Sistem Deteksi Posisi Pada Area Indoor Menggunakan GSM Fingerprinting

Sistem deteksi posisi menggunakan GSM fingerprinting merupakan teknologi yang dirancang untuk mengetahui posisi dan pergerakan sebuah objek pada area indoor. Teknologi ini dikembangkan untuk mengatasi keterbatasan teknologi GPS yang tidak mampu bekerja pada area indoor. GSM Fingerprinting merupakan salah satu teknologi yang digunakan untuk penentuan posisi berdasarkan pola sinyal yang diterima dari sinyal GSM. Pengujian dan pengambilan data untuk penelitian ini dilakukan di area Kampus Politeknik Caltex Riau. Hasil pengujian didapatkan tingkat akurasi sebesar 47

Indoor Positioning in Wireless LANs using Compressive Sensing Signal-Strength Fingerprints

International audienceAccurate indoor localization is a significant task for many ubiquitous and pervasive computing applications, with numerous solutions based on IEEE802.11, bluetooth, ultrasound and infrared technologies being proposed. The inherent sparsity present in the problem of location estimation motivates in a natural fashion the use of the recently introduced theory of compressive sensing (CS), which states that a signal having a sparse representation in an appropriate basis can be reconstructed with high accuracy from a small number of random linear projections. In the present work, we exploit the framework of CS to perform accurate indoor localization based on signal-strength measurements, while reducing significantly the amount of information transmitted from a wireless device with limited power, storage, and processing capabilities to a central server. Equally importantly, the inherent property of CS acting as a weak encryption process is demonstrated by showing that the proposed approach presents an increased robustness to potential intrusions of an unauthorized entity. The experimental evaluation reveals that the proposed CS-based localization technique is superior in terms of an increased localization accuracy in conjunction with a low computational complexity when compared with previous statistical fingerprint-based methods

Indoor Positioning in Wireless LANs using Compressive Sensing Signal-Strength Fingerprints

International audienceAccurate indoor localization is a significant task for many ubiquitous and pervasive computing applications, with numerous solutions based on IEEE802.11, bluetooth, ultrasound and infrared technologies being proposed. The inherent sparsity present in the problem of location estimation motivates in a natural fashion the use of the recently introduced theory of compressive sensing (CS), which states that a signal having a sparse representation in an appropriate basis can be reconstructed with high accuracy from a small number of random linear projections. In the present work, we exploit the framework of CS to perform accurate indoor localization based on signal-strength measurements, while reducing significantly the amount of information transmitted from a wireless device with limited power, storage, and processing capabilities to a central server. Equally importantly, the inherent property of CS acting as a weak encryption process is demonstrated by showing that the proposed approach presents an increased robustness to potential intrusions of an unauthorized entity. The experimental evaluation reveals that the proposed CS-based localization technique is superior in terms of an increased localization accuracy in conjunction with a low computational complexity when compared with previous statistical fingerprint-based methods

Development of a WiFi and RFID based indoor location and mobility tracking system

Ubiquitous positioning and people mobility tracking has become one of the critical parts of our daily life. As a core element of the Location Based Services (LBS), the ubiquitous positioning capability necessitates seamless positioning across both indoor and outdoor environments. Nowadays, tracking outdoor with a relatively high accuracy and reliability can be achieved using matured technologies such as Global Navigation Satellite Systems (GNSS). However, it is still challenging for tracking in indoor environments such as airports, shopping malls and museums. The demand for indoor tracking has driven the fast development of indoor positioning and tracking technologies, especially Wi-Fi, RFID and smartphone etc. All these technologies have significantly enhanced the convenience of people’s daily life and the competitiveness of business firms. With the rapidly increased ubiquity of Wi-Fi enabled mobile phones and tablets, developing a robust location and mobility tracking system utilising such technologies will have a great potential for industry innovation and applications. This research is part of an Australian Research Council (ARC) project that involves two universities and one industry partner who is a large global shopping mall management company located in Australia. The project aims to develop a smart system for robust modelling and analysing the shopping behaviours of customers so that value-added services can be effectively provided. A number of field tests have been conducted and a large amount of data has been acquired both in the shopping mall of interest and the RMIT Indoor Positioning Laboratory. A large cohort of real users in the shopping mall were recorded where only one Wi-Fi access point (AP) connection at a time for each mobile device user was provided for our research. This makes most of the conventional tracking and positioning methods inapplicable. To overcome this constraint, a new hybrid system for positioning and mobility tracking — called single AP-connection location tracking system (SCLTS) was developed, which utilised Wi-Fi, RFID and mobile device technologies and took advantage of both the cell of origin (CoO) and fingerprinting positioning methods. Three new algorithms for Wi-Fi based indoor positioning were developed during this research. They are the common handover point determination (CHOPD) algorithm for determining the boundary of the cell; the algorithm for positioning with the case of same-line-dual-connection (SLDC) in a long narrow space (e.g., a long corridor) and the algorithm for positioning with the case of perpendicular-dual-connection of APs in a T-shape corridor for improving the positioning accuracy. The architecture of the SCLTS system was also developed as part of the implementation of the SCLTS system. Various experiments were conducted in a simulated large shopping-mall-like environment (i.e., the RMIT Indoor Positioning Lab) and the results showed that the performance of the SCLTS developed was very promising and the original goal of the project has been achieved. In addition, the two most popular indoor positioning methods — trilateration and fingerprinting were also optimised and implemented in a real industrial product and promising results have been achieved