In this paper, we describe a novel animal-tracking-system, solely powered by thermal energy harvesting. The tracker achieves an outstanding 100 $\mathsf{\mu }\mathrm{W}$ of electrical power harvested over an area of only 2 times $20.5$$\mathrm{c}$$\mathrm{m}$${}^2$, using the temperature difference between the animal’s fur and the environment, with a total weight of 286 $\mathrm{g}$. The steps to enhance the power income are presented and validated in a field-test, using a system that fulfills common tracking-tasks, including GPS with a fix every 1.1 $\mathrm{h}$ to 1.5 $\mathrm{h}$, activity and temperature measurements, all data wirelessly transmitted via LoRaWAN at a period of 14 $\min$. Furthermore, we describe our ultra low power design that achieves an overall sleep power consumption of only 8$\mathsf{\mu }\mathrm{W}$ and is able to work down to temperature differences of $0.9$$\mathrm{K}$ applied to the TEGs.

Polymer-based capacitive strain gauges are a novel and promising concept for measuring large displacements and strains in various applications. These novel sensors allow for high strain, well above the maximum values achieved with state-of-the-art strain gauges (Typ. 1%). In recent years, a lot of interest in this technology has existed in orthopedics, where the sensors have been used to measure knee laxity caused by a tear of the anterior cruciate ligament (ACL), and for other ligament injuries. The validation of this technology in the field has a very low level of maturity, as no fast, reproducible, and reliable manufacturing process which allows mass production of sensors with low cost exists. For this reason, in this paper, a new approach for the fabrication of polymer-based capacitive strain gauges is proposed, using polydimethylsiloxane (PDMS) as base material. It allows (1) the fast manufacturing of sensor batches with reproducible geometry, (2) includes a fabrication step for embedding rigid electrical contacts on the sensors, and (3) is designed to produce sensor batches in which the size, the number, and the position of the sensors can be adapted to the patient’s anatomy. In the paper, the process repeatability and the robustness of the design are successfully proven. After 1000 large-strain elongation cycles, in the form of accelerated testing caused much higher strains than in the above-mentioned clinical scenario, the sensor’s electrical contacts remained in place and the functionalities were unaltered. Moreover, the prototype of a patient customizable patch, embedding multiple sensors, was produced.

The vigilant monitoring of climate change, especially through advanced environmental sensing and the deployment of distributed sensor systems, establishes a crucial link to the well-being of forest ecosystems. To ensure long term deployment and maintenance-free operation, such sensor systems need to be energy-autonomous, for example, be powered by harvested solar energy. However, given the complexity of the data processing required at the sensor node, the incoming harvested energy is limited with respect to the energy demands. Additionally, the size and the weight of the solar cells is further reduced from the need to have compact sensor systems, which do not interfere with the ecological processes. Under these circumstances, the electronics are forced to consume as low energy as possible and thus, selecting an ultra-low-power microcontroller, as the WSN’s central building block, that effectively balances minimal energy consumption and computational capabilities becomes critical to ensure reliable, autonomous operation. However, existing literature lacks practical methodologies for selecting ultra-low-power microcontrollers based on realistic, task-specific workloads. To address this gap, we introduce a novel task-specific profiling methodology tailored specifically for evaluating ultra-low-power microcontrollers in environmental WSNs. Two representative microcontrollers—the Ambiq Apollo 3 (ARM Cortex M4F-based) and Texas Instruments’ MSP430FR5994—were rigorously assessed in sensor data acquisition, intensive mathematical computations, memory operations, and LoRa-based wireless communication. Unlike conventional datasheet-based evaluations, our method offers empirical, workload-specific insights and enables the derivation of additional metrics such as energy per CPU cycle, which developers can leverage to make application-aware optimization decisions. Our results show clear trade-offs: MSP430FR5994 excels in integrated memory tasks, while Apollo 3 significantly outperforms in sensing and wireless communication, reducing total active-mode energy consumption by around 19.84%. Moreover, Apollo 3 achieved approximately 62% lower deep-sleep energy consumption when paired with an external ultra-low-power real-time clock (RTC). Overall, this study not only identifies the Apollo 3 as the optimal choice for our specific forest-based environmental monitoring case study but also introduces a platform-agnostic profiling framework. As this methodology quantifies energy per task and even energy per CPU cycle, it can be readily applied to diverse deployment contexts, from urban air quality monitoring to agricultural applications, and can be applied to a wide range of microcontroller architectures, including ARM Cortex, MSP430, and emerging RISC-V platforms. However, while the methodology is broadly applicable, it is not intended as a universal design methodology. Instead, it serves as a practical decision-support framework that complements system-level optimization strategies and enables developers to make energy-aware design choices in resource-constrained environments.

The leaf area index (LAI) is a key parameter in the context of monitoring the development of tree crowns and plants in general. As parameters such as carbon assimilation, environmental stress on carbon, and the water fluxes within tree canopies are correlated to the leaves surface, this parameter is essential for understanding and modeling ecological processes. However, its continuous monitoring using manual state-of-the-art measurement instruments is still challenging. To address this challenge, we present an innovative sensor concept to obtain the LAI based on the cheap and easy to integrate multi-channel spectral sensor AS7341. Additionally, we present a method for processing and filtering the gathered data, which enables very high accuracy measurements with an nRMSE of only 0.098, compared to the manually-operated state-of-the-art instrument LAI-2200C (LiCor). The sensor that is embedded on a sensor node has been tested in long-term experiments, proving its suitability for continuous deployment over an entire season. It permits the estimation of both the plant area index (PAI) and leaf area index (LAI) and provides the first wireless system that obtains the LAI solely powered by solar cells. Its energy autonomy and wireless connectivity make it suitable for a massive deployment over large areas and at different levels of the tree crown. It may be upgraded to allow the parallel measurement of photosynthetic active radiation (PAR) and light quality, relevant parameters for monitoring processes within tree canopies.

Stretchable printed circuit boards (S-PCBs) offer unique advantages over rigid PCBs such as enabling conformability to changing environments and ergonomic designs with increased lifetime under dynamic loads. This study introduces an innovative fabrication method and a cost-effective solution for rapid prototyping S-PCBs using commercially available materials. By utilizing silicone substrates with different levels of stiffness and structured copper sheets for electrical connections, the S-PCBs feature ‘stiff islands’ embedded in a flexible base material. This fabrication method helps alleviate mechanical strain on strain-sensitive components while allowing local deformation of the S-PCB. The proposed fabrication method does therefore enable to integrate surface mount device components into S-PCBs and facilitates complex circuit designs while maintaining stretchability and fatigue resistance. Through material characterization, video strain analysis, as well as quasi-static and cyclic loading tests, this article demonstrates the efficacy of our approach. Based on the experimental results, we provide insights into failure modes and suggest design principles to further enhance the durability of S-PCBs fabricated with our method. We then conclude by presenting a soft wearable S-PCB demonstrator. The S-PCBs fabricated with this method withstood mechanical strains up to 100% and cyclic loads with 30% strain up to 625 cycles. The results are very promising for applications in soft robotics, wearable devices, and soft sensors and actuators. Overall, our study offers a comprehensive toolkit for fast S-PCB prototyping, paving the way for advancements in stretchable electronics with a high degree of complexity and stretchability.