RSSI-Fading-Based Localization Approach in BLE5.0 Indoor Environments

The accurate positioning of location in indoor environment has become crucial in many location-based services, mainly where global positioning systems (GPSs) are unavailable or fail to navigate correctly. Conventional fingerprint-based approaches face challenges with instability, low accuracy, and being sensitive to changes in the environment. This study proposes a robust fingerprint-based machine learning (ML) model for dynamic environment indoor navigation in real time. The proposed model uses link quality indicator (LQI) values from IEEE 802.15.4 as fingerprints and supervised learning algorithms, showing high accuracy and a strong ability to adapt to changes in the environment. A room within a building floor has been regarded as the unit of location identification instead of the user’s exact coordinates to make the suggested model more relevant under practical conditions. The model was trained and tested using a real LQI dataset collected from varied indoor conditions to ensure the system can adapt effectively and operate consistently in dynamic environments and signal conditions. The results show that the proposed model surpasses fingerprinting indoor navigation in room detection accuracy and flexibility to environmental changes. An implemented prototype proved the real-time capability of the proposal in smart buildings, hospitals, and industrial IoT settings.

Robot odor source localization in indoor environments based on gradient adaptive extremum seeking search

In this paper, we present new received signal strength (RSS)-based indoor localization system. In indoor environment, it is not desirable to use a theoretical signal propagation path-loss model. The signal propagation is severally affected by surrounded object, especially metallic object. In order to cope with this problem, we introduce a new path-loss exponent estimation (PLE) node. This node generates a path-loss model appropriate for a given space. We then determine a mobile node's location based on the new path-loss model. Since PLE node operates in real time manner, it can robustly adapt the path-loss model for the circumstance. We evaluate our adaptive path-loss model-based localization system through actual experimental tests.

As smartphones are increasingly popular, location-based services (LBSs) have become one of the crucial applications in daily lives. While outdoor localization is relatively easy leveraging GPS signals, localization in indoor environments is difficult due to the lack of GPS. Thus, due to the pervasive deployment of WiFi access points, there have been numerous studies on WiFi based indoor positioning. However, the multi-path fading of WiFi signals causes time-varying received signal strengths of WiFi signals, which leads to poor accuracy of WiFi localization. Moreover, WiFi scanning period, about 3∼4 seconds in general smartphone, may provide poor quality of services in the context of refreshment interval. Motivated by these limitations, we study the usability of tw o other sources that are currently available in smartphones: magnetic field strength and cellular signal strength, for indoor positioning purposes. Our preliminary measurements show that these two sources satisfy three properties needed for localization: time-in-variance, location representativeness and universality. Because these three sensors have their own characteristics, some problems in single sensory data based localization could be complementarily overcome. We will show how to combine the three smartphone sensory data for indoor positioning based on each module's characteristics, and how much accuracy is achieved by the hybrid localization.

People localization is required for many novel applications like for instance proactive care for the elders or people suffering degenerative dementia such as Alzheimer's disease. This paper introduces a new system for people localization in indoor environments. It is based on a topology-based WiFi signal strength fingerprint approach. Accordingly, it is a robust, cheap, ubiquitous and non-intrusive system which does require neither the installation of extra hardware nor prior knowledge about the structure of the environment under consideration. The well-known curse of dimensionality critically emerges when dealing with complex environments. The localization task turns into a high dimensional classification task. Therefore, the core of the proposed framework is a fuzzy rule-based multiclassification system, using standard methodologies for the component classifier generation such as bagging and random subspace, along with fuzzy logic to deal with the huge uncertainty that is characteristic of WiFi signals. Achieved results in two real environments are encouraging, since they clearly overcome those ones provided by the well-known nearest neighbor fingerprint matching algorithm, which is usually considered as a baseline for WiFi localization.

BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images

The purpose of this paper is to provide a new method for solving the problem of the 3D source localization in indoor environments with weak airflow by the experimental study on the multi-robot method. For this purpose, we developed a 3D MRO (mobile robot olfaction) system consisting of three mobile robots. The gas sensor carried with each robot has a moving range of 0.5 m to 1.5 m on the Z-axis. A total of 60 experiments were conducted to validate and compare the standard whale optimization algorithm (SWOA) and the standard particle swarm optimization (SPSO) methods in a 7.65 m×4.1 m experimental area with two source heights (0.75 m and 1.05 m). For two source heights, the success rate and localization step of the SPSO method are 56.7% (17/30) and 31.4 steps, respectively. In addition, the success rate and localization step of the SWOA method are 80% (24/30) and 32.9 steps, respectively. These results show that the SWOA method has strong application potential in indoor environments with weak airflow.

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.

Due to the limitation of Global Navigation Satellite System (GNSS), it cannot work properly without direct line of sight to GNSS satellites such as in urban and indoor environments. Visual processing such as Visual Odometry (VO) and Structure-From-Motion (SFM) is usually used for the indoor navigation or localization. SFM was introduced in 1990 and had been successfully used for reconstruction of unordered images, and in the process of reconstruction, the SFM would estimate the position of the camera for each image at the same time. This paper introduces three most common SFM tools and suggests a positioning method for small UAVs equipped with vision cameras in indoor environment. Each stage of the SFM algorithms is studied to analysis the impact on the final positioning accuracy. The output from different feature detection, feature matching, initial pair setting, and the reconstructions are compared with an indoor reference localization system based on infrared cameras. The objective of this paper is to suggest a positioning and navigation method for small UAVs equipped with vision cameras in indoor environment. In this research, we will study the impact of different stages of SFM algorithm on the final positioning accuracy. Several objects such as chairs or boxes would be placed randomly in a lab room to build a feature-based environment without any special landmarks. In our experiment, an indoor localization system based on infrared cameras will be used as the reference navigation result to evaluate the final performance of the suggested algorithm.

This paper researches a robotic localization system based on motion capture indoor environment. We apply motion capture technology to realize multi-robot positioning which achieves the multi-robots’ localization in indoor environment. It includes motion capture system, some robots, and main computer for data calibration and communication. Motions capture system based on the theory of photogrammetry which was analyzed. And the virtual experiment environment was established according to the actual experimental environment. The performance is demonstrated by numerical simulation and experiment.

Global localization problem is one of the classical and important problems in mobile robot. In this paper, we present an approach to solve robot global localization in indoor environments with grid map. It combines Hough Scan Matching (HSM) and grid localization method to get the initial knowledge of robot's pose quickly. For pose tracking, a scan matching technique called Iterative Closest Point (ICP) is used to amend the robot motion model, this can drastically decreases the uncertainty about the robot's pose in prediction step. Then accurate proposal distribution taking into account recent observation is introduced into particle filters to recover the best estimate of robot trajectories, which seriously reduces number of particles for pose tracking. The proposed approach can globally localize mobile robot fast and accurately. Experiment results carried out with robot data in indoor environments demonstrates the effectiveness of the proposed approach.

Wireless local area network fingerprint-based indoor localization schemes have been widely studied because of the increasing requirements of location-based services (LBSs). The features of fingerprint-based localization are known to have higher precision in indoor environments than traditional methods, such as triangulation. However, the precision depends on the amount of pre-created received signal strength (RSS) fingerprints, which is associated with the number of reference points (RPs) of the RSS measurements and the available signal sources in the environment. In this paper, we consider the resource limitations of todays’ wireless environment and propose an improved fingerprint-based localization approach that adapts a path loss model for fingerprint creation and localization. Based on the proposed approach, we present two related localization schemes. The first is a path-loss-based fingerprint localization (PFL) scheme and the second is a dual-scanned fingerprint localization (DFL) scheme. The PFL attempts to improve positioning precision, and the DFL attempts to guarantee positioning reliability. Several simulations are performed, and they show that the proposed schemes improve the positioning precision and reliability in resource-limited environments, which would improve the practicability of fingerprint-based localizations in indoor LBSs.

Since the well-established navigation systems such as GPS are ineffective in indoor environments, research into developing novel indoor positioning technologies has emerged in recent years. While several technologies are being investigated, a practical and reliable indoor positioning system is yet to emerge. Indoor positioning using light signals holds a great potential to provide a reliable solution to the indoor positioning problem. This paper presents a comprehensive review of the available literature on light-based indoor positioning systems. The paper establishes a classification based on the design of various light-based indoor positioning (LIP) systems, and discusses their key components including hardware, software and algorithms. The limitations and challenges of the LIP systems are discussed in details, and current published solutions are presented. The review also provides a comparison between LIP systems and other optical positioning systems, and identifies the associated research gaps. Finally, based on the identified gaps, the future research directions in both LIP systems and other optical positioning systems are presented.

<p>The interest in indoor positioning systems has been rising in the last couple of decades. This interest was further accelerated by the availability of signals of opportunity. The introduction of signals of opportunity into the indoor environments has meant that these widely available technologies can be utilised to achieve higher levels of accuracy and precision for positioning in parking garages, airports, underground locations, large office buildings, and shopping centres. Indoor positioning can also be utilised for emergency services or for Building Information Models (BIM) for purposes such as construction sites or hospital building management. In addition to the need for indoor and outdoor positioning, there is a need to ensure seamless transitions in and out of these different environments. For example, building information from BIM could be utilised for navigation of BIM users that considers the architectural barriers (e.g., for wheelchairs, baby strollers, and transport of large equipment within the facility). For this, a positioning system capable of seamlessly transitioning in and out of buildings is needed. This paper proposes a multi-sensor and cooperative positioning method of seamless environment transition and presents the first architecture concept. For this purpose, Global Navigation Satellite System (GNSS) data are planned to be fused with Ultra Wide-Band (UWB), Inertial Measurement Unit (IMU), Wireless-Fidelity (Wi-Fi), visual camera and LiDAR (Light Detection and Ranging) data. All sensors required for such a positioning system, are already available on modern smartphones commonly used as low-cost platforms for pedestrian navigation and localisation. Newer smartphones are also equipped with LiDAR and UWB chips, which are a future signal of opportunity, just like Wi-Fi currently is. The transition to different environments will be determined based on the UWB anchors called identical anchors that will be used to transition from the global coordinate system for the outdoor environment to the local building environment for positioning in indoor environments. The proposed multi-sensor and cooperative positioning system is expected to be able to achieve decimetre-level accuracy.</p>

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.