The growing market of wearables is expanding into different areas of application such as devices designed to improve and monitor sport activities. This in turn is pushing research on low-cost, very low-power wearable systems with increased analysis capabilities. This paper proposes integrated energy-aware techniques and a convolutional neural network (CNN) for a cardiac arrhythmia detection system that can be worn during sport training sessions. The dynamic power management strategy (DPMS) is programmed into an ultra-low-power microcontroller, and in combination with a photovoltaic (PV) energy harvesting (EH) circuit, achieves a battery-life extension towards a self-powered operation. The CNN-based analysis filters, scales the image, and using a bicubic technique, interpolates the measurements to subsequently classify the electrocardiogram (ECG) signal into normal and abnormal patterns. Experimental results show that the EH-DPMS achieves an extension in the battery charge for a total of 14.34% more energy available, which represents 12 consecutive workouts of 45 min without the need to manually recharge it. Furthermore, an arrhythmia detection precision of 98.6% is achieved among the experimental sessions using 55,222 images for training the system with the MIT-BIH, QT, and long-term ST databases, and 1320 implemented on a wearable system. Therefore, the proposed wearable system can be used to monitor an athlete’s condition, reducing the risk of abnormal heart conditions during sports activities.

Intelligent sensing systems based on the edge-computing paradigm are essential for the implementation of Internet of Things (IoT) and Agriculture 4.0 applications. The development of edge-computing wireless sensing systems is required to improve the sensor’s accuracy in soil and data interpretation. Therefore, measuring and processing data at the edge, rather than sending it back to a data center or the cloud, is still an important issue in wireless sensor networks (WSNs). The challenge under this paradigm is to achieve a sustainable operation of the wireless sensing system powered with alternative renewable energy sources, such as plant microbial fuel cells (PMFCs). Consequently, the motivation of this study is to develop a sustainable forage-grass-power fuel cell solution to power an IoT Long-Range (LoRa) network for soil monitoring. The stenotaphrum secundatum grass plant is used as a microbial fuel cell proof of concept, implemented in a 0.015 m${}^3$-chamber with carbon plates as electrodes. The BQ25570 integrated circuit is employed to harvest the energy in a 4 F supercapacitor, which achieves a maximum generation capacity of $1.8$ mW. The low-cost pH SEN0169 and the SHT10 temperature and humidity sensors are deployed to analyze the soil parameters. Following the edge-computing paradigm, the inverse problem methodology fused with a system identification solution is conducted, correcting the sensor errors due to non-linear hysteresis responses. An energy power management strategy is also programmed in the MSP430FR5994 microcontroller unit, achieving average power consumption of 1.51 mW, ∼19% less than the energy generated by the forage-grass-power fuel cell. Experimental results also demonstrate the energy sustainability capacity achieving a total of 18 consecutive transmissions with the LoRa network without the system’s shutting down.

Electroless ZnO (≈900 nm) was deposited on the surface of an Mg-Al alloy (AM60) to reduce its degradation in the marine environment. Uncoated and coated ZnO samples were exposed to an SME simulated marine solution for up to 30 days. The AFM and optical images revealed that the corrosion attack on the ZnO-AM60 surface was reduced due to an increase in the surface hydrophobicity of the ZnO coating (contact angle of ≈91.6°). The change in pH to more alkaline values over time was less pronounced for ZnO-AM60 (by ≈13%), whereas the release of ${\mathrm{Mg}}^{2+}$ ions was reduced by 34 times, attributed to the decrease in active sites on the Mg-matrix provided by the electroless ZnO coating. The OCP (free corrosion potential) of ZnO-AM60 shifted towards less negative values of ≈100 mV, indicating that electroless ZnO may serve as a good barrier for AM60 in a marine environment. The calculated polarization resistance (${\mathrm{R}}_{\mathrm{p}}$), based on EIS data, was ≈3 times greater for ZnO-AM60 than that of the uncoated substrate.

The Green Internet of Things (GIoTs) has emerged as a transformative paradigm for environmental conservation, enabling autonomous, self-sustaining sensor networks that operate without batteries and with minimal ecological footprint. This approach is especially critical for long-term mangrove monitoring in smart coastal cities, where conventional battery-powered systems are impractical due to frequent, costly, and environmentally disruptive replacements that hinder continuous data collection. This paper presents a self-sustaining GIoT sensing system for mangrove monitoring powered by sedimentary microbial fuel cells (SMFCs), enabling perpetual, battery-less, and zero-emission operation. A spatial dynamic energy management (DPM) strategy is implemented for the efficient integration of a microcontroller unit with a LoRa wireless communication transceiver and the SMFC harvested energy, ensuring a balanced self-sustained approach into a GIoT sensing network. Experimental results demonstrate an average power consumption of 190.45 μW per 14-byte data packet transmission, with each packet containing pH, electrical conductivity and ambient temperature measurements from the mangrove environment. Under the spatial DPM strategy, the network of four sensing nodes exhibited an energy consumption of 1.14 mWh. Given a harvested power density of 15.1 mW/m² from the SMFC, and utilizing a 0.1 F supercapacitor as an energy buffer, the system can support at least six consecutive data transmissions. These findings validate the feasibility of sustainable, low-power GIoT architectures for ecological monitoring.