One-to-all regularized logistic regression-based classification for WiFi indoor localization

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

There are two longstanding issues: the fluctuation of wireless signal and the unstability of Access Point (AP), which greatly affect the performance of Wi-Fi based indoor localization. Most existing fingerprint based Wi-Fi localization methods adopt machine learning or data mining algorithms to get the location information; however, they ignore some intrinsic factors. According to massive observations, we discover some underlying characteristics of Wi-Fi indoor localization from the view of signal strength and AP. Hence, a new dissimilarity based localization method SAP (Signal-AP) is proposed to implement high performance indoor localization. The results show that SAP not only improves the localization accuracy, but also has desirable scalability of environment.

Localization is a fundamental operation in wireless networks. Location determination is normally accomplished using the Global Positioning System (GPS) for outdoor applications. For indoor localization, GPS does not work due to the lack of the line of sight to satellites. High-precision indoor localization is critical to many personal and business applications. WiFi-based indoor localization was proposed to be a practical method to locate WiFi-enabled devices due to the popularity of WiFi networks. However, it suffers from large localization errors. Our experimental results indicate that this scheme consistently leads to an average error around 3 meters. The existence of different locations with similar WiFi signal strength is the reason behind the large errors. To improve the localization precision, a hybrid indoor localization scheme, HILL, is proposed in this paper. Inspired by the fact that a large number of WiFi-enabled mobile devices have been deployed, HILL uses 3 phases to improve the precision of WiFi-based localization. First of all, it measures the distances between each pair of peer devices through acoustic ranging. Secondly, the Classical Metric Multidimensional Scaling (MDS) method is applied to the collected distances, which results in a graph consistent with the distances. Finally, the graph generated by MDS is embedded onto the graph corresponding to WiFi-based localization in order to achieve high localization precision. Our experimental results indicate that the average localization error of HILL is about 1 meter.

Wi-Fi based indoor localization has attracted great interest due to its ubiquitous access in many indoor environments. In this paper, we propose ResLoc, a deep residual sharing learning based system for indoor localization with channel state information (CSI) tensor data. We first introduce CSI data in wireless systems and show how to build CSI tensors for indoor localization. Then, we present the design of ResLoc, which employs dual-channel, bi-modal CSI tensor data to train the deep network using the proposed deep residual sharing learning in the offline phase. In the online test phase, we use newly received CSI tensor data to estimate the location of the mobile device based on an enhanced probabilistic method. The experimental results show that the proposed ResLoc system can obtain submeter level accuracy with a single access point.

Compared with the Wi-Fi Received Signal Strength (RSS) commonly-used for the indoor localization, the Channel State Information (CSI) can be used for precise ranging to achieve the high Wi-Fi indoor localization accuracy since it includes the fine-grained physical-layer information such as the amplitude and phase of each subcarrier during the signal transmission. Due to the lack of the theoretical analysis of the localization error bound in the existing CSI ranging-based localization methods, it is not easy to compare the ideal performance of different localization methods. Therefore, this paper proposes a CSI ranging-based Wi-Fi indoor localization error bound analysis method, which is based on the indoor signal propagation model to derive out the CSI ranging-based localization error bound by considering the relationship between the localization accuracy and the path loss, shadow fading, multipath effect and asynchronous effect. Besides, through the experimental comparison, this paper analyzes the difference between the actual localization error and the derived localization error bound, as well as discusses the impact of different experimental parameters on the localization performance.

Due to the advent and pervasive deployment of wireless local area networks, WiFi-based indoor localization systems have received increasing attention in the last few years. However, their localization accuracy has always been a challenging issue. In addition, because of diverse interference such as multipath effects, the block of signals, an unstable or weak signal in itself, etc., not all the access points (APs) are informative for the localization. Faced with these problems, we propose a WiFi-based localization model by modifying the large localization errors and enhancing the Gaussian process regression (MEGPR). 1) To select the AP subsets that contribute more to the localization and further reduce the computational load, the AP discrimination criterion (APDC) is introduced to quantify the discernibility of the APs detected in the workspace and filter out the APs with low discrimination. 2) Second, to enhance the localization model, the localization residual is fed and learnt by the model. 3) Furthermore, the large localization errors are mitigated by the location modification method (LMM). Experiments were conducted in a real environment with an area of more than 1200 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and the results show that compared with other existing localization models, the average localization error of the proposed MEGPR model is minimum, which further verifies the effectiveness of the proposed MEGPR localization model.

Indoor localization technology based on Received Signal Strength (RSS) fingerprint is widely used in life and industry. Compared with the traditional localization methods, the localization technology integrating multiple machine learning methods has better localization accuracy. However, in multi-floor localization, the existing fusion localization technology ignores the interaction between different floors in the localization process, resulting in low indoor localization accuracy. This paper proposes a Wi-Fi indoor 3D localization method based on multi-classifier fusion named FLMCF. Firstly, floor classification training is carried out to reduce the location deviation in the vertical direction. Secondly, for each floor, multiple classifiers are used for model training and training the optimal weight set by minimizing the average localization error. In this case, the advantages of each classifier can be fully integrated to improve the localization accuracy. Finally, the Reliability Fusion Weight Selection (RFWS) algorithm determines the weight and calculates the final location estimation. The experimental results indicate that FLMCF is nearly 14.1% better than DIFMIC in the 90th percentile of CDF.

An indoor localization system in wireless sensor networks has become a hot development area. Received signal strength indicator (RSSI)-based localization is a promising technique since it requires a relatively low configuration, battery power and easy control. However, the received signal strength is influenced by channel interference and propagation environments. This characteristic affects channel stability and location accuracy in RSSI-based localization. As a result, we propose a novel indoor localization system consisting of a preprocessing method and a post-processing method. To improve channel stability, the pre-processing method selects an optimal channel in terms of the smallest distance error. The optimal channel is less affected to IEEE 802.11. To develop location accuracy, the post-processing method performs maximum likelihood estimation-based location tracking scheme considering the reliability of the RSSI value measurement. We apply three methods to the existing MLE to improve the reliability of the RSSI value. By using this indoor localization system with preprocessing and post-processing, the location error can be reduced. We also proved the performance of the indoor localization system by conducting experiments in a real indoor environment.

Location-based services have become of great importance to a wide range of applications in the era of mobile computing. Various wireless indoor localization techniques are emerging. Due to the wide deployment and availability of WiFi infrastructure, WiFi fingerprint-based indoor localization has become one of the most attractive localization techniques. Site survey to build WiFi fingerprint library and fingerprint matching-based real-time positioning are based on WiFi indoor localization method two basic stages. Equipment heterogeneity and environmental dynamics are two main factors affecting indoor localization accuracy. In order to solve these problems, an algorithm based on Hierarchical Edit Distance(HED) function is proposed to realize calibrations-free fingerprint comparison of heterogeneous devices. RSSI information collected by different mobile devices is transformed into AP sequence. The hierarchical energy level of each AP is calculated according to the difference of the RSSI values. Then the distance between the WiFi fingerprints is calculated by edit distance. In order to locate WiFi fingerprint RSSI information, we use HED to obtain K neighbors, and use the WKNN algorithm to predict position. In order to verify robustness and effectiveness of the algorithm, five different mobile devices were used to collect WiFi RSSI information in three different types of indoor environments. The average localization accuracy was 1.5m.

In recent years, developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location-Based Service (LBS) in indoor environment. Several advantages of Radio Frequency Identification (RFID) Technology, such as anti-interference, small, light and portable size of RFID tags, and its unique identification of different objects, make it superior to other wireless communication technologies for indoor positioning. However, certain drawbacks of existing RFID-based IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the LBS, largely limit the application of RFID-based IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID-based IPS by using cheaper active RFID tags and sensors. Furthermore, we also proposed three localization algorithms: Weighted Path Loss (WPL), Extreme Learning Machine (ELM) and integrated WPL-ELM. WPL is a centralized model-based approach which does not require any reference tags and provides accurate location estimation of the target effectively. ELM is a machine learning fingerprinting-based localization algorithm which can provide higher localization accuracy than other existing fingerprinting-based approaches. The integrated WPL-ELM approach combines the fast estimation of WPL and the high localization accuracy of ELM. Based on the experimental results, this integrated approach provides a higher localization efficiency and accuracy than existing approaches, e.g., the LANDMARC approach and the support vector machine for regression (SVR) approach.

Received Signal Strength Indicator (RSSI) is simplest among various indoor localization methods. The main drawback is requiring a training phase to build fingerprints database, which is time consuming and sensitive to changes of test environment. Time based approaches such as Time of Arrival (ToA) and Time Difference of Arrival (TDoA) achieve better performance with some hardware expenditure, while location accuracy is significantly affected by multipath interference. By employing Super-resolution techniques (SRTs), multipath components can be estimated to improve localization precision, but limited to OFDM-based Multiple Input Multiple output (MIMO) wireless communication systems. Considering for general radio signals with strong correlation, we propose an enhanced TDoA approach based on RSS-assisted cross-correlation method. Simulation and experimental results demonstrate that our method is effective to mitigate multipath interference, verified by IEEE 802.11b WLAN. Compared with SRTs, our method achieves comparable localization precision, and has superiority in computational complexity and anti-noise performance.

Today, there is widespread use of mobile applications that take advantage of a user's location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user's behalf can significantly improve a smartphone's battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy.

This work designs and develops indoor and outdoor navigation architecture for accurate positioning in small areas. It combines sophisticated technologies, including GPS, Google Maps API, and spatial and descriptive data, to create a precise and user-friendly navigation solution. In addition, maintaining an updated database and integrating interactive features facilitates the adoption of future advanced technologies such as Bluetooth Low Energy (BLE) and Wi-Fi networks for improved accuracy in indoor navigation. This architecture is applied at the University of Mosul to guide students and visitors aboard the large university with many campus facilities. The application allows users to find specific colleges and facilities, look for destinations, and find the shortest way to reach their desired location. The testing results show that the application is efficient regarding location accuracy and response speed, with a 95% user satisfaction rate.

As the communication field continues to evolve, positioning technology has emerged as a crucial and valuable research avenue. Particularly, indoor positioning presents an innovative and trending direction in this technology. During the research process of indoor positioning, it is essential to consider the impact of various environmental disturbances on positioning accuracy. Additionally, efforts should be geared towards minimizing these environmental influences on the positioning system's performance and reducing the complexity of the positioning algorithm. The ideal scenario for indoor positioning involves using a relatively straightforward positioning algorithm to obtain results with high positioning accuracy. That said, it is a challenging balance to strike, given the intricate factors that influence the effectiveness of indoor positioning. This paper focuses on the Time Difference of Arrival (TDOA) localization method. We explore the TDOA-based triangle centroid algorithm, Fang's algorithm, and Chan's algorithm, all prominent methods in this field. Each of these approaches presents unique strengths and challenges, offering diverse solutions to address the complexity of indoor positioning. Their comparative analysis will provide insights into the development of efficient, accurate, and straightforward indoor positioning techniques.

While some indoor Location Based Services (LBSs), such as medical equipment location in hospitals or people location in museums, do not need to estimate the trajectory of devices at short time intervals, some others, such as people guidance, require a frequent estimation of the device position. When providing an LBS for the latter, motion models and the information provided from motion sensors are commonly used to reduce the error in the localization, but this information is not always available. In this paper, we propose an approach to estimate the position of a moving device using a topological radio-map designed for static WiFi localization in a previous work. This approach uses a Bayes filter that continuously estimates the most likely position of the device. This filter will have to deal with the low working frequency of the device and the uncertainty of the observation to provide an accurate and fast estimation. Experiments performed in a real multi-floor environment show that the system is able to correctly track the device position, reducing the mean localization error.