating conditions, working phase, light sleep, deep sleep and dormant. Its consumption was measured during execution of various computational loads, as defined by three algorithms as FFT, FIR and Autocorrelation. Additionally, the power consumption for data transmission over UART protocol was measured in different baud rates. Furthermore, all the above measurements were performed in several operating conditions, regarding frequency and voltage settings. The collected measurements form a guide on energy demands of an IoT system audio specific based on RP2040 MCU.

A large number of autonomous devices is nowadays supported by renewable and green energy sources. A vital sub-circuit in such systems is the power converter circuit, which should efficiently transform and store the available energy. In order to obtain the maximum efficiency under varying energy conditions, various maximum power point tracking (MPPT) methods are used. In this work a complete harvesting module with battery management and MPPT is presented, suitable for a plethora of autonomous applications. A novel, low-complexity and ultra-low power consumption design is proposed, which offers very wide operating voltage and power range with high MPPT efficiency and very low power consumption. It can be combined with different harvesters, such as thermoelectric generators or photovoltaic panels and is able to work under widely varying energy conditions. As supported by experimental results, the proposed module covers a very wide working input power range, from 40 μW up to 4 W, as well as a very wide input voltage range, from 650 mV up to 2.8 V with 96.5% average MPPT efficiency and a total power consumption of 3.9 μW at 3.6 V. The module relies on an embedded ultra-low power microcontroller unit (MCU) to perform the power management and MPPT operations, which can also be used for extra tasks (e.g., sensor reading). Using the proposed module, an autonomous sensor node was built, able to acquire acceleration measurements, and wirelessly communicate with a remote user in order to send an alert or stream the acquired sensor data in real time.