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.

Ensuring the provision of sustainable and secure electrical power for ingestible/implantable medical devices (IMDs) is crucial for facilitating the multifaceted capabilities of these IMDs and preventing the need for recurrent battery replacements. Using photovoltaic (PV) energy harvesting in conjunction with an external light source can be advantageous for an optical wireless power transfer (OWPT) system to enable energy self-sufficiency in IMDs. This study investigates the performance of OWPT using commercial monocrystalline silicon PV cells exposed to an 810 nm Near-infrared (NIR) LED light. The ethical concerns are addressed by utilizing porcine samples (ex vivo approach), eliminating the need for live animal experimentation. The experimental setup employs porcine meat samples with several compositions, e.g., pure fat, pure muscle, and different layers of fat-muscle. The primary goal of this initial study is to analyze the open-circuit voltage output (V_OC) of the PV against received optical power in the presence of biological tissue. Our study demonstrates that PV cells can generate voltage even when exposed to light passing through porcine samples with a thickness of up to 30 mm. Furthermore, the V_OC values of PV cells attained in this study meet the required voltage input level for supplying current IMDs, typically ranging from 2V to 3V. The findings of this study provide valuable insights into OWPT systems in the future, where monocrystalline silicon PV cells can be employed as energy harvester devices to supply various IMDs utilizing NIR light.

Advancements in 6G and IoT sensor networks prioritise sustainability and energy efficiency, with positioning services essential for improved functionality. Light-based IoT (LIoT) systems present a promising solution by achieving energy autonomy through Photovoltaic (PV) energy harvesting and enabling Visible Light Communication (VLC) via indoor luminaries as Optical Access Points (OAP). This research explores the repurposing of energy harvesters at LIoT nodes and detectors at OAPs for positioning and orientation detection in energy-autonomous, battery-free, intermittently operating LIoT sensor networks. We propose potential node and OAP designs, validated by proof-of-concept prototypes, utilising conventional Machine Learning (ML) and Deep Neural Networks (DNN) to enhance localisation. Performance evaluations demonstrate 80% positioning accuracy within 12.5 cm tolerance and 68% orientation prediction accuracy. This approach allows LIoT devices to communicate, harvest energy, and determine their position and orientation using the same illumination from OAPs, underscoring the potential of this LIoT-based system as a sustainable solution for next-generation IoT sensors.

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 ($P_{max}$ = 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.

Conventional active product monitoring systems typically rely on battery and Radio Frequency (RF) communication designs, which pose challenges to sustainability and scalability, limiting their effectiveness. To overcome these limitations, this work presents a sustainable smart packaging system based on Light-based Internet of Things (LIoT) architecture, termed LIoT-based Active Smart Packaging (LIoT-ASP). The proposed solution integrates Photovoltaic Energy Harvesting (PV-EH) and OpticalWireless Communication (OWC) to enable battery-free, energy-autonomous operation for product condition monitoring in retail and logistics environments. A prototype system was developed using commercially available components, incorporating both visible light downlink and Infrared (IR) uplink channels based on consumer device IR protocol-inspired OWC, adapted for low-power operation. The bidirectional OWC supports a data rate of 1.1 kbps, sufficient for periodic sensing and display updates in low-throughput smart packaging applications. The system employs energy-aware control mechanisms and intermittent operation strategies to manage sensing and data communication under variable indoor illumination. Performance of the LIoT-ASP concept was assessed through the developed proof-of-concept prototype system, followed by experimental validation in a real-world scenario involving temperature-sensitive dairy products. The system successfully detected and reported threshold violations via cloud-connected mobile alerts, demonstrating reliable end-to-end functionality. These findings validate the practical feasibility of deploying LIoT-ASP systems as a sustainable and scalable solution for smart packaging applications.

The growing demand for Internet of Things (IoT) networks has sparked interest in sustainable, zero-energy designs through Energy Harvesting (EH) to extend the lifespans of IoT sensors. Visible Light Communication (VLC) is particularly promising, integrating signal transmission with optical power harvesting to enable both data exchange and energy transfer in indoor network nodes. VLC indoor channels, however, can be unstable due to their line-of-sight nature and indoor movements. In conventional EH-based IoT networks, maximum Energy Storage (ES) capacity might halt further harvesting or waste excess energy, leading to resource inefficiency. Addressing these issues, this paper proposes a novel VLC-based WPANs concept that enhances both data and energy harvesting efficiency. The architecture employs densely distributed nodes and a central controller for simultaneous data and energy network operation, ensuring efficient energy exchange and resource optimisation. This approach, with centralised control and energy-state-aware nodes, aims for long-term energy autonomy. The feasibility of the Data-Energy Networking-enabled Light-based Internet of Things (DE-LIoT) concept is validated through real hardware implementation, demonstrating its sustainability and practical applicability. Results show significant improvements in the lifetime of resource-limited nodes, confirming the effectiveness of this new data and energy networking model in enhancing sustainability and resource optimisation in VLC-based WPANs.