Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors

In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a real-world environment, collect a large dataset of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms in localizing a mobile device. We have found algorithms that give a mean error as accurate as 0.76 meters, outperforming other indoor localization systems reported in the literature. We also propose a hybrid instance-based approach that results in a speed increase by a factor of ten with no loss of accuracy in a live deployment over standard instance-based methods, allowing for fast and accurate localization. Further, we determine how smaller datasets collected with less density affect accuracy of localization, important for use in real-world environments. Finally, we demonstrate that these approaches are appropriate for real-world deployment by evaluating their performance in an online, in-motion experiment.Comment: 6 pages, 4 figure

Pelacakan Posisi Tag RFID Menggunakan Algoritma Genetika

Paper ini akan membahas tentang cara melacak posisi tag RFID berdasarkan nilai RSSI yang dideteksi oleh reader terhadap tag. Beberapa tag referensi sebagai acuan digunakan untuk menghasilkan posisi perkiraan dari tag yang dilacak. Algoritma Genetika digunakan untuk menyeleksi dan mengambil posisi terbaik dari beberapa posisi perkiraan. Metode ini digunakan untuk mencari optimalisasi posisi terbaik dari beberapa posisi perkiraan tag yang dilacak. Algoritma Genetika ini menggunakan persamaan jarak Euclidian. Dengan membandingkan posisi Aktual dan posisi yang dihasilkan oleh Algoritma Genetika Maka kesalahan posisi dapat dicari. Sistem pelacakan ini digunakan untuk mencari posisi tag yang berada di jalur pergerakan. Namun, meskipun begitu tetap ada perbedaan (error) posisi walaupun kecil. Nilai RSSI yang diambil oleh RFID reader cukup berpengaruh dalam terbentuknya posisi-posisi perkiraan. Semakin baik nilai RSSI yang diperoleh maka posisi tag yang dihasilkan akan menjadi lebih akurat

Experimental evaluation of mobile phone sensors

Smart phone has become an important part of people's daily life. Most of current smart phone are equipped with a rich set of built-in sensors. The mobile applications such as geo-location based video annotation and indoor positioning require precise measurements from sensors. In addition, understanding the sensing performance of a smart phone device is helpful for implementing a mobile application that needs sensor data. This paper presents an experimental evaluation of key sensors in a state of the art smart phone - Google Nexus 4. The sensors chosen in the paper are accelerometer, gyroscope, magnetometer and GPS. Substantial tests have been executed to evaluate the sensors' accuracy, precision, maximum sampling frequency, sampling period jitter, energy consumption

Smart displays: Personalisation of information panels

Information panels are a common part of university or administrative buildings. We can see large panels with news or social media feeds as well as small displays on conference rooms with information about their occupancy. All these panels usually present general information without any relation to a present audience. Presentation of personalised information for a particular user can be very helpful; however, for such personalisation we must take into account many aspects: identification of users in the display vicinity, sharing of the screen among multiple users etc. This paper is focused on the architecture of such system that allows presenting customised information on information panels for users within university buildings. Our solution allows detection of a user via Bluetooth beacons. The selected close display then presents information related to the user. In case there are multiple users in the display vicinity, the system evaluates their requirements and decides how to share the display.O

An Indoor Localization and Tracking System Using Successive Weighted RSS Projection

This letter proposes a novel successive weighted received signal strength (RSS) indoor localization and tracking system that projects previous time instance estimated mobile device (MD) position to provide projected RSS values. Such RSS projection increases the number of available RSS from Nm to Nm + N AP , where N AP is the total number of access points and Nm is the number of RSS values measured by MD, ranging from 0 to N AP . Our proposed system thus resolves the issues associated with insufficient or no RSS values received by MD. Inertial navigation system (INS) is merged with RSS localization system to provide a weighted fusion of projected and measured RSS values. The weighting factors are derived based on the INS and RSS localization accuracy where the former is initially accurate but deteriorates with time and the latter is time-independent but environment-dependent. The proposed system was tested in indoor environments and outperformed other existing localization systems such as RSS and INS fusion using extended Kalman filter and non-line-of-sight (NLOS) selection scheme, especially in heavy multipath environment, by 42% and 75%, respectively

Localização indoor por identificação de marcos

Dissertação de mestrado em Engenharia InformáticaHoje em dia, existem diversos métodos para determinar a localização devido ao fácil acesso à Internet, GPS e outras tecnologias existentes. Existem sistemas bem conhecidos como o GoogleMaps, que permitem a visualização do planeta Terra através de satélite, como existem também aplicações que, recorrendo ao GPS, permitem indicar a localização do utilizador a partir de um smartphone ou do automóvel. Estas evoluções tornam o mundo mais pequeno, mas são apenas referências exteriores, mostrando ruas e edifícios. Neste projeto, foi desenvolvido um sistema de localização indoor, mapeando um edifício e colocando marcos identificadores espalhados pela sua área. Foi desenvolvida uma aplicação móvel Android e sem necessidade de ligação à rede, para que seja possível a qualquer pessoa utilizar. O programa permite ao utilizador escolher o seu destino pretendido de uma lista de locais do edifício. Ao ser lido um dos marcos no edifício através da aplicação, o utilizador tem acesso a informação do marco e do local a que o marco está associado. O utilizador pode visualizar as plantas do edifício onde é indicado o seu ponto de origem e um destino escolhido, de modo a ajudar o utilizador a lá chegar.Nowadays, there are several methods to determine our localization due to the easy access to the internet, GPS and other technologies available. There are well-known systems like GoogleMaps, that allow the visualization of the planet Earth through satellite images, as well as other applications, which use the GPS signal to indicate our localization through a smartphone or in our car. These evolutions make the world smaller, but they are just outdoor references, showing streets and buildings. This project consists of a system for indoor localization, mapping a building and placing landmarks scattered around its area. It has been developed a mobile app using Android with no need to internet connection so everyone can use it. The program allows its user to select a destination from a set of locations, inside the building. When a landmark is read by the application, the user gets access to the information embedded on the landmark and the location related to it. The user can check the building's blueprints and where the starting point and the chosen destination are marked, in order to help the user to get there

RSS-Based Indoor Localization System with Single Base Station

The paper proposes an Indoor Localization System (ILS) which uses only one fixed Base Station (BS) with simple non-reconfigurable antennas. The proposed algorithm measures Received Signal Strength (RSS) and maps it to the location in the room by estimating signal strength of a direct line of sight (LOS) signal and signal of the first order reflection from the wall. The algorithm is evaluated through both simulations and empirical measurements in a furnished open space office, sampling 21 different locations in the room. It is demonstrated the system can identify user’s real-time location with a maximum estimation error below 0.7 m for 80% confidence Cumulative Distribution Function (CDF) user level, demonstrating the ability to accurately estimate the receiver’s location within the room. The system is intended as a cost-efficient indoor localization technique, offering simplicity and easy integration with existing wireless communication systems. Unlike comparable single base station localization techniques, the proposed system does not require beam scanning, offering stable communication capacity while performing the localization process

Comparative analysis of indoor positioning systems based on communications supported by smartphones

In this paper, common trends of architectural design, technologies, properties, and drawbacks of indoor positioning systems based on communications supported by smartphones are analyzed. The main idea of such kind of systems is that their users can use them through their mobile devices because such systems include positioning functionality based on such technologies as Wi-Fi, Bluetooth, and GSM. For example, museums might not need to buy expensive audioguides, but instead can provide their visitors with appropriate software for their smartphones. The paper presents a comparative analysis of most promising at the moment systems and solutions

Pelacakan Posisi Tag RFID Menggunakan Algoritma Genetika

Paper ini akan membahas tentang cara melacak posisi tag RFID berdasarkan nilai RSSI yang dideteksi oleh reader terhadap tag. Beberapa tag referensi sebagai acuan digunakan untuk menghasilkan posisi perkiraan dari tag yang dilacak. Algoritma Genetika digunakan untuk menyeleksi dan mengambil posisi terbaik dari beberapa posisi perkiraan. Metode ini digunakan untuk mencari optimalisasi posisi terbaik dari beberapa posisi perkiraan tag yang dilacak. Algoritma Genetika ini menggunakan persamaan jarak Euclidian. Dengan membandingkan posisi Aktual dan posisi yang dihasilkan oleh Algoritma Genetika Maka kesalahan posisi dapat dicari. Sistem pelacakan ini digunakan untuk mencari posisi tag yang berada di jalur pergerakan. Namun, meskipun begitu tetap ada perbedaan (error) posisi walaupun kecil. Nilai RSSI yang diambil oleh RFID reader cukup berpengaruh dalam terbentuknya posisi-posisi perkiraan. Semakin baik nilai RSSI yang diperoleh maka posisi tag yang dihasilkan akan menjadi lebih akurat