FreeNavi: Landmark-Based Mapless Indoor Navigation Based on WiFi Fingerprints

Indoor localization has gained significant attention recently due to the growing demand for location-aware applications within indoor environments. Among various techniques, Wi-Fi fingerprinting combined with the Internet of Things (IoT) has emerged as a promising solution for accurate and cost-effective indoor localization. This paper surveys the existing research related to indoor localization using Wi-Fi fingerprinting with the IoT, aiming to provide a comprehensive understanding of the advancements, challenges, and potential applications in this field. The paper begins by introducing the fundamental concepts of indoor localization and the role of Wi-Fi fingerprinting in achieving accurate position estimation. In addition, this paper focuses on the contributions of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in improving localization accuracy, robustness, and scalability. The reviewed papers have been examined from various aspects, including system architecture, deployment strategies, fingerprint creation techniques, and localization algorithms, with a discussion about the advantages and limitations of each approach. In addition to discussing the state-of-the-art techniques, this paper identifies research gaps and open challenges in indoor localization using Wi-Fi fingerprinting with the IoT. The findings presented in this paper can guide future research efforts, leading to the development of intelligent and context-aware IoT applications within indoor environments.

Introduction:An important and complicated problem related to the multilateration of Wi-Fi or Bluetooth Low Energy signals as well as Wi-Fi fingerprinting is the procedure of infrastructure adjustment which includes Wi-Fi radio map construction and Wi-Fi or Bluetooth Low Energy radio signal path loss model calibration.Purpose:Developing a method for navigation and Wi-Fi radio map construction, which would provide user’s indoor navigation, Bluetooth Low Energy path loss model calibration and Wi-Fi radio map collaborative semi-automatic construction.Results:The paper presents a collaborative semi-automatic Wi-Fi radio map construction method based on the combination of Bluetooth Low Energy multilateration, Wi-Fi fingerprinting, Wi-Fi radio map collaborative semiautomatic construction procedure and semi-automatic Bluetooth Low Energy path loss model calibration. For the semi-automatic calibration procedure of the Bluetooth Low Energy signal propagation model and for the method of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation, a calibration algorithm and an algorithm of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation were proposed, respectively. A mobile application has been developed which implements the proposed algorithms in order to test them and analyze the results.Practical relevance:The proposed method allows you to avoid time-consuming procedures of constructing a map of Wi-Fi radio signals and semi-automatic calibration of Bluetooth Low Energy signal propagation in the offline phase.

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.

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.

In the cloud age, it is quite easy to collect sensory data from smartphones. With these sensory data, it is desired to provide various kinds of applications to serve the user. In this research, we aim at developing an indoor navigation system on smartphone using solely smartphone sensory data. There are many researches on indoor localization and navigation in the literature. Nevertheless, environmental sensors and/or wearable sensors are usually needed. This can be costly and inconvenient. In this paper, we propose a smartphone indoor localization system using only accelerometer and gyroscope data from the smartphone. The Pedestrian Dead Reckoning (PDR) approach is used to build this system. The PDR approach is simple and efficient though seems traditional. The major weakness of the PDR is that the estimation error would accumulate over time. Thus we propose to add so-called calibration marks which look like short arrows and are placed on both the floor plan and the ground. To use the system, the user first finds a calibration mark on the ground, stands on it and faces the right direction. He/she then moves the android icon (representing the user) on top of the calibration mark on the floor plan on the smartphone. When the user starts to move, the android icon also moves on the floor plan following the real-time estimation of step length and moving direction change for each step from accelerometer and gyroscope data. This is a prototype of an indoor navigation system that can become fully functional after an optimal path planning module is included. Experimental results of estimated walking trace tests show high accuracy. The system is promising and useful as long as a floor plan and calibration marks are built in advance.

Autonomous multisensor tracking surveillance system

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.

Indoor positioning system plays a key role in location-based services since the widely used Global Navigation Satellite System (GNSS) is denied in indoor scenarios. Crowdsensing or walking-surveying based indoor positioning is proposed aiming at providing low-cost and high-efficient 3D location. This paper proposes a crowdsensing/walking-surveying 3D indoor positioning system by fusing the crowd-sensed inertial data and Wi-Fi fingerprinting samples using deep learning frameworks. A sine-wave-based step detector is used for pedestrian dead-reckoning (PDR) to generate original dense-trajectories. An enhanced optimization-based algorithm (Opt) and a smoothing-based algorithm (Smo) are proposed and evaluated to correct the original dense-trajectories into near-true dense-trajectories which are used to construct the inertial database and Wi-Fi radio map. A ResNet-based inertial neural-network and a BiLSTM-based Wi-Fi fingerprinting neural-network are trained on the constructed navigation database and combined by a Kalman filter to provide accurate and robust 3D localization performance. The realistic experimental results among complex indoor environments demonstrate that the proposed algorithms are proved to achieve a precise 3D indoor localization performance which is superior to several existing relative methods.

Abstract. Recent years have witnessed the rapid development of navigation systems. They gradually become the default way of navigating in our daily life. Despite benefiting from their convenience, we may be negatively influenced by them (Parush et al. 2007). Current navigation systems usually draw too much of users' attention to the mobile devices, which leads to users’ lacking interaction with the environment. It is often the case that people easily forget travelled route and get lost without navigation systems (Reilly et al. 2008). Empirical study shows that by dividing attention, navigation systems impair users’ spatial memory, and thus impact their navigation skills (Gardony et al. 2013). Since a higher engagement with the environment can improve users’ spatial knowledge acquisition without decreasing efficiency (Brügger et al. 2017), navigation systems should incorporate important environmental features.As objects of visual, cognitive or structural importance (Sorrows & Hirtle 1999), landmarks have been extensively studied, and considered as an important environmental feature to be incorporated into navigation systems (Nuhn & Timpf 2018, Richter 2017, Duckham et al. 2010, Raubal & Winter 2002). They serve as references to provide users with more natural and pleasant navigating experience (Richter & Winter 2014). However, most of landmark-related studies are in outdoor environment. Although researchers have attempted to classify and assess indoor landmarks (Ohm 2014, Viaene 2014), it is still very difficult to determine suitable ones. As a subset of semantic indoor landmarks, existing navigational aids (e.g. signs and floor plans) are designed to assist navigation. Among them, signs are most preferred by users (Wang et al. 2017). Being the signs that give necessary information to route users to possible destinations, directional signs contribute most to wayfinding (Gibson 2009). With route information explicitly presented by them, they are relevant environmental features to be incorporated into indoor navigation systems.In this study, we therefore propose to incorporate directional signs into indoor navigation systems, in order to guide users’ attention back to the environment, to facilitate their spatial learning and improve their spatial knowledge acquisition.We incorporated directional signs into a proof-of-concept indoor navigation system. First, we integrated directional signs and their semantics into a graph-based model. A directional sign was modelled as a node. Each destination on the sign was linked with the sign by an edge. In order to differentiate it from the normal edge, it was marked as semantic edge. Thus, the semantics of the sign was modelled directly into the graph, instead of being stored as attributes in a previous version of our study (Wang & Gartner 2018). Then, a top-K shortest path algorithm was employed to find candidate routes (Yen 1971). In the end, the route with the largest coverage of semantic edges was suggested to the user. The users would then be explicitly guided to follow the directional signs in the environment.In the next step, we will evaluate the efficiency and effectiveness of the proposed navigation system by an empirical study with human subjects. Within the experiment, we will also investigate on whether this kind of guidance can succeed in drawing users’ attention back to the environment and support their spatial knowledge acquisition.

Indoor localization of mobile nodes is emerging as a promising field in the past decades due to the advances in mobile devices and the increasing number of wireless services and applications. Wi-Fi fingerprints which is a vector of Received Signal Strength (RSS) values in a specific location are considered a good feature for indoor localization due to their simplicity. However, the existing methods such as decision tree (DT) and Naïve Bayes (NB) techniques have some drawbacks. DT has low efficiency because it uses the Boolean function, and NB provides a low accuracy of prediction for the sparse dataset. In this research, fingerprint referencing with two important methods, Support Vector Machine (SVM) and Long-Short Term Memory (LSTM) machine learning algorithms, are used to enable accurate indoor localization. Furthermore, data normalization is implemented to reduce positioning error by increasing the inconsistency of the RSS values. Further, several experiments have been conducted by using three mobile devices in the area of 280 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to create our dataset by taking 480 RSS records for both training and testing phases. The results show that the accuracy LSTM algorithm is 0.9 m, whereas the SVM algorithm accuracy is 1.1 m. Both experimental results show better accuracy for indoor environments can be achieved by the proposed approach than results recorded by several prior studies.

Novel weighted ensemble classifier for smartphone based indoor localization

Wi-Fi fingerprint based indoor positioning methods exploits naive signal strength in each location to predict user's location. However, using naive signal strength may limit the prediction accuracy, because the Wi-Fi signals follow the Log-distance path loss model in signal propagation. In this research, we propose to generate Wi-Fi fingerprint with the consideration of radio propagation model to reflex the characteristics of Wi-Fi signal. The evaluation result shows that following log-distance path loss model improves the prediction accuracy in fingerprint based indoor positioning.

Using smartphones for accurate indoor localization opens a new frontier of mobile services, offering enormous opportunities to enhance users' experiences in indoor environments. Despite significant efforts on indoor localization in both academia and industry in the past two decades, highly accurate and practical smartphone-based indoor localization remains an open problem. To enable indoor location-based services (ILBS), there are several stringent requirements for an indoor localization system: highly accurate that can differentiate massive users' locations (foot-level); no additional hardware components or extensions on users' smartphones; scalable to massive concurrent users. Current GPS, Radio RSS (e.g. WiFi, Bluetooth, ZigBee), or Fingerprinting based solutions can only achieve meter-level or room-level accuracy. In this paper, we propose a practical and accurate solution that fills the long-lasting gap of smartphone-based indoor localization. Specifically, we design and implement an indoor localization ecosystem Guoguo. Guoguo consists of an anchor network with a coordination protocol to transmit modulated localization beacons using high-band acoustic signals, a realtime processing app in a smartphone, and a backend server for indoor contexts and location-based services. We further propose approaches to improve its coverage, accuracy, and location update rate with low-power consumption. Our prototype shows centimeter-level localization accuracy in an office and classroom environment. Such precise indoor localization is expected to have high impact in the future ILBS and our daily activities.

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.