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, called R 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.

Wireless fingerprint localization technologies have drawn significant attention because of their ubiquitous infrastructure and low energy consumption. However, they often suffer from the Received Signal Strength (RSS) instability (i.e., RSS variations across heterogeneous devices and RSS fluctuations over time) and fingerprint spatial ambiguity issues. Although various deep learning-based methods have been proposed recently, most of them only pursue remarkable accuracy in an ideal environment and escape the above-mentioned problems. The co-existence of these factors would make most existing methods suboptimal or invalid when applied in real scenarios. In this paper, to address the RSS instability, we define the fingerprint spatial gradient (FSG), which can convert the vulnerable and unstable fingerprints to stable relative values but lose absolute information for positioning. Next, to relieve the fingerprint instability and model the temporal sequential dependencies in a trajectory, we propose a Long Short-Term Memory network (LSTM)-based framework to fuse FSG and RSS, where an attention module is used to guide the fusion according to their contributions to the result. Finally, the beam search strategy is used to search for the optimal solution from outputs of the LSTM, eliminating the spatial ambiguity of fingerprints. The experiments show that DeepWiPos reduces average positioning error by 23.44% and 22.18% on Bluetooth and Wi-Fi, respectively when compared with the state-of-the-art.

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.

More and more devices, such as Bluetooth and IEEE 802.15.4 devices forming Wireless Personal Area Networks (WPANs) and IEEE 802.11 devices constituting Wireless Local Area Networks (WLANs), share the 2.4 GHz Industrial, Scientific and Medical (ISM) band in the realm of the Internet of Things (IoT) and Smart Cities. However, the coexistence of these devices could pose a real challenge—co-channel interference that would severely compromise network performances. Although the coexistence issues has been partially discussed elsewhere in some articles, there is no single review that fully summarises and compares recent research outcomes and challenges of IEEE 802.15.4 networks, Bluetooth and WLANs together. In this work, we revisit and provide a comprehensive review on the coexistence and interference mitigation for those three types of networks. We summarize the strengths and weaknesses of the current methodologies, analysis and simulation models in terms of numerous important metrics such as the packet reception ratio, latency, scalability and energy efficiency. We discover that although Bluetooth and IEEE 802.15.4 networks are both WPANs, they show quite different performances in the presence of WLANs. IEEE 802.15.4 networks are adversely impacted by WLANs, whereas WLANs are interfered by Bluetooth. When IEEE 802.15.4 networks and Bluetooth co-locate, they are unlikely to harm each other. Finally, we also discuss the future research trends and challenges especially Deep-Learning and Reinforcement-Learning-based approaches to detecting and mitigating the co-channel interference caused by WPANs and WLANs.

Currently, various indoor positioning technologies are widely studied, and visible light positioning (VLP) is a promising technology due to its high accuracy, low cost, and high output rate. However, the most common method based on the received signal strength (RSS) requires calibrating the model in advance, which has a weak generalization ability. This article focuses on the VLP method based on the time difference of arrival (TDOA), which does not require heavy preparatory work. Firstly, we analyze the influence of different errors on TDOA-based VLP, such as the time synchronization error, receiver noise, etc. Secondly, a convolution neural network (CNN) based network is designed for phase difference estimation, which significantly improves the accuracy of phase difference estimation compared to the traditional in-phase&quadrature signal-based method. Lastly, a particle filter based on the motion state is proposed to improve positioning accuracy and robustness. Simulated experiments evaluate the proposed methods, and the final results show a significant improvement in accuracy when compared with traditional methods. The improvements in ranging and localization accuracy can both reach over 50%.