Wi-Fi Indoor Positioning Research Articles

The State of the Art of Deep Learning-Based Wi-Fi Indoor Positioning: A Review

The State of the Art of Deep Learning-Based Wi-Fi Indoor Positioning: A Review

FedPos: A Federated Transfer Learning Framework for CSI-Based Wi-Fi Indoor Positioning

This article proposes FedPos, a federated transfer learning framework together with a novel position estimation method for Wi-Fi indoor positioning. Compared with traditional machine learning with privacy leakage problems and the cloud model trained through federated learning (FL) fails in personalization, the FedPos framework aggregates nonclassification layer parameters of models trained from different environments to build a robust and versatile encoder on the cloud server while preserving user privacy. The global cloud encoder can aggregate different classifiers and then construct personalized models for new users through fine-tuning. The proposed framework can be updated incrementally and is highly extensible. Specifically, we exploit channel state information (CSI) as the positioning feature and assess the transferability of a lightweight convolutional neural network (CNN) in unfamiliar environments. We evaluate the performance of our proposed framework and position estimation method in different indoor environments. Our experimental results indicate that the proposed framework can achieve a mean localization error of 42.18 cm in a 64-position living room. They also confirm that FedPos can achieve a 5.22% average localization performance boost and reduce the average model training time by about 34.78% when compared with normal training. By reusing part of the feature extractor layers that are trained from other environments, at least 65% of training data can be saved to achieve a localization performance that is similar to the base model. Overall, the proposed position estimation method can effectively improve localization accuracy as compared with seven other existing CSI-based methods.

Developing A Conceptual Passive Contact Tracing System for Commercial Buildings Using WiFi Indoor Positioning

Contact tracing is one of the critical tools for fighting against pandemic disease outbreaks, such as the fast-growing SARS-CoV-2 virus and its different variants. At present, automated contact tracing systems face two main challenges: (1) requiring application installation on smart devices and (2) protecting the users’ privacy. This study introduces a conceptual passive contact tracing system using indoor WiFi positioning to address these challenges and investigate the role of such a system in commercial buildings. In this regard, this study uses a simulated small-office layout in a case study to demonstrate the applicability of the proposed system. The special use of the proposed contact tracing system could be academic facilities and office buildings, where (1) the WiFi infrastructure already exists and therefore implementing such a system could be cost-effective, and (2) the same users use the facility regularly, enabling the system to notify the users upon a confirmed case once they are back in the building and connected to the WiFi system. Such technology can not only enhance the current automated contact tracing system in commercial buildings by illuminating the need to use smartphone applications while protecting users’ privacy, but could also reduce the risk of infection in indoor environments. The developed system can benefit facility managers, business owners, policy makers, and authorities in assisting to find occupants’ high-risk contacts and control the spread of SARS-CoV-2 or similar infectious diseases in commercial buildings, particularly university campuses and office buildings.

Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories.

In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.

Reliable Wi-Fi Indoor Localization in Case of AP Loss by Using Integrated Model Based on Signal Anomaly Detector and Signal Distance Corrector

When developing a Wi-Fi indoor positioning system in a real-world environment, the problems we have to face are that some access points’ signal strength fluctuates extensively or even loses contact due to the cybersecurity threats, leading to the fact that the indoor location system cannot get reliable application in a real-world environment. To solve this problem, we propose a new integrated model based on signal anomaly detector and signal distance corrector to provide reliable position estimation when the access points’ signal is lost under cybersecurity threats. The signal anomaly detector improves recognition capability of the uncertain signal and noise, while the signal distance corrector improves the robustness and fault tolerance of the highly variable Wi-Fi signals. To fully reflect the performance of the proposed method, experiments have been carried out in the real environment of indoor parking lots. The results show that the proposed integrated model successfully provides reliable position estimation when the access points are lost under cybersecurity threats.

An Automatized Contextual Marketing System Based on a Wi-Fi Indoor Positioning System.

A complete contextual marketing platform including an indoor positioning system (IPS) for smartphones is proposed and evaluated to later be deployed in large infrastructures, such as malls. To this end, we design and implement a novel methodology based on location-as-a-service (LAAS), comprising all the required phases of IPS generation: mall digital map creation, the tools/procedures for offline calibration fingerprint acquisition, the location algorithm, the smartphone app acquiring the fingerprint data, and a validation procedure. To select an appropriate fingerprint location algorithm, a comparison among K-nearest neighbors (KNN), support vector machine (SVM), and Freeloc is accomplished by employing a set of different smartphones in two malls and assessing different occupancy levels. We demonstrate that our solution can be quickly deployed at shop level accuracy in any new location, resulting in a robust and scalable proposal.

Indoor Wi-Fi Positioning Algorithm Based on Location Fingerprint

Currently most of the existing indoor fingerprint positioning algorithms are based on fingerprint database. The accuracy of the fingerprint database will directly affect the final positioning accuracy. Therefore, through the research of fingerprint data, a method based on skewness-kurtosis normality test and Kalman filter fusion is proposed. In the training phase, the RSSI (Received Signal Strength Indication) samples received on each fingerprint point are tested based on the skewness-kurtosis normality. If the normal distribution model is met, the normal distribution function is used to estimate the probability density of the samples. If not the kernel function will be used. And then the value of the large probability density is taken for Kalman filtering, and finally, the averaged value after filtering is used to establish a high-precision fingerprint database. In the online positioning stage, the weighted KNN (K-Nearest Neighbor) is used to estimate the position, and finally, the positioning point is corrected by the fusion of the Levenberg-Marquardt method and the Kalman filter. The optimization of the three stages can improve the positioning accuracy. The simulation results show that the indoor positioning method proposed in this paper has the least number of iterations and the positioning accuracy is improved by 60% compared with the traditional Kalman filtering method.

Fine‐grained CSI fingerprinting for indoor localisation using convolutional neural network

As an important positioning source of indoor positioning technology, Wi-Fi signals have attracted the attention of researchers for a long time. Fingerprint positioning can solve the problems caused by non-line-of-sight propagation and multipath effects. To improve the accuracy of Wi-Fi indoor positioning, this study proposes an indoor positioning algorithm based on fine-grained channel state information (CSI) and convolutional neural network (CNN). CSI is a kind of observable measurement that better describes the nature of Wi-Fi signal propagation than received signal strength indication. This method uses the subcarrier amplitude and phase difference information extracted from CSI data to establish fingerprints. The clustering method is used to analyse the number of clusters of fingerprint data, and the fingerprint database is divided into two sub-databases according to the threshold. CNNs with the same network structure are used to train the two kinds of fingerprint sub-databases. In the positioning stage, the sub-database to which the data to be measured belongs is determined according to the calibration algorithm, and the corresponding CNN model is used to estimate the position. Experiments are performed in a typical indoor environment. Compared with existing fingerprint-based positioning methods, this method has higher positioning accuracy.

A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning

Fingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, in the offline phase of fingerprinting positioning, the construction and maintenance of a Received Signal Strength (RSS) fingerprint database yield high labor. Moreover, the sparsity and stability of RSS fingerprinting datasets can be the most dominant error sources. This work proposes a minimally Semi-simulated RSS Fingerprinting (SS-RSS) method to generate and maintain dense fingerprints from real spatially coarse RSS acquisitions. This work simulates dense fingerprints exploring the cosine similarity of the directions to Wi-Fi access points (APs), rather than only using either a log-distance path-loss model, a quadratic polynomial fitting, or a spatial interpolation method. Real-world experiment results indicate that the semi-simulated method performs better than the coarse fingerprints and close to the real dense fingerprints. Particularly, the model of RSS measurements, the ratio of the simulated fingerprints to all fingerprints, and a two dimensions (2D) spatial distribution have been analyzed. The average positioning accuracy achieves up to 1.01 m with 66.6% of the semi-simulation ratio. The SS-RSS method offers a solution for coarse fingerprint-based positioning to perform a fine resolution without a time-consuming site-survey.

Wi-Fi RTT Ranging Performance Characterization and Positioning System Design

The aim of this research is to implement a precise Wi-Fi indoor positioning system (IPS) or localization system based upon the IEEE 802.11mc fine-timing measurement (FTM) scheme also known as the Wi-Fi round trip time (RTT) ranging technique, where ranging refers to a sub-process of positioning that determines the distance between a transmitter and receiver. Our system and its algorithms were implemented using a COTS (Commercial-Off-The-Shelf) smartphone and Wi-Fi access points. Experiments were conducted in several real-life indoor environments. This paper presents the detailed Wi-Fi RTT ranging performance of these devices in different system configurations and characterizes the systematic biases and noise model to improve the ranging accuracy. A novel three-step-positioning method is proposed to overcome the issues of no or multiple intersect points in trilateration due to ranging errors to improve positioning accuracy. This consists of the following: 1) systematic bias determination and removal; 2) clustering-based trilateration (CbT) supported by weighted concentric circle generation (WCCG), namely CbT & WCCG; 3) positioning result and trajectory optimization using a Kalman filter. As a result, the evaluation experiments gave a position accuracy of ±1.2 m in 2D static positioning and ±1.3 m for dynamic motion tracking. Also, our CbT & WCCG method demonstrate good tolerance against ranging errors. Moreover, the computational cost and positioning accuracy of CbT & WCCG methods are compared with least square (LS) and recursive least square (RLS) methods and the accuracy standard deviation of our algorithm is the closest to the Cramer–Rao bound (CRB).

A Self-Adaptive AP Selection Algorithm Based on Multiobjective Optimization for Indoor WiFi Positioning

With the widely deployed wireless access points (APs) and the worldwide popularization of smartphones, WiFi-based indoor positioning has attracted great attention to both industry and academia. Locating and tracking objects within an indoor environment plays an important role in Internet of Things application and service. However, it is a challenging problem to achieve high accuracy using WiFi positioning technique due to the high instability in received signal strength from AP. Thus, it is desirable to select APs by considering both signal strength and connection quality. In this article, an AP selection algorithm based on multiobjective optimization is proposed to improve indoor WiFi positioning accuracy. The self-adaptive AP selection algorithm can be easily applied to various real scenarios and the performance of the new method is considerably better than classical algorithms. Learning algorithm is exploited to obtain the optimal solution for the self-adaptive AP selection algorithm. Experiments are conducted and the proposed algorithm is compared with classical algorithms. The experimental results demonstrate that the performance of the self-adaptive AP selection algorithm is at least a few decimeters better than classical algorithms in terms of RMSE of position estimation. Meanwhile, the new method is robust to the random generation of initial particles and normalizing factor as their effect on the positional accuracy is less than 1 decimeter.

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Wi-Fi-based indoor positioning for determining accurate wireless indoor location information has become crucial in meeting increasing demands for location-based services by leveraging the Internet of Things (IoT) and ubiquitous connectivity. Most Wi-Fi-based indoor positioning techniques using wireless received signal strength (RSS)-based methods are affected by the indoor environment and depend on the respective signals from at least three reference access points. In this paper, we propose a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation through a Wi-Fi located on a one-hop wireless network. Our cloudlet-based cloud computing system provides the reference point data and real-time interactive response for a self-driving indoor cart. The system was tested in a real environment with the following results: (1) our system autonomously performed actions, such as turning right or left or going straight according to a movement decision algorithm and determined the position within a stable range of Wi-Fi coverage; (2) the cloudlet and core cloud can track navigation for an indoor self-driving cart; (3) the global and local positions designed for reference access points and a specific position can navigate the self-driving cart to a particular position accurately; (4) the moving edge clouds play a role in deciding three action movements (go straight, turn left, and turn right), as well as managing the local position of the items; and (5) a core cloud is deployed to store all information for the items, such as their positions and corresponding Wi-Fi locations. A core cloud manages items that have the same position (i.e., a global position) defined as the corresponding Wi-Fi location. Finally, the practical results have significance in designing a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation.

AP Optimization for Wi-Fi Indoor Positioning-Based on RSS Feature Fuzzy Mapping and Clustering

In indoor environments, Access Points (APs) are widely deployed in various locations of buildings, and thereby the AP optimization-based Wi-Fi indoor positioning technology is of great significance for achieving the satisfactory indoor Location-based Services (LBSs). However, the current Wi-Fi indoor positioning methods rarely pay attention to the diversity of Received Signal Strength (RSS) features for AP optimization, which may result in the low positioning accuracy and high positioning overhead. In order to deal with such issues, this article proposes a new concept of multi-dimensional RSS feature fuzzy mapping and clustering for AP optimization in Wi-Fi indoor positioning. Besides, the extensive experiments conducted in an actual indoor environment show that compared with the existing positioning methods, the proposed method can not only achieve higher positioning accuracy by using the optimized APs but also reduce the positioning overhead in the online phase.

A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning

The uncertainty of indoor Wi-Fi positioning is susceptible to many factors, such as sensor distribution, the internal environment (e.g., of a shopping mall), differences between receivers, and the flow of people. In this paper, an indoor pedestrian trajectory pattern mining approach for the assessment of the error and accuracy of indoor Wi-Fi positioning is proposed. First, the stay points of the customer were extracted from the pedestrian trajectories based on the spatiotemporal staying patterns of the customers in a shopping mall. Second, the drift points were distinguished from the stay points through analysis of noncustomer behavior patterns. Finally, the drift points were presented to calculate the errors in the pedestrian trajectories for the accuracy assessment of the indoor Wi-Fi positioning system. A one-month indoor pedestrian trajectories dataset from the Xinxiang Baolong shopping mall in Henan Province, China, was used for the assessment of the error and accuracy values with the proposed approach. The experimental results were verified by incorporating the distribution of the AP sensors. The proposed approach using big data pattern mining can explore the error distribution of indoor positioning systems, which can provide strong support for improving indoor positioning accuracy in the future.

Modeling and predicting the occupancy in a China hub airport terminal using Wi-Fi data

The research on building energy consumption of airport terminal is of great importance, especially considering the rapid development of civil aviation in China. One of the key elements influencing the building energy consumption is the occupancy. This paper proposes an occupancy model based on dwell time distribution and applies it to the data set acquired through the Wi-Fi indoor positioning system. Firstly, this paper briefly describes the passenger flow in a Chinese hub airport terminal in 66 days. On this basis, the distribution of passengers’ dwell time, averaging at 96.97 min but varies with time, airlines or delay, is extracted and modeled through a Bayesian method. Furthermore, the occupancy is effectively predicted in high spatial-temporal resolution based on the flight schedule and the model before, reaching a 0.747 r-square. The new data source and mathematical model will be instructive in future occupancy research, and the prediction model will benefit the optimization of the operational strategy of terminal buildings.

An Indoor Positioning Method Based on RSSI Probability Distribution

In view of the influence of the time-variation of RSSI on the positioning accuracy in Wi-Fi indoor positioning, this paper proposes to use the probability distribution of RSSI value as a fingerprint feature over a period of time, and combines the dimension reduction algorithm and the weighted K nearest neighbor algorithm to achieve positioning. The method firstly calculates the probability distribution of the received RSSI value, uses the dimensionality reduction algorithm to reduce the dimension of the statistical probability distribution.The K-reference points with the smallest Euclidean distance were combined with the weighted nearest neighbor algorithm to obtain the positioning results. Through simulation experiments, it is shown that the positioning accuracy is higher than the traditional method, and the positioning time is significantly reduced.

Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm

The fingerprint method has been widely adopted for Wi-Fi indoor positioning. In the fingerprint matching process, user poses and user body shadowing have serious impact on the received signal strength (RSS) data and degrade matching accuracy; however, this impact has not attracted large attention. In this study, we systematically investigate the impact of user poses and user body shadowing on the collected RSS data and propose a new method called the pose recognition-assisted support vector machine algorithm (PRASVM). It fully exploits the characteristics of different user poses and improves the support vector machine (SVM) positioning performance by introducing a pose recognition procedure. This proposed method firstly establishes a fingerprint database with RSS and sensor data corresponding to different poses in the offline phase, and fingerprints of different poses in the database are extracted to train reference point (RP) classifiers of different poses and a pose classifier using an SVM algorithm. Secondly, in the online phase, the poses of RSS data measured online are recognised by a pose classifier, and RSS data measured online are grouped with different poses. Then online RSS data from each group at an unknown user location are reclassified as corresponding RPs by the RP classifiers of the corresponding poses. Finally, user location is determined by grouped RSS data corresponding to coordinates of the RPs. By considering the user pose and user body shadowing, the observed RSS data matches the fingerprint database better, and the classification accuracy of grouped online RSS data is remarkably improved. To verify performances of the proposed method, experiments are carried out: one in an office setting, and the other in a lecture hall. The experimental results show that the positioning accuracies of the proposed PRASVM algorithm outperform the conventional weighted k-nearest neighbour (WKNN) algorithm by 52.29% and 40.89%, outperform the SVM algorithm by 73.74% and 60.45%, and outperform the pose recognition-assisted WKNN algorithm by 34.76% and 21.86%, respectively. As a result, the PRASVM algorithm noticeably improves positioning accuracy.

Trilateration Approaches for Indoor Wi-Fi Positioning

In smartphones several sensors and receivers are embedded which enable positioning in Location-based Services and other navigation applications. They include GNSS receivers and Wireless Fidelity (Wi-Fi) cards as well as inertial sensors, such as accelerometers, gyroscope and magnetometer. In this paper, indoor Wi-Fi positioning is studied based on trilateration. Three methods are investigated which are a resection, a calculation of the center of gravity point and a differential approach. The first approach is a commonly employed resection using the ranges to the Wi-Fi Access Points (APs) as radii and intersect the circles around the APs. In the second method, the center of gravity in a triangle of APs is calculated with weighting of the received signal strength (RSS) of the Wi-Fi signals. The third approach is developed by analogy to Differential GNSS (DGNSS) and therefore termed Differential Wi-Fi (DWi-Fi). Its advantage is that a real-time modeling of the temporal RSS variations and fluctuations is possible. For that purpose, reference stations realized by low-cost Raspberry Pi units are deployed which serve at the same time as APs. The experiments conducted in a laboratory and entrance of an office building showed that position deviations from the ground truth of around 2 m are achievable with the second and third method. Thereby the positioning accuracies depend mainly on the geometrical point location in the triangle of APs and reference stations and the RSS scan duration.

A Novel Clustering Algorithm for Wi-Fi Indoor Positioning

In recent years, the Wi-Fi-based indoor positioning technology has become a research hotspot. This technology mainly locates the indoor Wi-Fi based on the received signal strength indicator (RSSI) signals. The most popular Wi-Fi positioning algorithm is the k-nearest neighbors (KNN) algorithm. Due to the excessive amount of RSSI data, clustering algorithms are generally adopted to classify the data before KNN positioning. However, the traditional clustering algorithms cannot maintain data integrity after the classification. To solve the problem, this paper puts forward an improved public c-means (IPC) clustering algorithm with high accuracy in indoor environment, and uses the algorithm to optimize the fingerprint database. After being trained in the database, all fingerprint points were divided into several classes. Then, the range of each class was determined by comparing the cluster centers. To optimize the clustering effect, the points in the border area between two classes were allocated to these classes simultaneously, pushing up the positioning accuracy in this area. The experimental results show that the IPC clustering algorithm achieved better accuracy with lighter computing load than FCM clustering and k-means clustering, and could be coupled with KNN or FS-KNN to achieve good positioning effect.

Sensor Fusion for Recognition of Activities of Daily Living

Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.