Visible light positioning (VLP) is one of the most promising technologies for providing high-precision, low-cost indoor positioning and navigation services. However, the traditional calibration methods of VLP are complex and unfriendly to users, which hinders the large-scale commercial deployment of VLP systems. In this paper, a fast and robust calibration method for received signal strength (RSS)-based VLP system is proposed, which dispenses with geometric measurement and greatly simplifies the calibration procedures. The proposed method calibrates the Lambert model through two steps, firstly using a ratio method to calibrate the Lambert order, and then estimating the constant term. The actual processes only require moving a robot equipped with a photo-detector (PD) along a rectangular trajectory once, and then the program will automatically estimate the required parameters by analyzing the RSS during this period. Experimental results show a good stability of the calibrated parameters, as well as a excellent distance measuring accuracy within 12 cm. The proposed method is 3.7 times more efficient than traditional methods while the positioning accuracy is close, which will greatly reduce the time and labor costs of large-scale deployment.

Visible Light Positioning (VLP) has emerged as a promising technique in the Internet of Things landscape and gained increasing attention worldwide due to its widely existing infrastructure, high precision, and cost-effectiveness. Recently, ratio and difference-based VLP systems have been used to reduce errors from environmental noise, ambient light, and device differences. However, there may be intricate interference patterns that simple ratios and differences struggle to address. Moreover, a single LED often has limited capability to achieve self-diagnosis and self-correction. In fact, the information from other LEDs can be used to refine the signal and suppress interference. Thus, we propose to organize the VLP system in a graph and use the Graph Neural Network to model the interrelationships among LED lamps. This allows us to optimize the signals and further efficiently suppress interferences by simultaneously considering multiple LED lamps. In addition, the precisions of LEDs’ measurements is different due to various factors (e.g., distances and powers), and low-precision measurements may reduce the performance of the VLP system. To address this issue, we incorporate an attention layer to allow our model to give higher weights to high-precision measurements. Finally, the long short-term memory network is used to model the temporal dependencies between adjacent positions in a trajectory. Taking these modules together, we develop a robust VLP system called DeepVLP. The comprehensive experiments demonstrate that DeepVLP achieves better performance than state-of-the-art methods.

Visible light positioning (VLP), characterized by high accuracy, low power consumption, cost effectiveness, and eco-friendliness, has garnered substantial attention in Internet of Things (IoT) positioning applications. However, the commonly used received signal strength (RSS)-based multianchor methods exhibit limitations in addressing certain challenging scenarios, such as tilted receivers, nonuniform light intensity, and narrow spaces, while incurring additional expenses for wiring and controllers. To tackle these challenges, we propose a novel VLP system utilizing a custom-designed light-emitting diode (LED) array. Initially, we conduct an in-depth analysis of the array’s size, layout, and specialized LED design, referencing prevalent household lamps to ensure that the devised array fulfills both illumination and positioning functions. Subsequently, a phase-difference-based ranging and localization method is introduced to supplement the array. By assuming equidistance among the specialized LEDs, a base distance is established for global distance calculations. Our proposed method not only eliminates the need for model calibration in RSS-based approaches but also excels in addressing challenging situations that RSS-based methods fail to resolve. Furthermore, we incorporate the Cramer–Rao lower bound and the geometric dilution of precision to assess the theoretical performance and efficacy of the proposed method. Simulation experiments are performed to evaluate the proposed array and method, taking into account various error sources, such as time synchronization errors and receiver noise. Results demonstrate that our proposed method’s average localization error is less than 10 cm under various influences and diverse scenarios, which can fulfill a wide range of IoT applications’ requirements. Moreover, it holds the potential to replace commonly used household lamps.

Visible Light Positioning (VLP), a promising indoor positioning technique, has gained wide popularity worldwide because of its ubiquitous infrastructure, low power consumption, and high positioning precision. However, VLP systems based on photodiodes (PDs) often suffer from varying ambient light with time and space, which seriously degrades their positioning precision and robustness. In this article, we carefully evaluate the influence of the ambient light on the VLP system, which includes the reduction of positioning accuracy by varying ambient light with time and the inaccurate parameter calibration by unevenly distributed ambient light. Then, we figure out that the influence of ambient light on the Received Signals Strength (RSS) values is determined by the ambient light intensity and PD, which is independent of external factors, including distance, frequency, LED, etc. Next, we propose a new positioning framework, RatioVLP, where a ratio model that is more robust to varying ambient light with time is used. However, the ratio model is severely dependent on the Lambert parameters that are vulnerable to ambient light, which reduces the framework's precision when the calibration area is unevenly covered by ambient light. Thus, we design new parameters that are less sensitive to ambient light, calledR parameter, to connect the RSS ratio and its corresponding distance ratio, which can strengthen the ratio model's robustness and effectively reduce the influence of ambient light on the parameter calibration process. Experimental results show that the positioning precision of the proposed method is improved by more than 50 % when compared to the conventional Lambert model in scenes influenced by ambient light.

The SPiForest, a new isolation-based approach to outlier detection, constructs iTrees on the space containing all attributes by probability density-based inverse sampling. Most existing iForest (iF)-based approaches can precisely and quickly detect outliers scattering around one or more normal clusters. However, the performance of these methods seriously decreases when facing outliers whose nature "few and different" disappears in subspace (e.g., anomalies surrounded by normal samples). To solve this problem, SPiForest is proposed, which is different from existing approaches. First, SPiForest uses the principal component analysis (PCA) to find principal components and estimate each component's probability density function (pdf). Second, SPiForest utilizes the inv-pdf, which is inversely proportional to the pdf estimated from the given dataset, to generate support points in the space containing all attributes. Third, the hyperplane decided by these support points is used to isolate the outliers in the space. Next, these steps are repeated to build an iTree. Finally, many iTrees construct a forest for outlier detection. SPiForest provides two benefits: 1) it isolates outliers with fewer hyperplanes, which significantly improves the accuracy and 2) it effectively detects the outliers whose nature "few and different" disappears in subspace. Comparative analyses and experiments show that the SPiForest achieves a significant improvement in terms of area under the curve (AUC) when compared with the state-of-the-art methods. Specifically, our method improves by at most 17.7% on AUC when compared to iF-based algorithms.

As a critical topic of Internet of Things applications, smartphone-based indoor navigation has a rapidly growing need in various applications. However, indoor navigation technology is unreliable when facing a challenge in complex indoor environments. This article presents a tightly coupled (TC) integration of pedestrian dead reckoning (PDR) and Bluetooth for indoor pedestrian navigation and enhances it from three approaches. We first establish a Gaussian-based distance model (GDM) that improves the signal path-loss model to incorporate the prior information on the variation of signal volatility with distance. Then, the use of map information and a back-off strategy to optimize the particle transfer strategy further improves the positioning accuracy and rationality of the system. Moreover, we leverage behavioral landmarks, signal landmarks, and distance information to build a graph optimization model to optimize the proposed navigator. We have extensively verified the proposed navigator and compared it with the existing solutions and systems. Experimental results demonstrated that the average errors of the proposed solutions in three scenes were 34.71% of Bluetooth, 14.04% of PDR, 45.13% of the extended Kalman filter, 57.83% of the unscented Kalman filter, and 56.10% of PF, respectively. The results showed that our proposed solution has apparent advantages, especially when addressing the issues of incorrect trajectory updating and divergence of the system in a complex environment.

Snap-fit peg-in-hole assembly widely exists in both industry and daily life, especially for consumer electronics. The buckle mechanism leads to a damping zone inside the port where insertion force needs to be increased. It is much difficult to automate this process by robots, for size and clearance of the components are always small, and the damping buckle should be perceived and distinguished from solid inner walls of the port. End-effector position control might be invalid, since grasping error will make it difficult to locate the plug accurately. In this article, we undertake this assembly challenge by taking advantage of fingertip tactile perception combined with visual images and force feedback. Raw sensor data is collected, processed, and fused together to be state input of a reinforcement learning network, generating continuous action vectors. We also propose a novel damping zone predictor through feature extraction and multimodal fusion, which is able to identify whether the plug has touched the buckle mechanism, so as to adjust the insertion force. The whole framework is implemented through a common USB Type-C insertion experiment on Franka Panda robot platform, reaching a success rate of 88%. Furthermore, system robustness is verified, and comparisons of different modalities are also conducted.

Predicting and utilizing the evolution trend of hot topics is critical for contingency management and decision-making purposes of government bodies and enterprises. This paper proposes a model named online opinion dynamics (OODs) where any node in a social network has its unique confidence threshold and influence radius. The nodes in the OOD are mainly affected by their neighbors and are also randomly influenced by unfamiliar nodes. In the traditional opinion model, however, each node is affected by all other nodes, including its friends. Furthermore, many traditional opinion evolution approaches are reviewed to see if all nodes (participants) can eventually reach a consensus. On the contrary, OOD is more focused on such details as concluding the overall trend of events and evaluating the support level of each participant through numerical simulation. Experiments show that OOD is superior to the improvement of the original Hegselmann-Krause (HK) model, HK-13 and HK-17, with respect to qualitative predictions of the evolution trend of an event. The quantitative predictions of the HK model cannot be used to make decisions, whereas the results of the OOD model are proved to be acceptable.