Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios

This paper presents our experience on a real case of applying an indoor localization system formonitoringolderadultsintheirownhomes. Sincethesystemisdesignedtobeusedbyrealusers, therearemanysituationsthatcannotbecontrolledbysystemdevelopersandcanbeasourceoferrors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task

Perancangan Indoor Localization Menggunakan Bluetooth untuk Pelacakan Posisi Benda di dalam Ruangan

Dewasa ini teknologi untuk melacak posisi telah banyak digunakan. Teknologi tersebut menggunakan GPS milik Pemerintah Negara Amerika Serikat. GPS tersebut dapat digunakan untuk mencari posisi sekarang, alamat tempat atau arah ke suatu tempat. Akan tetapi GPS memiliki kekurangan yaitu akurasi yang rendah ketika digunakan di dalam ruangan. Oleh karena itu, teknologi untuk melacak posisi di dalam ruangan mulai dikembangkan dengan konsep indoor localization. Sistem indoor localization ini dibangun dalam media perangkat bergerak menggunakan kekuatan sinyal Bluetooth untuk memprediksi posisi pengguna dan smartphone yang kemudian data tersebut akan disimpan dalam basis data guna menunjang fungsionalitas lainnya. Kekuataan sinyal tersebut diolah menggunakan metode Trilateration. Metode tersebut memiliki 2 komponen penting dalam memprediksi posisi yaitu jarak dan posisi pemancar Bluetooth. Jarak dapat dihitung dengan mengambil rata-rata nilai kekuatan sinyal Bluetooth yang didapat. Pengujian sistem ini dilakukan di Laboratorium Algoritma dan Pemrograman, Teknik Informatika ITS.Hasil pengujian sistem ini terdiri dari dua pengujian yaitu fungsionalitas dan akurasi. Pengujian fungsionalitas menghasilkan hasil berhasil di semua fungsionalitas yang dibangun. Sedangkan pengujian akurasi menghasilkan akurasi yang cukup rendah yaitu 2,44 meter

InLoc: Indoor Visual Localization with Dense Matching and View Synthesis

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching rather than local features to deal with textureless indoor scenes, and (iii) pose verification by virtual view synthesis to cope with significant changes in viewpoint, scene layout, and occluders. Second, we collect a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data

POSTER: Privacy-preserving Indoor Localization

Upcoming WiFi-based localization systems for indoor environments face a conflict of privacy interests: Server-side localization violates location privacy of the users, while localization on the user's device forces the localization provider to disclose the details of the system, e.g., sophisticated classification models. We show how Secure Two-Party Computation can be used to reconcile privacy interests in a state-of-the-art localization system. Our approach provides strong privacy guarantees for all involved parties, while achieving room-level localization accuracy at reasonable overheads.Comment: Poster Session of the 7th ACM Conference on Security & Privacy in Wireless and Mobile Networks (WiSec'14

Efficient AoA-based wireless indoor localization for hospital outpatients using mobile devices

The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients’ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients’ requirements for wayfinding

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

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