Indoor positioning based on global positioning system signals

The Global Positioning System (GPS) is highly reliable and accurate when used outdoors.However, in indoor environments, due to the additional signal loss incurred by the walls of the buildings, the detection and decoding of GPS signals becomes a difficult task. As a solution to the indoor area coverage problem, an indoor positioning system based on GPS repeaters and a modified positioning algorithm is proposed, designed, and tested. A prototype indoor positioning system for 1D/2D positioning is built using directional GPS antennas and low-noise amplifiers (LNA). The modified positioning algorithm is used for the real time processing of captured live GPS data. All the system components are integrated and positioning is obtained for the evaluation of the system performance. Results of the experiments show that the proposed system can be used for indoor positioning in locations where there is no GPS signal reception. The proposed system facilitates the continuation of GPS services indoors with hardware additions to the buildings and only a software update to a standard GPS receiver

A centralized localization algorithm for prolonging the lifetime of wireless sensor networks using particle swarm optimization in the existence of obstacles

The evolution in micro-electro-mechanical systems technology (MEMS) has triggered the need for the development of wireless sensor network (WSN). These wireless sensor nodes has been used in many applications at many areas. One of the main issues in WSN is the energy availability, which is always a constraint. In a previous research, a relocating algorithm for mobile sensor network had been introduced and the goal was to save energy and prolong the lifetime of the sensor networks using Particle Swarm Optimization (PSO) where both of sensing radius and travelled distance had been optimized in order to save energy in long-term and shortterm. Yet, the previous research did not take into account obstacles’ existence in the field and this will cause the sensor nodes to consume more power if obstacles are exists in the sensing field. In this project, the same centralized relocating algorithm from the previous research has been used where 15 mobile sensors deployed randomly in a field of 100 meter by 100 meter where these sensors has been deployed one time in a field that obstacles does not exist (case 1) and another time in a field that obstacles existence has been taken into account (case 2), in which these obstacles has been pre-defined positions, where these two cases applied into two different algorithms, which are the original algorithm of a previous research and the modified algorithm of this thesis. Particle Swarm Optimization has been used in the proposed algorithm to minimize the fitness function. Voronoi diagram has also used in order to ensure that the mobile sensors cover the whole sensing field. In this project, the objectives will be mainly focus on the travelling distance, which is the mobility module, of the mobile sensors in the network because the distance that the sensor node travels, will consume too much power from this node and this will lead to shortening the lifetime of the sensor network. So, the travelling distance, power consumption and lifetime of the network will be calculated in both cases for original algorithm and modified algorithm, which is a modified deployment algorithm, and compared between them. Moreover, the maximum sensing range is calculated, which is 30 meter, by using the binary sensing model even though the sensing module does not consume too much power compared to the mobility module. Finally, the comparison of the results in the original method will show that this algorithm is not suitable for an environment where obstacle exist because sensors will consume too much power compared to the sensors that deployed in environment that free of obstacles. While the results of the modified algorithm of this research will be more suitable for both environments, that is environment where obstacles are not exist and environment where obstacles are exist, because sensors in this algorithm .will consume almost the same amount of power at both of these environments

Securing a wireless sensor network for human tracking: a review of solutions

Currently, wireless sensor networks (WSNs) are formed by devices with limited resources and limited power energy availability. Thanks to their cost effectiveness, flexibility, and ease of deployment, wireless sensor networks have been applied to many scenarios such as industrial, civil, and military applications. For many applications, security is a primary issue, but this produces an extra energy cost. Thus, in real applications, a trade-off is required between the security level and energy consumption. This paper evaluates different security schemes applied to human tracking applications, based on a real-case scenario.Junta de Andalucía P07-TIC-02476Junta de Andalucía TIC-570

Short Survey of Wireless Indoor Positioning Techniques and Systems

Smart city offers different services to different people depending on a wish list. It fulfills people's aspiration level, wherever there is willingness to change and to reform. Due to the complexity people movement within and between cities, localization techniques became popular with the global positioning system for outdoor applications, followed by Personal Networks (PNs) localization for indoor applications. PN are designed to provide a flexible and fast wireless communication between user’s devices and other devices, in various indoor environment places. PN mainly uses indoor positioning systems (IPSs) for improving numerous factors such as Self-organizing sensor networks, location sensitive billing, ubiquitous computing, context- dependent information services, tracking, and guiding. This paper gives a short survey of some kinds of IPSs, and focuses on triangulation to predict the target location, where for example it calculates the distance by measuring time difference of signals arrival (TDOA) over Orthogonal Frequency Division Multiplexing (OFDM), as one of several techniques identify the distance between the transmitters and receiver

Acoustic indoor localization employing code division multiple access

Thesis (Master)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2010Includes bibliographical references (leaves: 107-108)Text in English; Abstract: Turkish and Englishxvi, 160 69 leavesIndoor localization becomes a demand that comes into prominence day by day. Although extensively used outdoor location systems have been proposed, they can not operate in indoor applications. Hence new investigations have been carried on for accurate indoor localization in the last decade. In this thesis, a new indoor location system, that aims to locate an entity within an accuracy of about 2 cm using ordinary and inexpensive off-the-shelf devices, has been proposed and an implementation has been applied to evaluate the system performance. Therefore, time of arrival measurements of acoustic signals, which are binary phase shift keying modulated Gold code sequences using direct sequence spread spectrum technique, are done. Direct sequence-code division multiple access is applied to perform simultaneous accurate distance measurements and provides immunity to noise and interference. Two methods have been proposed for the location estimation. The first method takes the average of four location estimates obtained by trilateration technique. In the second method, only a single robust position estimate is obtained using three distances while the least reliable fourth distance measurement is not taken into account. The system performance is evaluated at positions from two height levels using two sets of variables determined by experimental results. The precision distributions in the work area and the precision versus accuracy plots depict the system performance for different sets of variables. The proposed system provides location estimates of better than 2 cm accuracy within 99% precision. Eventually, created graphical user interface provides a user friendly environment to adjust the parameters

An indoor positioning system based on global positioning system

GPS (Global Positioning System) has great demand in recent years and the use of GPS has increased widely in many areas like transportation, tracking, navigation, as well as being implemented in almost all of the smart phones for location based services to improve the quality of our daily life. GPS system communicates with the satellites which send the GPS signals to the earth to be able to provide needed information to the GPS receivers. GPS signals that reach to the earth is in low power and GPS receivers evaluate the position with respect to information in the signal. This position evaluation can be done with the error of 2.5 meters in today's technology. Despite this system is successful in outdoor areas, it is not so successful in indoor areas. Decoding GPS signals in the indoor areas is hard due to additional loss in the GPS signal because of interaction of the signals with physical obstacles. There is a need for increasing coverage of GPS signals in indoor areas like tunnels, undersea and buildings. In this thesis, an indoor positioning system based on GPS infrastructure is proposed and designed. Designed indoor positioning system consists of directional GPS antennas, downconverters, upconverter and IF antennas. For realizing the system, downconverters, upconverter and IF antenna are designed, manufactured and measured. The experiments show that indoor positioning can be done with our designed system by adding some hardware and updating in positioning algorithm to the conventional GPS receivers

An indoor positioning system based on global positioning system: design, implementation and analysis

Civil Global Positioning System (GPS) has become very popular in recent years and it has widespread use in many areas such as traffic management, medical emergency services and location finding in wireless handsets. Owing to the latest technological advances, GPS receivers are able to locate themselves with an error of 5 meters outdoors. Although GPS positioning is very successful in outdoor areas, it is hard to decode GPS signals indoors due to the additional signal loss caused by the buildings and walls. In this thesis, in order to solve indoor coverage problem, an indoor positioning system based on GPS infrastructure is proposed, designed and analyzed. Designed indoor positioning system consists of GPS repeaters and a GPS receiver with improved positioning algorithms. In order to analyze the proposed indoor positioning system, directional GPS antenna, GPS repeater with amplifiers is designed, manufactured and measured. Positioning algorithms are implemented and operated real time on live GPS data. All the system components are integrated and positioning is obtained for evaluation of the system performance. Results of the experiments show that the proposed system can be used for indoor positioning thus continuation of the GPS service can be expanded to indoors with a hardware addition to the buildings and a software update to the standard GPS receivers where indoor coverage is needed

Adaptive Image Classification on Mobile Phones

The advent of high-performance mobile phones has opened up the opportunity to develop new context-aware applications for everyday life. In particular, applications for context-aware information retrieval in conjunction with image-based object recognition have become a focal area of recent research. In this thesis we introduce an adaptive mobile museum guidance system that allows visitors in a museum to identify exhibits by taking a picture with their mobile phone. Besides approaches to object recognition, we present different adaptation techniques that improve classification performance. After providing a comprehensive background of context-aware mobile information systems in general, we present an on-device object recognition algorithm and show how its classification performance can be improved by capturing multiple images of a single exhibit. To accomplish this, we combine the classification results of the individual pictures and consider the perspective relations among the retrieved database images. In order to identify multiple exhibits in pictures we present an approach that uses the spatial relationships among the objects in images. They make it possible to infer and validate the locations of undetected objects relative to the detected ones and additionally improve classification performance. To cope with environmental influences, we introduce an adaptation technique that establishes ad-hoc wireless networks among the visitors’ mobile devices to exchange classification data. This ensures constant classification rates under varying illumination levels and changing object placement. Finally, in addition to localization using RF-technology, we present an adaptation technique that uses user-generated spatio-temporal pathway data for person movement prediction. Based on the history of previously visited exhibits, the algorithm determines possible future locations and incorporates these predictions into the object classification process. This increases classification performance and offers benefits comparable to traditional localization approaches but without the need for additional hardware. Through multiple field studies and laboratory experiments we demonstrate the benefits of each approach and show how they influence the overall classification rate.Die Einführung von Mobiltelefonen mit eingebauten Sensoren wie Kameras, GPS oder Beschleunigungssensoren, sowie Kommunikationstechniken wie Bluetooth oder WLAN ermöglicht die Entwicklung neuer kontextsensitiver Anwendungen für das tägliche Leben. Insbesondere Applikationen im Bereich kontextsensitiver Informationsbeschaffung in Verbindung mit bildbasierter Objekterkennung sind in den Fokus der aktuellen Forschung geraten. Der Beitrag dieser Arbeit ist die Entwicklung eines bildbasierten, mobilen Museumsführersystems, welches unterschiedliche Adaptionstechniken verwendet, um die Objekterkennung zu verbessern. Es wird gezeigt, wie Ojekterkennungsalgorithmen auf Mobiltelefonen realisiert werden können und wie die Erkennungsrate verbessert wird, indem man zum Beispiel ad-hoc Netzwerke einsetzt oder Bewegungsvorhersagen von Personen berücksichtigt