RESUMEN
The utilization of actual biological tissue (e.g., pork meat samples) and tissue-mimicking phantoms for optical-based in-body data and energy transfer studies is crucial. Near-infrared (NIR) light, a part of the light spectrum that falls between visible light and infrared, is highly advantageous as a carrier for data transmission due to its superior ability to penetrate biological tissue, for instance, the human body. Using pork meat samples as a propagation medium for prolonged experiments is challenging due to the deterioration of meat quality caused by drying in the temperature chamber. Typically, a controlled-temperature chamber can be utilized to warm the tissue samples to 37 °C. Some experiments need to be carried out over long periods, in some cases exceeding one hour, including the demonstration of transmitting large-size data (e.g., high-definition images or videos) in real-time through biological tissue using NIR LED. Moreover, for statistical analysis, some experiments need to be repeated, therefore degradation of the tissue sample should be avoided. Furthermore, experiments may also encompass investigations into optical wireless power transfer (OWPT) conducted on biological tissues under NIR illumination and employing energy harvester-based commercial photovoltaic cells (PV) at the receiving ends, which would require a long time to charge the storage (e.g., battery or supercapacitor) fully. Using phantoms for such an experiment is also not straightforward, requiring careful consideration, such as standardization issues. One possible approach to address this challenge is to conduct experiments in a free-space environment (e.g., sample-free) while guaranteeing that the optical power received in free-space is equivalent to that obtained through biological tissue. This can be achieved by carefully controlling the LED's current and arranging the optical channel's distance to achieve comparable results. The received optical power is the primary parameter for comparing free-space and biological tissue setups. This dataset provides settings for NIR LEDs ( P m a x = 375 mW and λ = 810 nm), allowing in-body communication experiments in a free-space environment. The LED's current settings in this dataset (free-space) are equivalent in comparison to those used in a test-bed using biological tissue with 5 (five) different variations of LED currents (i.e., 500 mA, 400 mA, 300 mA, 200 mA, and 100 mA). The dataset consists of six pork meat samples with different thicknesses and fat-muscle layer compositions, resulting in 36 data points. This dataset holds significant potential for reuse in any biomedical research, particularly in the fields of in-body communication and energy transfer utilizing light.
RESUMEN
The light-based Internet of things (LIoT) concept defines nodes that exploit light to (a) power up their operation by harvesting light energy and (b) provide full-duplex wireless connectivity. In this paper, we explore the LIoT concept by designing, implementing, and evaluating the communication and energy harvesting performance of a LIoT node. The use of components based on printed electronics (PE) technology is adopted in the implementation, supporting the vision of future fully printed LIoT nodes. In fact, we envision that as PE technology develops, energy-autonomous LIoT nodes will be entirely printed, resulting in cost-efficient, flexible and highly sustainable connectivity solutions that can be attached to the surface of virtually any object. However, the use of PE technology poses additional challenges to the task, as the performance of these components is typically considerably poorer than that of conventional components. In the study, printed photovoltaic cells, printed OLEDs (organic light-emitting diodes) as well as printed displays are used in the node implementation. The dual-mode operation of the proposed LIoT node is demonstrated, and its communication performance in downlink and uplink directions is evaluated. In addition, the energy harvesting system's behaviour is studied and evaluated under different illumination scenarios and based on the results, a novel self-operating limitation aware algorithm for LIoT nodes is proposed.