The rapid expansion of the Internet of Things enables seamless collaboration among connected devices, making indoor localization a critical component of industrial automation. However, conventional localization techniques struggle in dynamic environments. These methods depend on extensive, frequent data collection and environment-specific calibration, limiting scalability, interoperability, and effective use of prior research while imposing significant energy penalties that hinder practical deployment. To address these challenges, we propose an energy-efficient transfer learning (TL) based approach for visible light communication based indoor localization. Our method leverages TL to tackle environmental variability, including lighting fluctuations and physical obstacles that typically degrade localization performance, while simultaneously reducing computational overhead and energy consumption compared to conventional approaches. We also introduce a novel model efficiency (ME) metric, designed to integrate localization accuracy, energy efficiency, data efficiency, and transfer gain into a single evaluative measure for comprehensive optimization. Evaluations on a real-world dataset collected from a BOSCH factory demonstrate that our model achieves a 47% improvement in localization accuracy compared to a conventional model that does not utilize TL, and achieves up to 67.5% ME with TL models, compared to only 10.4% in conventional models under high-noise conditions. These results underscore the potential of our approach to deliver a highly efficient and scalable indoor localization system suitable for energy-constrained industrial applications.

Indoor localization systems are critical for Industry-4.0 applications.Visible Light Communication (VLC)-based systems offer advantages such as immunity to electromagnetic interference, high accuracy, and energy efficiency, but their performance is hindered by environmental variability across deployment areas. This paper presents a transfer learning (TL) framework for VLC-based indoor localization that enhances cross-area robustness while reducing data requirements. Using real-world measurements from four distinct areas of a Bosch manufacturing facility, we show that fine-tuning a deep neural network (DNN) pre-trained on source area data with only 40% of target-area measurements recovers 97% of full-data performance, thereby reducing the amount of target data needed. We further introduce the Transfer Efficiency Score (TES), a composite metric that identifies the most effective source models for transfer without exhaustive evaluation. Guided by TES, the framework selects the best source model, yielding localization errors as low as 40.9 cm with success rates above 86%. Validation on an unseen area confirms its generalization capability, with a 3.28 cm mean error achieved using the TES-selected source model. Overall, the proposed framework provides a scalable and data-efficient pathway for industrial VLC localization, substantially reducing deployment costs and computational burden while maintaining high accuracy in diverse and dynamic environments.

Visible Light Positioning (VLP) has emerged as a promising solution for accurate indoor positioning by leveraging the ubiquitous LED infrastructure. While camera-based VLP systems offer the advantage of high-resolution imaging and cost efficiency, their performance is significantly degraded when the receiver is tilted in random directions—a common occurrence in mobile or robotic applications. Such orientation-induced distortions violate the assumptions of conventional geometric models, leading to substantial positioning errors. To solve this limitation, we propose a novel VLP framework based on ResNet50 enhanced with a multi-head attention mechanism, designed to improve robustness against receiver tilt. The model directly learns the mapping between distorted image features and 3D coordinates, eliminating the need for additional sensors or calibration procedures. Evaluations conducted in a real-world indoor environment (2.6 m × 2.6 m × 2.2 m) with tilt angles up to ±30° demonstrate that our method achieves a positioning accuracy within 2.5 cm and superior performance compared to existing algorithms.

As the deployment of optical wireless communication (OWC) technology continues to expand, the demand for robust and reliable device identification methods has become crucial. This paper presents an enhanced thermal-resistant fingerprint model tailored for device identification within OWC systems. By conducting an in-depth analysis of traditional physic-based fingerprints, a temperature-independent fingerprint feature model is proposed to address the distortion of temperature fluctuations on fingerprints. Through extensive simulations and experimental validation, it is demonstrated that the proposed model significantly enhances identification performance, achieving higher accuracy compared to existing physics-based fingerprints. The results indicate the model’s applicability in practical OWC scenarios, underscoring its potential to improve security and efficiency in optical communication.

Accurate indoor localization is crucial in industrial environments. Visible Light Communication (VLC) has emerged as a promising solution, offering high accuracy, energy efficiency, and minimal electromagnetic interference. However, VLC-based indoor localization faces challenges due to environmental variability, such as lighting fluctuations and obstacles. To address these challenges, we propose a Transfer Learning (TL)-based approach for VLC-based indoor localization. Using real-world data collected at a BOSCH factory, the TL framework integrates a deep neural network (DNN) to improve localization accuracy by 47 %, reduce energy consumption by 32 %, and decrease computational time by 40 % compared to the conventional models. The proposed solution is highly adaptable under varying environmental conditions and achieves similar accuracy with only 30 % of the dataset, making it a cost-efficient and scalable option for industrial applications in Industry 4.0.

Location information serves as the fundamental element for numerous Internet of Things (IoT) applications. Traditional indoor localization techniques often produce significant errors and raise privacy concerns due to centralized data collection. In response, Machine Learning (ML) techniques offer promising solutions by capturing indoor environment variations. However, they typically require central data aggregation, leading to privacy, bandwidth, and server reliability issues. To overcome these challenges, in this paper, we propose a Federated Learning (FL)-based approach for dynamic indoor localization using a Deep Neural Network (DNN) model. Experimental results show that FL has the nearby performance to Centralized Model (CL) while keeping the data privacy, bandwidth efficiency and server reliability. This research demonstrates that our proposed FL approach provides a viable solution for privacy-enhanced indoor localization, paving the way for advancements in secure and efficient indoor localization systems.

In recent years, visible light positioning (VLP) techniques have been gaining popularity in research. Among them, the scheme of using a camera as a receiver is more popular, and the technology provides low-cost, high-precision positioning capability and easy integration with existing multimedia devices and robots. However, receiver pose changes can lead to image distortion and light source displacement, significantly increasing positioning errors. Addressing these errors is crucial for enhancing the accuracy of VLP technology. Most current solutions rely on gyroscopes or Inertial Measurement Units (IMUs) for error optimization, but these approaches often add complexity and cost to the system. To overcome these limitations, we propose a positioning algorithm based on an attention mechanism convolutional neural network (CNN), aimed at reducing the errors caused by angles. We designed experiments and comparisons within a rotation angle range of ±15 degrees. The results demonstrate that algorithm maintains an average positioning error within 5 cm.

The rapid evolution of next-generation communi¬cation technology has underscored the critical challenge of spectrum resource scarcity. Visible light communication (VLC) is widely recognized as a promising solution to tackle this issue. Benefits from the rich spectrum resources and no electromagnetic interference radiation, VLC can be widely used in various indoor communication scenarios. However, the accessing of the existing network for the VLC system still remains a challenge. Thus, an optical fiber-based VLC structure is proposed to unlock traditional network cable transmission for the first time. Specifically, an optoelectronic media converter is designed to efficiently connect the fiber signal and the desired electrical signal. Moreover, an analog equalizer with low implementation complexity is exploited to extend the LED bandwidth, ensuring transmitted signals without distortion. It is worth noting that the VLC receiver adopts the auto gain control amplifier to meet the signal voltage requirement of the Ethernet physical layer. Experimental testbed is conducted and corresponding results demonstrated that the transmission rate of the proposed VLC system can reach 100Mbps with sufficiently low bit error rate. Therefore, this validates the capability of the proposed system to directly connect the VLC access point with the existing optical network facilities.

Camera pose estimation is a fundamental module for many vision tasks. It is usually based on feature correspondences, i.e., feature matches across different images. However, correspondences always contain non-negligible outliers, which may negatively affect pose estimation efficiency and accuracy. This paper proposes a multi-camera pose estimation method by leveraging point and line correspondences with non-negligible outliers, in which a weighted M-Estimate Sample Consensus (w-MSAC) based on the customized weights and the coarse pose prior is introduced to improve the efficiency and accuracy of pose estimation. The customized weights could decrease the iterations of the pose hypothesis and improve the pose estimation accuracy. The coarse pose prior is used to perform the pre-validation of the pose hypothesis, eliminating many unnecessary validations. Experiments demonstrate the superiority of the proposed w-MSAC1over existing state-of-the-art methods, e.g., improving 22% positioning and 24% orientation accuracy meanwhile decreasing 15% iterations and 92% validations than the MSAC.

Visible light positioning (VLP) has advantages over traditional indoor positioning techniques in terms of high positioning accuracy and low cost. However, in resource-constrained VLP systems, especially devices deployed on Internet-of-Things networks, both the bandwidth of the light-emitting diode (LED)-based transmitter and the sampling rate of the photodetector (PD)-based receiver are limited. In turn, this significantly limits the positioning accuracy. To address this issue and accommodate the scenario of low bandwidth and hardware cost, a novel positioning scheme with low bandwidth requirements and high-precision pulse reconstruction is proposed in the paper. Specifically, a new beacon signal is designed based on the on-off keying pulse pairs to remove the synchronization requirement and save bandwidth costs. Then, based on the maximum a posteriori probability criterion, a low sampling rate positioning scheme is exploited to estimate the location from the pulse pairs received by the PD. Moreover, an experimental test-bed is constructed to verify the effectiveness and feasibility of the proposed positioning scheme. Experimental results demonstrate that the proposed scheme achieves a positioning accuracy of 1.7 cm by using the reconstructed 2 GHz sampling rate in the case of a bandwidth of 50 MHz and a real sampling rate of 100 MHz. The positioning accuracy achieved by the proposed scheme remains within 30 cm, even under the few MHz of inherent LED bandwidth.

Visible light positioning (VLP) as a promising technology can provide high-precision indoor positioning services without electromagnetic interference, especially in multiple light-emitting diode (LED) scenarios. However, both high synchronization control cost and the multi-path interference are inevitable, which limits the performance gain of the VLP systems. In order to solve this problem, a differential phase of arrival (DPOA)-based positioning scheme is proposed in this paper to remove the reliance on time synchronization and alleviate the multi-path interference. Specifically, a dual-frequency orthogonal beacon is designed and the DPOA-based positioning algorithm is exploited to extract the differential phase information. Then, the positioning optimization problem is formulated by considering both positioning error and location constraints. Thus, the semi-definite programming (SDP) optimization method is exploited to obtain position. The simulation results demonstrate that the positioning accuracy achieved by the proposed scheme outperforms that obtained by traditional received signal strength and the time difference of arrival-based schemes, 90% positioning errors of the proposed method remain within 1.3 cm. Moreover, an experimental VLP test-bed is conducted to verify the feasibility and effectiveness of the proposed positioning scheme. The experimental results show that the average positioning error is 7.12 cm, which meets the centimetre-level positioning requirements.

Response to industrial 4.0, massive devices are supposed to be equipped to collect and process a large amount of data. The high-dynamic environment is therefore created because of changes and moves of various equipments. In addition, indoor visible-light localization has attracted wide attention because of the popularity of the light-emitting diode (LED). However, most researches focus on the visible-light localization in the static environment. In this paper, we propose a high resolution visible-light localization approach in the dynamic industrial environment. This approach is based on the Particle Swarm Optimization (PSO) algorithm because it can reduce the influence of changes in a dynamic environment, but, it still be influenced. Thus, the PSO localization further supported by a neural network (NN) to achieve high and robust localization accuracy. Simulations are conducted to verify that the proposed approach has less sensitivity to environment's changes and three times coverage with localization less than 0.1 m compared to the conventional PSO algorithm.

The bound of the information transmission rate of direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) for visible light communication (VLC) with finite-alphabet inputs is yet unknown, where the corresponding spectral efficiency (SE) and energy efficiency (EE) stems out as the open research problems. In this paper, we derive the exact achievable rate of the DCO-OFDM system with finite-alphabet inputs for the first time. Furthermore, we investigate SE maximization problems of the DCO-OFDM system subject to both electrical and optical power constraints. By exploiting the relationship between the mutual information and the minimum mean-squared error, we propose a multi-level mercury-water-filling power allocation scheme to achieve the maximum SE. Moreover, the EE maximization problems of the DCO-OFDM system are studied, and the Dinkelbach-type power allocation scheme is developed for the maximum EE. Numerical results verify the effectiveness of the proposed theories and power allocation schemes.

—Visible Light Positioning (VLP) is considered to be one of the most promising candidates for future Location Based Service (LBSs). The traditional Received Signal Strength Indication (RSSI) based VLP system is highly sensitized with receiver’s orientation. However, the assumption of the receiver’s orientation fixed or perfectly known is not realistic in practice. Thus, a random angle between receiver and the horizontal plane inevitably appears among localization, which extremely affects positioning results. This paper proposed an Environment-Aware RSSI based positioning algorithm for VLP system. It enables to mainly eliminate random angle interference without the help of extra equipment. The positioning error caused by random angle was theoretically analyzed and validated by numerical simulation. Moreover, a demonstration on a real 5G New Radio (NR) signal platform was implemented to verify the feasibility of our algorithm. According to the results, the average positioning error declined by 73.28%.

In future networks, with the advent of massive machine type communications (mMTC), physical layer security is becoming a significant research area in the fifth generation (5G) and beyond 5G (B5G) communication systems. Device fingerprinting is a technology widely viewed to enhance the security of radio frequency (RF) based wireless systems. Meanwhile, visible light communication (VLC) is developing rapidly due to its remarkably high throughput in indoor situations and its security advantages for both privacy and health. In this paper, a VLC device fingerprint extraction and identification method are presented to improve the security of Visible Light Communication (VLC) in the 5G network. This method based on the fingerprint of Light Emitting Diodes (LEDs) has been investigated theoretically and verified experimentally. Moreover, a laboratory demonstration showed that the fingerprints of five identical white LEDs could be extracted and identified successfully. The best identification accuracy was up to 98.8%.

The Visible Light Indoor Positioning System (VLIPS) has been a popular research area recently. In VL-IPS, many localization methods have been proposed by leveraging the Received Signal Strength (RSS) based trilateration. However, the traditional RSS based trilateration localization (RSS-TL) method is very sensitive to the lighting environment and would results in a big localization error due to the presence of nonline-of-sight (NLOS) light signal. In light of this, we propose a novel NLOS-aware localization algorithm, namely Enhanced Fingerprinting-aided RSS-TL (EFP-RSS-TL). It permits to improve the localization accuracy for the corner regions of a room by eliminating the NLOS impact while keeping the same high accuracy for the room center. This is achieved by leveraging a RSS fingerprint database which records the line-of-sight (LOS) light power ratios beforehand. For validation purpose, we built a real VL-IPS platform and implemented the proposed algorithm. Experimental results show that the proposed NLOS-aware EFPRSS-TL algorithm enables to reduce significantly the average positioning error (by up to 79%) compared to its counterparts. Besides, our proposal cuts the database size by 50% and is more robust to environment changes. In a room of 4.7 mx2.7m, the achieved average positioning error is around 6 cm when it is vacant and it is no more than 14.5 cm when it is occupied by several people.

Visible light communication (VLC) using the light emitting diode (LED) will become an appealing alternative to the radio frequency communication technology for indoor wireless broadband access. However, VLC needs a ubiquitous network as its backbone to avoid becoming an information isolated island. Power line communication (PLC) systems could easily solve the informative problem of VLC while powering the LED lamps at the same time, which is considered as a good partner of VLC for the cost-effective implementation. In this paper, a novel and cost-effective framework of ubiquitous indoor broadband access based on deeply integrated VLC and PLC technology with only low-cost modification to the current infrastructure is therefore proposed. The broadband access network supports duplex transmission through each LED using the decode-and-forward (DF) working mode. This paper will present our recent research progress in this area, including a prototyping of duplex voice communications network based on hybrid PLC and VLC in our lab. Our research and development plan in this area for the near future will also be covered.