Indoor Positioning Techniques Based on Wireless LAN

As well as delivering high speed internet, Wireless LAN (WLAN) can be used as an effective indoor positioning system. It is competitive in terms of both accuracy and cost compared to similar systems. To date, several signal strength based techniques have been proposed. Researchers at the University of New South Wales (UNSW) have developed several innovative implementations of WLAN positioning systems. This paper describes the techniques used and details the experimental results of the research

Pedestrain Monitoring System Using Wi-Fi Technology and RSSI Based Localization

This paper presentsa new simple mobile tracking system based on IEEE802.11 wireless signal detection, which can be used for analyzingthe movement of pedestrian traffic. Wi-Fi packets emitted by Wi-Fi enabled smartphones are received at a monitoring station and these packets contain date, time, MAC address, and other information. The packets are received at a number of stations, distributed throughout the monitoring zone, which can measure the received signal strength. Based on the location of stations and data collected at the stations, the movement of pedestrian traffic can be analyzed. This information can be used to improve the services, such as better bus schedule time and better pavement design. In addition, this paper presents a signal strength based localization method

A comparative survey of WLAN location fingerprinting methods

The term “location fingerprinting” covers a wide variety of methods for determining receiver position using databases of radio signal strength measurements from different sources. In this work we present a survey of location fingerprinting methods, including deterministic and probabilistic methods for static estimation, as well as filtering methods based on Bayesian filter and Kalman filter. We present a unified mathematical formulation of radio map database and location estimation, point out the equivalence of some methods from the literature, and present some new variants. A set of tests in an indoor positioning scenario using WLAN signal strengths is performed to determine the influence of different calibration and location method parameters. In the tests, the probabilistic method with the kernel function approximation of signal strength histograms was the best static positioning method. Moreover, all filters improved the results significantly over the static methods.Peer reviewe

RSSI-Based Smooth Localization for Indoor Environment

Radio frequency (RF) technique, for its better penetrability over traditional techniques such as infrared or ultrasound, is widely used for indoor localization and tracking. In this paper, three novel measurements, point decision accuracy, path matching error and wrong jumping ratio, are firstly defined to express the localization efficiency. Then, a novel RSSI-based smooth localization (RSL) algorithm is designed, implemented, and evaluated on the WiFi networks. The tree-based mechanism determines the current position and track of the entity by assigning the weights and accumulative weights for all collected RSSI information of reference points so as to make the localization smooth. The evaluation results indicate that the proposed algorithm brings better localization smoothness of reducing 10% path matching error and 30% wrong jumping ratio over the RADAR system

Análisis de medidas de potencia en interiores para su aplicación en sistemas de localización basados en la técnica del fingerprinting

Esta tesis está relacionada con la técnica de localización en interiores denominada fingerprinting. La localización en interiores está sufriendo un gran avance en los últimos años debido a una creciente demanda de los servicios de valor añadido en los terminales móviles. Muchos de estos tienen que ver con el posicionamiento contextual del usuario. La técnica del fingerprinting es una de las más utilizadas en los sistemas de localización en interiores. Está técnica utiliza la relación entre los niveles de potencia recibidos en el móvil de los diferentes puntos de acceso de una red inalámbrica y los niveles de potencia en una serie de puntos, conocidos como huellas, cuya posición es conocida. Ese conjunto de puntos se conoce como radiomap. Por tanto es de gran importancia tener valores precisos de potencia en las huellas con el fin de tener una buena precisión en la localización. Sin embargo debido a las características de la propagación en interiores esto es complicado debido a la aleatoriedad de la señal recibida. En esta tesis se ha diseñado una pequeña red de localización constituida por unos pocos puntos de acceso y un dispositivo móvil. Con esta red se han realizado diferentes medidas en diferentes escenarios en diferentes situaciones ambientales. Un factor al que se ha prestado especial atención es la presencia humana en los escenarios de medidas. El objetivo ha sido estudiar la influencia de diversos factores sobre la variabilidad de los niveles de potencia medidos en diversas posiciones o huellas, y por tanto como puede variar la precisión de la localización en las diferentes situaciones. También se ha implementado algoritmos de clusterización para separar los valores de potencia medidos en grupos o clusters. La idea es asociar una huella a cada cluster. Entre las aplicaciones que esto tiene está en comprobar si el sistema de localización está bien diseñado viendo que los clusters coinciden con las huellas. También se puede diseñar un algoritmo de localización basado en la identificación de los valores de potencia recibidos en el móvil con un determinado cluster, y por tanto con una huella. Para realizar esto se han implementado los algoritmos k-medias y rek-medias y se han aplicado a un conjunto de potencias medido en un determinado radiomap

A Framework for Dynamic Traffic Monitoring Using Vehicular Ad-Hoc Networks

Traffic management centers (TMCs) need high-quality data regarding the status of roadways for monitoring and delivering up-to-date traffic conditions to the traveling public. Currently this data is measured at static points on the roadway using technologies that have significant maintenance requirements. To obtain an accurate picture of traffic on any road section at any time requires a real-time probe of vehicles traveling in that section. We envision a near-term future where network communication devices are commonly included in new vehicles. These devices will allow vehicles to form vehicular networks allowing communication among themselves, other vehicles, and roadside units (RSUs) to improve driver safety, provide enhanced monitoring to TMCs, and deliver real-time traffic conditions to drivers. In this dissertation, we contribute and develop a framework for dynamic trafficmonitoring (DTMon) using vehicular networks. We introduce RSUs called task organizers (TOs) that can communicate with equipped vehicles and with a TMC. These TOs can be programmed by the TMC to task vehicles with performing traffic measurements over various sections of the roadway. Measurement points for TOs, or virtual strips, can be changed dynamically, placed anywhere within several kilometers of the TO, and used to measure wide areas of the roadway network. This is a vast improvement over current technology. We analyze the ability of a TO, or multiple TOs, to monitor high-quality traffic datain various traffic conditions (e.g., free flow traffic, transient flow traffic, traffic with congestion, etc.). We show that DTMon can accurately monitor speed and travel times in both free-flow and traffic with transient congestion. For some types of data, the percentage of equipped vehicles, or the market penetration rate, affects the quality of data gathered. Thus, we investigate methods for mitigating the effects of low penetration rate as well as low traffic density on data quality using DTMon. This includes studying the deployment of multiple TOs in a region and the use of oncoming traffic to help bridge gaps in connectivity. We show that DTMon can have a large impact on traffic monitoring. Traffic engineers can take advantage of the programmability of TOs, giving them the ability to measure traffic at any point within several km of a TO. Most real-time traffic maps measure traffic at midpoint of roads between interchanges and the use of this framework would allow for virtual strips to be placed at various locations in between interchanges, providing fine-grained measurements to TMCs. In addition, the measurement points can be adjusted as traffic conditions change. An important application of this is end-of-queue management. Traffic engineers are very interested in deliver timely information to drivers approaching congestion endpoints to improve safety. We show the ability of DTMon in detecting the end of the queue during congestion

Algoritmos probabilísticos para WiFi Fingerprinting

Dissertação de mestrado integrado em Engenharia de Telecomunicações e InformáticaA técnica Wi-Fi Fingerprinting é uma técnica amplamente utilizada no posicionamento em interiores. Através desta técnica é possível determinar a posição do dispositivo, combinando os valores da intensidade do sinal recebidos com os valores da intensidade do sinal pré-adquiridos, presentes numa base de dados. O grande problema desta técnica é que, ao longo do tempo o cenário vai sofrendo várias alterações, condicionando a estimativa do posicionamento. Já foram propostos vários algoritmos de localização baseados em fingerprinting, sendo o mais popular o algoritmo k Nearest Neighbors (KNN). O propósito desta dissertação centra-se em construir novos algoritmos que permitam estimar o posicionamento, baseados na técnica Wi-Fi fingerprinting. São abordados nesta dissertação dois tipos de algoritmos, algoritmos determinísticos e algoritmos probabilísticos, com o intuito de avaliar o desempenho de cada um deles em ambientes indoor. Entre os algoritmos determinísticos, foi escolhido e implementado um algoritmo hierárquico já existente. Este algoritmo inclui três etapas distintas, nomeadamente a identificação do edifício, depois do respetivo piso e finalmente a estimativa da localização. Tendo em conta o ambiente em estudo, este algoritmo hierárquico apresenta resultados satisfatórios, sendo utilizado como referência na análise de desempenho dos restantes algoritmos aqui apresentados. Ainda nos algoritmos determinísticos, são efetuadas propostas de alteração ao algoritmo hierárquico de forma a melhorar os resultados. Relativamente aos algoritmos probabilísticos, são descritas e implementadas três variantes. Estas três variantes calculam a probabilidade de uma fingerprint pertencer a um determinado local, utilizando diferentes metodologias. A primeira variante, faz uso de uma distribuição baseada em histogramas. É construído um histograma de valores da intensidade do sinal para cada ponto de acesso de uma fingerprint. A segunda variante recorre à probabilidade de um ponto de acesso ter sido observado numa determinada posição. A terceira variante utiliza a função gaussiana de Kernel para cada ponto de acesso. Todos estes algoritmos, tanto os determinísticos como os probabilísticos foram testados recorrendo a datasets de dados reais, que permitiram obter os resultados descritos neste documento.Wi-Fi Fingerprinting is a widely used technique in interior positioning systems. Due to this technique it is possible to determine the position of a device, combining the values of the received signal intensity with the values of the signals intensity pre-acquired from a database. The main problem of this technique is that, over the time the scenario suffer several changes conditioning the estimated position. There have been proposed several localization algorithms based in fingerprinting in which the most popular is the k Nearest Neighbors algorithm. This dissertation focuses on developing new algorithms that permit the estimation of the positioning, based in the Wi-Fi fingerprint technique. In this dissertation we make two approaches, deterministic algorithms and probabilistic algorithms, with the aim to evaluate the performance of each one in indoor environments. Between the deterministic algorithms, an existent hierarchical algorithm was chosen and then implemented. This algorithm includes three different steps, the building identification, the floor identification and finally the estimated localization. Taking into account the study environment, this hierarchical algorithm shows decent results, so it is used as a reference in the performance analyses of the other algorithms presented here. Still in the deterministic algorithms, it is made several proposals to modify the hierarchical algorithm in order to improve the results. Relatively to the probabilistic algorithms it is described and implemented three variants. These three variants calculate the probability of a fingerprint belong to a particular location, using several methodologies. The first uses distribution histograms. It is built an histogram of the signal intensity values for each access point of a fingerprint. The second resorts on the probability of an access point being observed in a certain position. The third uses the Kernel’s gaussian function for each access point. All of these algorithms, both deterministic as probabilistic were tested using datasets of real data, that permitted to obtain the results described in this document

Interference charecterisation, location and bandwidth estimation in emerging WiFi networks

Wireless LAN technology based on the IEEE 802.11 standard, commonly referred to as WiFi, has been hugely successful not only for the last hop access to the Internet in home, office and hotspot scenarios but also for realising wireless backhaul in mesh networks and for point -to -point long- distance wireless communication. This success can be mainly attributed to two reasons: low cost of 802.11 hardware from reaching economies of scale, and operation in the unlicensed bands of wireless spectrum.The popularity of WiFi, in particular for indoor wireless access at homes and offices, has led to significant amount of research effort looking at the performance issues arising from various factors, including interference, CSMA/CA based MAC protocol used by 802.11 devices, the impact of link and physical layer overheads on application performance, and spatio-temporal channel variations. These factors affect the performance of applications and services that run over WiFi networks. In this thesis, we experimentally investigate the effects of some of the above mentioned factors in the context of emerging WiFi network scenarios such as multi- interface indoor mesh networks, 802.11n -based WiFi networks and WiFi networks with virtual access points (VAPs). More specifically, this thesis comprises of four experimental characterisation studies: (i) measure prevalence and severity of co- channel interference in urban WiFi deployments; (ii) characterise interference in multi- interface indoor mesh networks; (iii) study the effect of spatio-temporal channel variations, VAPs and multi -band operation on WiFi fingerprinting based location estimation; and (iv) study the effects of newly introduced features in 802.11n like frame aggregation (FA) on available bandwidth estimation.With growing density of WiFi deployments especially in urban areas, co- channel interference becomes a major factor that adversely affects network performance. To characterise the nature of this phenomena at a city scale, we propose using a new measurement methodology called mobile crowdsensing. The idea is to leverage commodity smartphones and the natural mobility of people to characterise urban WiFi co- channel interference. Specifically, we report measurement results obtained for Edinburgh, a representative European city, on detecting the presence of deployed WiFi APs via the mobile crowdsensing approach. These show that few channels in 2.4GHz are heavily used and there is hardly any activity in the 5GHz band even though relatively it has a greater number of available channels. Spatial analysis of spectrum usage reveals that co- channel interference among nearby APs operating in the same channel can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city -scale are similar to those of WiFi deployments in public spaces of different indoor environments. We validate our approach in comparison with wardriving, and also show that our findings generally match with previous studies based on other measurement approaches. As an application of the mobile crowdsensing based urban WiFi monitoring, we outline a cloud based WiFi router configuration service for better interference management with global awareness in urban areas.For mesh networks, the use of multiple radio interfaces is widely seen as a practical way to achieve high end -to -end network performance and better utilisation of available spectrum. However this gives rise to another type of interference (referred to as coexistence interference) due to co- location of multiple radio interfaces. We show that such interference can be so severe that it prevents concurrent successful operation of collocated interfaces even when they use channels from widely different frequency bands. We propose the use of antenna polarisation to mitigate such interference and experimentally study its benefits in both multi -band and single -band configurations. In particular, we show that using differently polarised antennas on a multi -radio platform can be a helpful counteracting mechanism for alleviating receiver blocking and adjacent channel interference phenomena that underlie multi -radio coexistence interference. We also validate observations about adjacent channel interference from previous studies via direct and microscopic observation of MAC behaviour.Location is an indispensable information for navigation and sensing applications. The rapidly growing adoption of smartphones has resulted in a plethora of mobile applications that rely on position information (e.g., shopping apps that use user position information to recommend products to users and help them to find what they want in the store). WiFi fingerprinting is a popular and well studied approach for indoor location estimation that leverages the existing WiFi infrastructure and works based on the difference in strengths of the received AP signals at different locations. However, understanding the impact of WiFi network deployment aspects such as multi -band APs and VAPs has not received much attention in the literature. We first examine the impact of various aspects underlying a WiFi fingerprinting system. Specifically, we investigate different definitions for fingerprinting and location estimation algorithms across different indoor environments ranging from a multi- storey office building to shopping centres of different sizes. Our results show that the fingerprint definition is as important as the choice of location estimation algorithm and there is no single combination of these two that works across all environments or even all floors of a given environment. We then consider the effect of WiFi frequency bands (e.g., 2.4GHz and 5GHz) and the presence of virtual access points (VAPs) on location accuracy with WiFi fingerprinting. Our results demonstrate that lower co- channel interference in the 5GHz band yields more accurate location estimation. We show that the inclusion of VAPs has a significant impact on the location accuracy of WiFi fingerprinting systems; we analyse the potential reasons to explain the findings.End -to -end available bandwidth estimation (ABE) has a wide range of uses, from adaptive application content delivery, transport-level transmission rate adaptation and admission control to traffic engineering and peer node selection in peer -to- peer /overlay networks [ 1, 2]. Given its importance, it has been received much research attention in both wired data networks and legacy WiFi networks (based on 802.11 a/b /g standards), resulting in different ABE techniques and tools proposed to optimise different criteria and suit different scenarios. However, effects of new MAC/PHY layer enhancements in new and next generation WiFi networks (based on 802.11n and 802.11ac standards) have not been studied yet. We experimentally find that among different new features like frame aggregation, channel bonding and MIMO modes (spacial division multiplexing), frame aggregation has the most harmful effect as it has direct effect on ABE by distorting the measurement probing traffic pattern commonly used to estimate available bandwidth. Frame aggregation is also specified in both 802.11n and 802.1 lac standards as a mandatory feature to be supported. We study the effect of enabling frame aggregation, for the first time, on the performance of the ABE using an indoor 802.11n wireless testbed. The analysis of results obtained using three tools - representing two main Probe Rate Model (PRM) and Probe Gap Model (PGM) based approaches for ABE - led us to come up with the two key principles of jumbo probes and having longer measurement probe train sizes to counter the effects of aggregating frames on the performance of ABE tools. Then, we develop a new tool, WBest+ that is aware of the underlying frame aggregation by incorporating these principles. The experimental evaluation of WBest+ shows more accurate ABE in the presence of frame aggregation.Overall, the contributions of this thesis fall in three categories - experimental characterisation, measurement techniques and mitigation/solution approaches for performance problems in emerging WiFi network scenarios. The influence of various factors mentioned above are all studied via experimental evaluation in a testbed or real - world setting. Specifically, co- existence interference characterisation and evaluation of available bandwidth techniques are done using indoor testbeds, whereas characterisation of urban WiFi networks and WiFi fingerprinting based location estimation are carried out in real environments. New measurement approaches are also introduced to aid better experimental evaluation or proposed as new measurement tools. These include mobile crowdsensing based WiFi monitoring; MAC/PHY layer monitoring of co- existence interference; and WBest+ tool for available bandwidth estimation. Finally, new mitigation approaches are proposed to address challenges and problems identified throughout the characterisation studies. These include: a proposal for crowd - based interference management in large scale uncoordinated WiFi networks; exploiting antenna polarisation diversity to remedy the effects of co- existence interference in multi -interface platforms; taking advantage of VAPs and multi -band operation for better location estimation; and introducing the jumbo frame concept and longer probe train sizes to improve performance of ABE tools in next generation WiFi networks