924 research outputs found

    A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting

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    One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings --- e.g., a big shopping mall and a university campus --- is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural networks (DNNs) for scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built. Experimental results for the performance of building/floor estimation and floor-level coordinates estimation of a given location demonstrate the feasibility of the proposed DNN-based indoor localization system, which can provide near state-of-the-art performance using a single DNN, for the implementation with lower complexity and energy consumption at mobile devices.Comment: 9 pages, 6 figure

    Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

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    The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

    A Proposal of the Fingerprint Optimization Method for the Fingerprint-Based Indoor Localization System with IEEE 802.15.4 Devices

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    Nowadays, human indoor localization services inside buildings or on underground streets are in strong demand for various location-based services. Since conventional GPS cannot be used, indoor localization systems using wireless technologies have been extensively studied. Previously, we studied a fingerprint-based indoor localization system using IEEE802.15.4 devices, called FILS15.4, to allow use of inexpensive, tiny, and long-life transmitters. However, due to the narrow channel band and the low transmission power, the link quality indicator (LOI) used for fingerprints easily fluctuates by human movements and other uncontrollable factors. To improve the localization accuracy, FILS15.4 restricts the detection granularity to one room in the field, and adopts multiple fingerprints for one room, considering fluctuated signals, where their values must be properly adjusted. In this paper, we present a fingerprint optimization method for finding the proper fingerprint parameters in FILS15.4 by extending the existing one. As the training phase using the measurement LQI, it iteratively changes fingerprint values to maximize the newly defined score function for the room detecting accuracy. Moreover, it automatically increases the number of fingerprints for a room if the accuracy is not sufficient. For evaluations, we applied the proposed method to the measured LQI data using the FILS15.4 testbed system in the no. 2 Engineering Building at Okayama University. The validation results show that it improves the average detection accuracy (at higher than 97%) by automatically increasing the number of fingerprints and optimizing the values

    Autoencoder extreme learning machine for fingerprint-based positioning: A good weight initialization is decisive

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    Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use k -nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline

    A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

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    Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

    Wi-Fi Fingerprinting for Indoor Positioning

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    Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process
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