Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting

In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of the estimation of building/floor and floor-level location coordinates and uses a different estimation framework for each task with a dedicated output and hidden layers enabled by SIMO DNN architecture. We carry out preliminary evaluation of the performance of the hybrid floor classification and floor-level two-dimensional location coordinates regression using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere University of Technology (TUT), Finland, covering a single building with five floors. Experimental results demonstrate that the proposed SIMO-DNN-based hybrid classification/regression scheme outperforms existing schemes in terms of both floor detection rate and mean positioning errors.Comment: 6 pages, 4 figures, 3rd International Workshop on GPU Computing and AI (GCA'18

Maximum convergence algorithm for WiFi based indoor positioning system

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points

An indoor positioning system using Bluetooth Low Energy

In this paper, we present a Bluetooth Low Energy (BLE) based indoor positioning system developed for monitoring the daily living pattern of old people (e.g. people living with dementia) or individuals with disabilities. The proposed sensing system is composed of multiple sensors that are installed in different locations in a home environment. The specific location of the user in the building has been pre-recorded into the proposed sensing system that captures the raw Received Signal Strength Indicator (RSSI) from the BLE beacon that is attached on the user. Two methods are proposed to determine the indoor location and the tracking of the users: a trilateration-based method and fingerprinting-based method. Experiments have been carried out in different home environments to verify the proposed system and methods. The results show that our system is able to accurately track the user location in home environments and can track the living patterns of the user which, in turn, may be used to infer the health status of the user. Our results also show that the positions of the BLE beacons on the user and different quality of BLE beacons do not affect the tracking accuracy

Clustering benefits in mobile-centric WiFi positioning in multi-floor buildings

In mobile-centric indoor positioning, having a small databases to transfer from the network side to the mobile is of utmost importance. For scalable and low-complexity solutions, various clustering algorithms have been suggested in the literature, either in coordinates or 3D dimension or in the Access Points or Received Signal Strength (RSS) dimension. Typically, the two dimensions were investigated separately. This paper offers a comparative analysis between different clustering methods, together with a novel metric, called the Penalized Logarithmic Gaussian Distance metric which can boost the performance of the clustering. The results are compared based on real-field measurement data in two different multi-floor buildings and they are given in terms of estimation errors, floor detection probabilities and complexity. It is shown that the proposed metric enhances the performance of both 3D and RSS clustering and that the RSS clustering has lower complexity but worse performance than the 3D clustering. We are also providing in open-access the measurement data together with the Python-based implementation of the algorithms to serve as future benchmarks for indoor positioning studies.acceptedVersionPeer reviewe

From Compression of Wearable-based Data to Effortless Indoor Positioning

In recent years, wearable devices have become ever-present in modern society. They are typically defined as small, battery-restricted devices, worn on, in, or in very close proximity to a human body. Their performance is defined by their functionalities as much as by their comfortability and convenience. As such, they need to be compact yet powerful, thus making energy efficiency an extremely important and relevant aspect of the system. The market of wearable devices is nowadays dominated by smartwatches and fitness bands, which are capable of gathering numerous sensor-based data such as temperature, pressure, heart rate, or blood oxygen level, which have to be processed in real-time, stored, or wirelessly transferred while consuming as little energy as possible to ensure long battery life. Implementing compression schemes directly at the wearable device is one of the relevant methods to reduce the volume of data and to minimize the number of required operations while processing them, as raw measurements include plenty of redundancies that can be removed without damaging the useful information itself. This thesis presents a number of contributions in the field of compression of wearable-based data, mainly in areas of lossy compression techniques designated for the time series sensor-based data and positioning. In the scope of this work, two novel time-series compression techniques are proposed, namely Direct Lightweight Temporal Compression (DLTC) and Altered Symbolic Aggregate Approximation (ASAX), which are specifically designed to address relevant challenges of modern wearable systems. As many of the modern wearables also possess localization capabilities critical for navigation, tracking, and monitoring applications, reducing the computational and storage demands for indoor positioning applications is the second addressed challenge. Performing the positioning task quickly and efficiently on all connected devices, including wearables, becomes crucial in industrial applications, eHealth, or security. As the localization technique of choice in Global Navigation Satellite System (GNSS) signal-obscured scenarios, positioning via fingerprinting proves a reliable and efficient solution, while arising new challenges to be solved. Improving the efficiency of the fingerprinting-based system by applying lossy compressions onto the training radio map is realized by proposing, implementing, and evaluating various novel dimensionality-reduction techniques. This thesis proposes Element-Wise cOmpression using K-means (EWOK), a bitlevel compression based on element-wise k-means clustering, radio Map compression Employing Signal Statistics (MESS), a sample-wise compression that extracts signal statistics based on their locations, as well as evaluates feature-wise methods Principal Component Analysis (PCA) and Auto-Encoder (AE) that transform fingerprints into low-dimensional representation. The evaluation in the thesis shows the effectiveness of each compression scheme on 26 different datasets and provides the results achieved by combining the individual schemes together, accomplishing multi-dimensional radio map compression that sustains high positioning accuracy of the dataset, despite manyfold size reduction. The processing requirements of the positioning system are further addressed by proposing a cascade of models that reduces the required search space of the algorithm. By combining numerous Machine Learning (ML) architectures, it is capable of further reducing the positioning time (and thus, positioning effort), while improving the positioning performance. The thesis further includes the introduction of an indoor positioning dataset collected by the author, denoted TUJI 1, a novel performance metric to evaluate the latency caused by the lossy compression, and several crucial adjustments to the distance metric calculations, generalizing their applicability. The thesis provides novel insights into the compression of sensor-based, timeseries data and into reducing the computational effort of the fingerprinting positioning schemes while introducing a relevant number of novel and efficient solutions beyond the State-of-the-Art.Cotutelle -yhteisväitöskirj