The need for long-term and reliable power sources for wireless implantable electronic devices (IEDs) has driven research into rechargeable solutions. This paper explores an optical wireless charging method based on near-infrared (NIR) light, focusing on its potential to safely, privately, and securely extend the lifetime of IEDs under an aligned scenario. Experiments were conducted using a single-beam 810 nm NIR LED with two illumination modes across a 40 mm-thick soft tissue-mimicking optical phantom: 1) continuous-wave (CW) with high irradiance (full power of the NIR LED) and a safer irradiance level based on ICNIRP and ANSI standards, and 2) pulsed-wave (PW) at 20 kHz with 25% and 75% duty cycle. A commercial tiny photovoltaic (PV) cell was used to represent the energy harvester within the system. Key measurements included the open-circuit output voltage of the PV cell (vOC), wireless charging duration (tcharge), and energy harvesting (EH) rate. Higher irradiance in CW mode increased both vOC and the EH rate, thereby accelerating supercapacitor charging, whereas lower irradiance and PW operation reduced these metrics but may be preferable for long-term reliability. Temporary increases in irradiance may be necessary during critical energy drops/deficits. The impact of supercapacitor capacity was assessed, showing that higher capacity results in longer tcharge. A longevity test was conducted to assess the practical applicability of the proposed method for energizing ultra-low-power IEDs. In addition, performance was evaluated under a clothing-covered scenario, demonstrating that the presence of clothing, representative of real-world conditions, can significantly affect wireless charging performance. These results confirm the feasibility of optical wireless power transfer (OWPT) through biological tissue, supporting its potential for powering ultra-low-power IEDs.

Batteryless Light-based Internet of Things (LIoT) sensing enhances sustainability by removing the need for batteries and their maintenance, but it imposes constraints on update scheduling due to harvested energy, optical channel quality, and the heterogeneous nature of different environmental parameters. This paper proposes an Age-of-Information-Aware Duty-cycle Scheduler (AoIADS) algorithm for batteryless multi-sensor LIoT nodes for indoor environmental monitoring. The system considers a Visible Light Communication (VLC)-based indoor channel and an illuminance-to-current energy-harvesting model supporting heterogeneous sensing processes, including air quality, temperature, humidity, and air pressure. Sensor nodes are clustered by location using the k-means clustering. At each decision epoch, the gateway selects the eligible node with the highest harvested energy, determines which sensor readings meet the Age of Information (AoI) threshold, and computes the sleep interval based on harvested energy, payload size, and intracluster constraints.We evaluated the proposed scheduler over 100 Monte Carlo runs in an indoor environment containing nine LED access points and 36 randomly deployed batteryless nodes. The proposed AoIADS scheduler achieved the highest mean bounded threshold-tracking score, 79.22%, compared to the baseline AoI-aware and pure energy-aware schedulers with 41.12% and 34.49%, respectively. These results indicate that clustered AoI-aware scheduling can improve the freshness–energy trade-off in batteryless LIoT networks under the considered simulation assumptions. Under varying energy-harvesting conditions, the considered scheduling approach increases duty-cycle frequency per node, yielding higher AoI threshold-tracking scores. Overall, clustered scheduling that integrates AoI awareness with energy and channel conditions yields improved freshness–efficiency trade-offs in batteryless LIoT networks.

Dual Connectivity (DC)-enabled Optical Wireless Communication and Radio Frequency Heterogeneous Networks (OWC/RF HetNets) offer a promising solution for achieving high data throughput, low latency, and energy-efficient wireless communication. However, the integration of fundamentally distinct technologies introduces significant challenges in coordinating handover management and resource allocation, especially under dynamic network conditions and user mobility. This paper formulates the joint handover and resource allocation problem in DC-enabled OWC/RF HetNets as a multi-objective optimization framework, explicitly modeling the trade-offs between transmission power, handover cost, and throughput across both technologies. We employ goal programming to unify the objectives, enabling balanced trade-offs among objectives. To address the limitations of conventional Reinforcement Learning (RL) methods, which rely on weighted summation to integrate multiple objectives into a single reward function, we propose a novel framework, termed Multi-Criteria Double Deep QLearning (MC-DDQL). The framework employs parallel deep Q-networks to learn policies for each reward function independently. It integrates the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) together with Boltzmann annealing to achieve effective multi-criteria action selection. Simulation results demonstrate that the proposed MC-DDQL framework addresses the challenge of objective weight selection, particularly in dynamic environments. The framework achieves accelerated convergence and superior performance relative to state-of-the-art benchmarks, while maintaining a performance profile comparable to the optimal solution.

This study tackles the handover challenge in optical wireless communication (OWC)/radio frequency (RF) heterogeneous networks (HetNets) by introducing two complementary and novel approaches. First, we present OPHO, an optimization framework formulated as a multi-objective mixed-integer linear programming (MILP) model. OPHO simultaneously minimizes the number of active access points (APs), node-to-AP distances, and handover costs, while accommodating various handover types. To balance conflicting objectives, goal programming is employed. Although OPHO provides a theoretically optimal benchmark, its centralized architecture and computational complexity hinder scalability and adaptability in dynamic environments. To address these limitations, we propose MAHO, a decentralized multi-attribute decision-making (MADM) scheme that combines a modified Kalman filter with fuzzy TOPSIS. Unlike conventional MADM-based methods, MAHO incorporates multiple predicted states—generated via the modified Kalman filter—as distinct decision attributes. This predictive multi-horizon modeling embeds forward-looking network conditions directly into the decision-making process. Furthermore, fuzzy TOPSIS serves as the decision-making engine, effectively handling uncertainty in both current and predicted metrics. Simulation results show that while OPHO achieves optimal handover performance, MAHO delivers near-optimal results with significantly lower computational overhead, making it highly suitable for real-time deployment in dynamic and resource-constrained OWC/RF HetNets.

This hardware paper introduces an experimental testbed for in-body optical wireless communication (OWC) studies. The conventional version often relies on bulky optical benches and costly supporting equipment, which are often cost-prohibitive for many research institutions. The proposed testbed featured a small footprint, lightweight, a vertically aligned optical path (with fixed optical component placement), and ambient light shielding. It can be printed using commercial 3D printing, reducing costs compared to conventional optical benches. The 3D-printable testbed consists of a box-like chassis that securely positions a near-infrared (NIR) LED TX at the top and a photodetector RX at the bottom, with a tissue sample (e.g., ex-vivo porcine tissue or a tissue-mimicking phantom) held firmly in between. All design files, including CAD and STL formats, along with detailed assembly instructions, are made openly available. The inherent design structure enables faster alignment, and the shields can effectively protect against exposure to indoor ambient light (e.g., typical laboratory lighting), thereby improving experimental reliability. The modular nature of the testbed allows for easy customization to accommodate sensors of different wavelengths and different tissue models. The proposed testbed offers practical benefits and an accessible solution for researchers conducting in-body OWC studies, especially when access to high-end optical equipment is limited.

Most current implantable electronic devices (IEDs) depend on finite-lifetime batteries whose depletion frequently requires surgical replacement. Near-infrared (NIR)-based optical wireless power transfer (OWPT) exploiting the "optical window" offers a promising method for wirelessly recharging IEDs. Photovoltaic (PV) cells are typically employed at the OWPT's receiver end and can produce direct DC output at high power density. Most silicon-based PV cells, however, are optimized for the broad-spectrum AM1.5 solar reference rather than for narrowband NIR illumination. A broadband NIR LED is a controlled light source whose emission spans a wider portion of the NIR spectrum, which is expected to improve the PV cell's spectral response and thereby enhance OWPT performance. This study aims to assess the performance of an OWPT on a tissue-mimicking optical phantom using broadband versus narrowband NIR LED sources (810 nm and 850 nm) for IEDs in two scenarios. First, each NIR LED source was calibrated to its wavelength-specific safety-limited irradiance, i.e., similar to 418 mW/cm(2), 332 mW/cm(2), and 399 mW/cm(2) for broadband (at 860 nm reference), 810 nm, and 850 nm, respectively. Second, the irradiance of each NIR LED is matched (similar to 250 mW/cm(2)) to isolate the spectral coupling factor. The experiments were performed using a vertical 3D-printed black-box testbed that housed the mounted NIR LED transmitter, a 40 mm-thick soft-tissue-mimicking optical phantom, and a commercial silicon-based PV energy harvester, all aligned consistently. Specifically, we repurposed small, rigid, non-implantable PV cells optimized for the solar spectrum in our system for test purposes. Performance was evaluated based on supercapacitor charging duration and energy-harvesting rate (the stored energy in the supercapacitor divided by the charging duration). In the experiment, a 0.16 F-rated supercapacitor was charged from 3 V to 4 V under the control of a power management integrated circuit. Although broadband NIR illumination was expected to improve spectral coupling to the silicon-based PV receiver, the optical-phantom results show that distributing optical power across the 770, 860, and 940 nm emission bands did not improve end-to-end wireless-charging performance compared with a well-selected narrowband source.

Wireless connectivity is required in modern in-body electronic devices (IEDs), such as in-body sensors. However, their frequent wireless communication operations will consume more energy than those of conventional IEDs. In addition, energy-limited IEDs require concurrent data and power transfer to maintain uninterrupted operation, enabling medical data communication while continuously supplying energy for battery recharging In this paper, we present a joint optical wireless data and power transfer system for IEDs based on single-carrier transmission, focusing on realistic operating scenarios, with particular emphasis on forward telemetry and energy-harvesting performance. . Commercially available components were employed, including a single-beam 850 nm NIR LED that simultaneously delivers modulated data and optical energy through biological tissue (i.e., ex vivo porcine tissue samples, a representative biological model for human soft-tissue optical propagation). A photodetector receives forward telemetry signals, and a photovoltaic (PV) cell harvests residual optical power concurrently to charge a supercapacitor via a power management integrated circuit (PMIC). In this study, we also account for a real-life factor, namely the impact of clothing on optical light transmission, which may attenuate the incident light depending on fabric type and thickness. Two textile samples were used in this study, representing a thin fabric with high porosity and a thicker fabric with lower porosity (dense yarn). Energy can be harvested through biological tissue during active optical data transmission, thereby providing supplementary energy support for battery-limited IEDs. However, the presence of cloth led to a noticeable decrease in the harvested energy, even with a thin layer. The slower supercapacitor charging is attributed to attenuation of incident optical power by clothing, thereby reducing the PV cell's output voltage. The study is important for developing future wearable-to-implant links for non-invasive medical applications that account for the presence of clothing.

In any optical link, including an in-body link, strict alignment is required for optimal performance. However, in practice, misalignment may occur for many clinical reasons, which changes the relative geometry between the external source and the implantable medical device (IMD). This paper focuses on the impact of misalignment on NIR LED transmitter-based optical wireless power and data transmission (OWPDT) across biological tissue. We employed a testbed comprising an NIR LED transmitter and an IMD receiver system. The IMD receiver system includes a photodetector for data reception, a photovoltaic (PV) cell for energy-harvesting (EH), and a power-management IC that charges a supercapacitor. A 40-mm-thick tissue-mimicking optical phantom was used as the propagation medium. The NIR LED transmitter was initially set at baseline position, where the NIR LED axis was at the PV cell/photodetector boundary across the optical phantom. This study evaluates the EH rate metric under OWPT and OWPDT scenarios, while the communication link remains stable during OWPDT operation. We then laterally shifted the NIR LED by approximately 15 mm toward the center of the photodetector and evaluated the EH rate performance of OWPT and OWPDT scenarios relative to baseline placement. Results show that the EH rate decreases due to lateral transmitter misalignment. The study suggests that the placement of the transmitter (e.g., a wearable device with an NIR LED patch) relative to the IMDs should be carefully considered for system deployment. In this paper, we use the term misalignment to refer to lateral transmitter displacement, including the slightly shifted condition.

The rapid emergence of Internet of Things (IoT) networks has intensified the demand for sustainable and maintenance-free solutions enabled by energy harvesting (EH). Light-based IoT (LIoT), which exploits indoor illumination for both optical communication and power, offers a promising pathway for zero-energy IoT (ZE-IoT) designs. However, in ZE-LIoT networks, non-uniform illumination and dynamic channel conditions create regions of energy surplus and deficit, while limited energy storage (ES) capacity prevents surplus nodes from harvesting additional energy, resulting in inefficient utilization of available resources. To address these challenges, this paper proposes a data-energy networking-enabled LIoT (DE-LIoT) architecture for indoor wireless personal area networks (WPANs), in which densely distributed, battery-free nodes are coordinated by a central controller. Within this framework, data exchange is performed via optical wireless communication (OWC), while surplus energy is intermittently redistributed through optical wireless power transfer (OWPT). The feasibility of intermittent OWPT is validated through a DE-LIoT prototype implementation, which demonstrates a 50% increase in average illumination at resource-deficient nodes and extends their runtime from 8 h to over 48 h. These results establish DE-LIoT as a promising approach to enhance LIoT applications and pave the way for large-scale, sustainable, and zero-energy IoT deployments, thereby meeting the stringent requirements of 6G IoT networks in terms of energy efficiency, scalability, and environmental sustainability.

Abstract Most of implantable electronic devices (IEDs) today are powered by non-rechargeable batteries, which have limited lifespans and require frequent replacement once depleted. Wireless power transfer (WPT) is a promising solution for efficiently recharging the batteries of IEDs; therefore, the vision of WPT is to reduce the need for frequent surgical battery replacements. The optical WPT (OWPT) method using near-infrared (NIR) light for wireless charging represents an attractive solution, relatively new field that warrants exploration. OWPT offers safe, secure, and private wireless recharging through biological tissue. NIR light is the best option among other wavelengths for carrying optical energy across biological tissue due to its relatively low absorption and scattering effects when propagating in this medium. Photovoltaic (PV) cells can be used at the receiver end as an energy harvester. However, the PV cell is typically optimized for wide-spectra source (e.g., sunlight or artificial lights), which can lead to inefficiencies when paired with narrow-spectra sources (e.g., NIR light) due to spectral mismatches. For this reason, using a wide-spectrum (broadband) NIR light source is envisioned to enhance energy conversion efficiency. This study investigates OWPT across biological tissues using a broadband NIR LED source. We employed tissue-mimicking phantoms to simulate the optical properties of biological tissues (i.e., human soft tissue) closely . This study investigates the use of tiny monocrystalline silicon PV cells to harvest energy from broadband NIR light penetrating phantoms. Key parameters evaluated include received optical power, open-circuit voltage ( V O C ), the output power of the PV cells, and power conversion efficiency (PCE) of the OWPT system. We also investigate wireless charging performances across various scenarios, including the impact of transmitted optical power on safety considerations, duty cycles, misalignments, and the presence of clothing layers on energy harvesting rates stored in a supercapacitor. The results demonstrate the feasibility of wireless charging for IEDs under broadband NIR LED illumination in realistic conditions, including clothing obstruction and misalignment issues.

Predictive maintenance (PdM) is central to Industry 5.0 strategies for reducing unplanned downtime in rotating machinery. This work proposes and evaluates, as a proof of concept on a controlled single-machine testbed, a multimodal TinyML edge architecture for PdM designed to remain compatible across the application plane’s evolution toward sixth-generation (6G) networks. Three complementary modalities run local inference on commercial off-the-shelf smart sensor nodes—vibration, acoustic, and thermography—with an embedded gateway bridging per-modality decisions to a serverless cloud back-end. Using real vibration data from a controlled static-unbalance protocol, five anomaly-detection model variants, operating on ten frequency-independent time-domain features extracted from 6 s windows, are benchmarked on the actual Cortex-M4F target; the INT8-quantized fully connected autoencoder, scored by per-window reconstruction error, reaches F1 = 0.9807 with 254 µs inference latency and a 6056 B Flash footprint, well within the microcontroller budget. In a second acquisition session with the remounted sensor, the frozen model retains perfect fault recall, and a short per-installation healthy-baseline recalibration restores F1 = 0.975 without any weight retraining. The acoustic modality is classified in-sensor on log-Mel filterbank energies by the Syntiant NDP120 neural coprocessor, and the thermographic modality by a lightweight binary CNN on 96 × 96 px frames. A preliminary intra-session late-fusion analysis suggests that a logistic-regression meta-learner over the three modality confidence scores can improve on single-modality baselines when no single modality already saturates, motivating multimodal sensing primarily for robustness and redundancy. An end-to-end latency experiment shows that the cloud-uplink leg dominates the budget (79–88%), establishing edge-first inference as a necessary condition for 6G URLLC gains to be observable at the application level. All experiments are conducted over Wi-Fi and MQTT with no 5G or 6G radio, so 6G compatibility is presented as a forward-looking roadmap rather than a tested capability.

Intelligent Internet of Medical Things (IIoMT) systems rely on pervasive wearable and implantable sensors to enable continuous monitoring, timely clinical decision support, and increasingly closed-loop care. Wireless Body Area Networks (WBANs) are a core enabler of these systems, yet conventional RF-only solutions face spectrum congestion, electromagnetic interference, and an expanding security/privacy attack surface, particularly in dense clinical environments and safety-critical use cases. Hybrid Optical-Radio WBANs (HyWBANs) address these limitations by combining radio links with optical wireless communication to provide context-aware connectivity that can trade off throughput, robustness, energy, and confidentiality depending on the scenario. This article surveys HyWBANs from an IIoMT perspective and proposes a reference layered architecture featuring a decision layer that supports intelligent reconfiguration across sensing and communication functions. We discuss enabling technologies, key components (sensors, hybrid hubs, and access points), and principal challenges including sustainability, energy harvesting, resilience to blockage/interference, and end-to-end trust. Finally, we outline research directions toward deployable HyWBANs for next-generation IIoMT, including edge intelligence, adaptive multimodal policies, and interoperable architectures for smart healthcare environments.

Situation awareness has become increasingly important for smart environments over the past two decades. Traditional approaches often rely on external sensors or user devices, which can limit scalability and raise privacy concerns. This paper presents a novel method that leverages existing white light-emitting diode (LED) lighting infrastructure to enable optical sensing and scene understanding without the use of cameras. The work highlights the emerging convergence of illumination and data communication, positioning solid-state lighting as a platform for both light delivery and information exchange. We introduce a conceptual framework and research roadmap for developing a low-resolution, privacy-preserving, and scalable sensing system that reuses lighting infrastructure for dual functions of illumination and sensing. The proposed approach offers potential societal benefits in domains such as healthcare, industrial monitoring, and smart automation, while ensuring privacy and cost-effectiveness. The paper also outlines key research questions, methodologies, and future directions for practical implementation.

This letter investigates the feasibility of using differential chaos-based modulation in visible light communication (VLC) systems. Taking into account both the complexity and the lack of consideration for illumination of previously studied schemes, a novel modulation scheme called optically-fitted differential chaos shift keying (Of-DCSK) is proposed to improve the bandwidth efficiency of VLC systems. The proposed Of-DCSK scheme utilizes flexible information duration to represent the information bits "0" and "1", rather than using traditional positive and negative value for information bits, which is proved to be feasible and advantageous in terms of improving system performance, i.e., average system optical power, bandwidth efficiency, and bit error rate (BER). The aforementioned three performance metrics are derived with mathematical expressions in line-of-sight (LOS) environments, and later compared with simulated results. The numerical results reveals that (i) the Of-DCSK scheme outperforms the traditional DCSK scheme in VLC communications, and (ii) the novel scheme provides a promising bandwidth-efficient modulation solution for VLC systems, where parameters a (visible light flicker and dimming) and beta (spreading factor) can be used to adjust flickering and dimming ranges using chaos signal length as well as spread factor.

6G must be designed to withstand, adapt to, and evolve amid prolonged, complex disruptions. Mobile networks' shift from efficiency-first to sustainability-aware has motivated this white paper to assert that resilience is a primary design goal, alongside sustainability and efficiency, encompassing technology, architecture, and economics. We promote resilience by analysing dependencies between mobile networks and other critical systems, such as energy, transport, and emergency services, and illustrate how cascading failures spread through infrastructures. We formalise resilience using the 3R framework: reliability, robustness, resilience. Subsequently, we translate this into measurable capabilities: graceful degradation, situational awareness, rapid reconfiguration, and learning-driven improvement and recovery. Architecturally, we promote edge-native and locality-aware designs, open interfaces, and programmability to enable islanded operations, fallback modes, and multi-layer diversity (radio, compute, energy, timing). Key enablers include AI-native control loops with verifiable behaviour, zero-trust security rooted in hardware and supply-chain integrity, and networking techniques that prioritise critical traffic, time-sensitive flows, and inter-domain coordination. Resilience also has a techno-economic aspect: open platforms and high-quality complementors generate ecosystem externalities that enhance resilience while opening new markets. We identify nine business-model groups and several patterns aligned with the 3R objectives, and we outline governance and standardisation. This white paper serves as an initial step and catalyst for 6G resilience. It aims to inspire researchers, professionals, government officials, and the public, providing them with the essential components to understand and shape the development of 6G resilience.

This research compares and contrasts 5G and 6G networks in the context of communication systems for connected vehicles. Simulation-based tests were used to evaluate key performance metrics, including quality of service (QoS), throughput, latency, packet loss, signal strength, and handover success rate. The results show that 6G networks consistently outperform 5G across these areas, indicating substantial improvements in connected vehicle communications. These results highlight the transformative potential of 6G technology for future transportation systems, enabling faster, more reliable, and efficient communication between vehicles and infrastructure. This comparative analysis aims to inform the development of V2X communication technologies and support progress toward safer and more efficient transportation systems.

Ultra-wideband (UWB) and narrowband (NB) technologies have been widely used for in-body communication systems. In recent years, there has been a growing interest among researchers in optical wireless communication (OWC) as an alternative technology for in-body communication; this trend has emerged as a response to the limitations and challenges found in UWB and NB communication technologies. Conducting an in-body OWC study using an ex-vivo approach should consider several key steps. Initially, a standardized test-bed must be prepared; this involves developing it step-by-step with commercial off-the-shelf components (COTS), followed by a thorough characterization/assesment of its performance. This data-in-brief paper provides a set of measurement data obtained from a developed test-bed for in-body OWC research based on phantom and ex-vivo samples. The methodology used for data collection and the significance of the measurements are explained. The test-bed employed two receiver ( R x ) devices representing an in-body device, namely 1) a photodetector module and 2) an optical sensor connected to an optical power meter console, where both devices are created by the same company (i.e., Thorlabs). The data includes the results of three measurement scenarios, namely 1) free-space channel (baseline case), 2) tissue-mimicking optical phantoms, and 3) biological tissue channels based on ex-vivo samples of fresh pork meat of different compositions. The uniqueness of this test-bed lies in its use of a photodetector module to serve as an optical power meter and then comparing the result to the optical power meter readings under three measurement scenarios (i.e., free-space, phantoms, and biological tissue samples). In addition to its primary role of converting information signals in the optical domain into the electrical domain, the photodetector module can be used indirectly to measure optical power by using the equations outlined in the datasheet, extracting the output voltage ( V o u t ) to determine the relative optical power. The dataset presents the impact of varying incident power of near infra-red (NIR) LED, achieved through adjustments in LED current using the LED driver module, on the received optical power measured by an optical power meter and photodetector module in a separate measurement. The influence of the photodetector's gain setting on the received optical power read by a photodetector module is also investigated. From the top-level perspective, the developed test-bed confirms its feasibility in demonstrating in-body OWC systems. From the specific point-of-view, data obtained in this paper suggests two main findings: first, changing the photodetector's gain can increase the V o u t , but it does not affect the measured optical power based on the calculation. Gain adjustment can serve to increase the scale of V o u t reading. Second, the received optical power read by the photodetector module in any gain setting is closely matched with the optical power meter reading set to approximately -4.30 dB. In this sense, multiplication should be considered to align the results of optical power readings between the photodetector module and the optical power meter when using them in the experiment at the free-space and ex-vivo settings, which is around 2.7×. Future use of the provided data is intended for researchers in the biomedical engineering field, particularly those focusing on in-body OWC and dealing with ex-vivo experiments. This dataset paper can facilitate the procedure of developing and testing an in-body OWC system on a laboratory scale using the standardized test-bed, providing inspiration to researchers in this area who wish to use a similar setting with comparable instruments.

The potential of using light as an alternative energy source and a medium for communicating with medical implants is attractive. Light can propagate through biological tissues, and it is well known that maximum penetration takes place with near-infrared (NIR) light. Light has several advantages, making it quite attractive for communication compared to radio frequency (RF) or acoustic waves. Light communications, in particular, are highly secure, safe, and private wireless links while also presenting opportunities for low-power implementation. Moreover, the light-based operation is free from electromagnetic interference. In the context of the wireless power transfer (WPT) method for medical implants, light also has unique advantages compared to RF and acoustics waves, that is, secure wireless charging capabilities. The emission of NIR light carrying the modulated data that passes through the biological tissue can further be converted into electrical energy by photovoltaic (PV) cells, enabling medical implants to be powered by light. In this chapter, a new paradigm in modern implanted devices, that is, joint data transfer and energy harvesting by exploiting light, is described. We present the progress of research (preliminary results) on experiments using a tissue-mimicking phantom emulating the human soft tissue, conducted in our laboratory to prove the concept, showcasing the potential of the data transfer and energy harvesting method by utilizing light. We demonstrate that optical channels are established at a depth of approximately 40 mm across the phantom, allowing for both wireless data and energy transfer with the implanted devices.

As the demand for advanced healthcare services grows, the functionality requirements of In-body electronic devices (IEDs) also increase, e.g., need both reliable telemetry and sustainable energy, which can be supported by optical technology. In particular, an optical approach offers safe, private, and secure joint data and power transmission across biological tissue, while eliminating electromagnetic interference (EMI) from radio frequency (RF) signals. Even though the optical approach is promising and gaining huge interest among researchers, clothing obstruction—a common occurrence in patients’ everyday lives—has not been extensively studied, as most prior work has focused on clothing-free settings. In this paper, we present a forward optical telemetry and wireless power transfer system through biological tissue, considering a realistic scenario in which the patient's skin is covered by fabric, as is typically the case when a patient wears clothing. A 4 cm-thick ex vivo porcine tissue sample was employed to represent biological tissue. Meanwhile, a typical cotton shirt fabric was selected and placed above the tissue sample to serve as an optical obstruction. We used a single thickness (i.e., 1 mm) and a single fabric color (i.e., black). Typically, dark-colored fabrics absorb more light in the visible spectrum than lighter-colored fabrics, and this behavior may also be relevant in the near-infrared (NIR) spectrum. An 850 nm NIR LED transmits data and energy during forward telemetry operation; a photodetector receives data, and a PV cell harvests energy that is stored in a supercapacitor. As expected, the results show that the presence of clothing introduces additional optical attenuation, resulting in lower received optical power, lower PV cell voltage output, and slower supercapacitor charging than in the baseline (no-cloth) scenario under identical operating conditions. Across seven forward telemetry sessions (each session lasting ∼10 min) per condition, the average harvesting energy rate decreases from 28.6 µW (final energy stored: 0.165 J) to 8.54 µW (0.046 J), representing a decrease of ∼72 % and ∼70 % in stored energy and energy harvesting rate, respectively. These findings underscore the importance of accounting for clothing-induced optical loss as a crucial factor in the practical implementation of joint optical data and power transmission for IEDs. We assumed that the power loss caused by clothing can be approximated as a scalar transmittance of the textile (Ttextile) by a first-order linear relation: in the clothing-covered scenario, the received optical power scales with the Ttextile relative to the no-cloth baseline. The approximation holds across several NIR LED drive levels, with a low mean absolute percentage error (MAPE) of approximately 1–2 %. Black coloration was selected in this study as a representative dark-colored material to establish a baseline for observing clothing-induced optical attenuation. This selection provides a reference point for future comparisons involving a wider range of fabric types and colors. Furthermore, the proposed empirical model offers a practical framework for predicting optical link performance under clothing-covered conditions, thereby supporting the design of future optical IED systems that demand greater robustness and efficiency.