Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation

High accuracy indoor positioning system using Galois field-based cryptography and hybrid deep learning

In smart manufacturing, logistics, and other inside settings where the Global Positioning System (GPS) doesn't work, indoor positioning systems (IPS) are essential. Due to environmental complexity, signal noise, and possible data manipulation, traditional IPS techniques struggle with accuracy, resilience, and security. Online and offline phases are distinguished in the suggested indoor location system that employs deep learning and fingerprinting. During the offline phase, mobile devices gather signal strength measurements and contextual data traverse inside settings via Wi-Fi, Bluetooth, and magnetometers. Fingerprint classification using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering follows the application of signal processing techniques for noise reduction and data augmentation. The online phase involves extracting information to improve the model's accuracy. These features can be signal-based, spatial-temporal, motion-based, or environmental. The Deep Spatial-Temporal Attention Network (Deep-STAN) is an innovative hybrid model for location classification that combines Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Long-Short Term Memory (LSTMs), and attention processes. The model hyperparameters are fine-tuned using hybrid optimization to guarantee optimal performance. The work's main contribution is the incorporation of ECC, an effective encryption and decryption method for signal data, which is based on Galois fields. This cryptographic method is well-suited for real-world applications since it guarantees low-latency operations while simultaneously improving data integrity and confidentiality. In addition, S-box enhances the IPS's resilience and security by including QR codes for distinct location marking and blockchain technology for safe and immutable storing of positioning data. Moreover, the performance of the suggested model includes an accuracy of 0.9937, precision of 0.987, sensitivity of 0.9898, and specificity of 0.9878, while when 80% of data were used it had an accuracy of 0.9804, precision of 0.9722, sensitivity of 0.9859, and specificity of 0.9756. These outcomes prove that the proposed system is stable and flexible enough to be used in indoor positioning applications.

Performance of Fingerprinting-Based Indoor Positioning with Measured and Simulated RSSI Reference Maps

Numerous indoor positioning technologies and systems have been proposed to localize people and objects in large buildings. Wi-Fi and Bluetooth positioning systems using fingerprinting have gained popularity, due to the wide availability of existing infrastructure. Unfortunately, the implementation of fingerprinting-based methods requires time-consuming radio surveys to prepare databases (RSSI maps) that serve as a reference for the radio signal. These surveys must be conducted for each individual building. Here, we investigate the possibility of using simulated RSSI maps with fingerprinting-based indoor localization systems. We discuss the suitability of the two popular radio wave propagation models for the preparation of RSSI reference data: ray tracing and multiwall. Based on an analysis of several representative indoor scenarios, we evaluated the performance of RSSI distribution maps obtained from simulations versus maps obtained from measurement campaigns. An experimental positioning system developed by the authors was used in the study. Based on Bluetooth Low Energy beacons and mobile devices (smartphones), the system uses fingerprinting followed by a particle filter algorithm to estimate the user’s current position from RSSI measurements and a reference spatial RSSI distribution database for each Bluetooth beacon in the building. The novelty of our contribution is that we evaluate the performance of the positioning system with RSSI maps prepared both from measurements and using the two most representative indoor propagation methods, in three different environments in terms of structure and size. We compared not only the three RSSI maps, but also how they influence the performance of the fingerprint-based positioning algorithm. Our original findings have important implications for the development of indoor localization systems and may reduce deployment times by replacing reference measurements with computer simulations. Replacing the labor-intensive and time-consuming process of building reference maps with computer modeling may significantly increase their usefulness and ease of adaptation in real indoor environments.

Study of Generalized Phase Spectrum Time Delay Estimation Method for Source Positioning in Small Room Acoustic Environment

This paper considers the application of signal processing methods to passive indoor positioning with acoustics microphones. The key aspect of this problem is time-delay estimation (TDE) that is used to get the time difference of arrival of the source’s signal between the pair of distributed microphones. This paper studies the approach based on generalized phase spectrum (GPS) TDE methods. These methods use frequency-domain information about the received signals that make them different from widely applied generalized cross-correlation (GCC) methods. Despite the more challenging implementation, GPS TDE methods can be less demanding on computational resources and memory than conventional GCC ones. We propose an algorithmic implementation of a GPS estimator and study the various frequency weighting options in applications to TDE in a small room acoustic environment. The study shows that the GPS method is a reliable option for small acoustically dead rooms and could be effectively applied in presence of moderate in-band noises. However, GPS estimators are far less efficient in less acoustically dead environments, where other TDE options should be considered. The distinguishing feature of the proposed solution is the ability to get the time delay using a limited number of the adjusted bins. The solution could be useful for passively locating moving emitters of narrow-band continual noises using computationally simple frequency detection algorithms.

Machine Learning Applications for Short Reach Optical Communication

With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends.

Indoor Mapping of Magnetic Fields Using UAV Equipped with Fluxgate Magnetometer

Unmanned aerial vehicles (UAVs) are used nowadays in a wide range of applications, including monitoring, mapping, or surveying tasks, involving magnetic field mapping, mainly for geological and geophysical purposes. However, thanks to the integration of ultrasound-aided navigation used for indoor UAV flight planning and development in sensorics, the acquired magnetic field images can be further used, for example, to enhance indoor UAV navigation based on the physical quantities of the image or for the identification of risk areas in manufacturing or industrial halls, where workers can be exposed to high values of electromagnetic fields. The knowledge of the spatial distribution of magnetic fields can also provide valuable information from the perspective of the technical cleanliness. This paper presents results achieved with the original fluxgate magnetometer developed and specially modified for integration on the UAV. Since the magnetometer had a wider frequency range of measurement, up to 250 Hz, the DC (Direct Current) magnetic field and low frequency industrial components could be evaluated. From the obtained data, 3D magnetic field images using spline interpolation algorithms written in the Python programming language were created. The visualization of the measured magnetic field in the 3D plots offer an innovative view of the spatial distribution of the magnetic field in the area of interest.

Indoor Path-Planning Algorithm for UAV-Based Contact Inspection

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.