Situation Goodness Method for Weighted Centroid-Based Wi-Fi APs Localization

Knowing the location of Wi-Fi antennas may be critical for indoor localization. However, in a real environment, their positions may be unknown since they can be managed by external entities. This paper introduces a new method for evaluating the suitability of using the weighted centroid method for the 2D localization of a Wi-Fi AP. The method is based on the idea that the weighted centroid method provides its best results when there are fingerprints taken around the AP. In order to find the probability of being in the presence of such situations, a natural neighbor interpolation method is used to find the regions with the highest signal strengths. A geometrical method is then used to characterize that probability based on the distribution of those regions in relation to the AP position estimation given by the weighted centroid method. The paper describes the testing location and the used Wi-Fi fingerprints database. That database is used to create new databases that recreate different sampling possibilities through a samples deletion strategy. The original database and the newly created ones are then used to evaluate the localization results of several AP localization methods and the new method proposed in this paper. The evaluation results have shown that the proposed method is able to provide a proper probability for the suitability of using the weighted centroid method for localizing a Wi-Fi AP

Algoritmos de optimización basados en capacidad y cobertura de redes inalámbricas para la infraestructura de medición avanzada de energía eléctrica

The advanced metering infrastructure AMI increasingly takes strength and domain in the world of smart grids, offering numerous applications in different fields: to save human lives, public and private property, to provide an automated environment and comfort for users such networks, etcetera. In this context, smart metering has a major role, and the optimal sizing of a network for it even more, so this paper presents a detailed degree of analysis optimization methods regarding how to group items and It proposes a comparison clustering algorithms in order to determine the most efficient algorithm to cover the capacity of link with smart meters - characteristic of each AP (Access Point) - and coverage - characteristic of each communication standard-, for a smart meters network based on wireless communication standard WiFi. As a result of this analysis it is to obtain the characteristics of each method analyzed under the same conditions both coverage and capacity expressed through graphics and establish the respective conclusions. That is why this research will focus on defining, modeling and simulation methods using K-means clustering, K-medoids and ILP (Integer Linear Programming) expressed as mathematical algorithms in a (500) smart meters network in an Urban residential area which we will call users, considering each method restrictions and objective functions of the network, ensuring a coverage rate of 100% of users located in clusters (groups) and thus providing an optimal solution in terms of time, fewer groups, and equitable distribution of smart meters in the different groups.La infraestructura de medición avanzada AMI toma cada vez con mayor fuerza, dominio en el mundo de las redes inteligentes, brindando numerosas aplicaciones en diferentes campos: para salvar vidas humanas, propiedad pública y privada, para ofrecer un ambiente automatizado y de confort para los usuarios de dichas redes, entre otras. En este ámbito, la medición inteligente tiene un rol trascendental, y el dimensionamiento óptimo de una red para la misma aún más, por lo que el presente trabajo de titulación presenta un análisis detallado sobre métodos de optimización en cuanto a la forma de agrupar ítems y propone una comparación entre algoritmos de agrupación con el fin de determinar el método más eficiente para cubrir tanto en capacidad de enlazar medidores inteligentes - característica propia de cada AP (Access Point)- como en la cobertura - característica propia de cada estándar de comunicación, para una red de medidores inteligentes planteada basada en el estándar de comunicación Wifi. Como consecuencia de este análisis se pretende obtener las características de cada método analizados en las mismas condiciones tanto de cobertura como de capacidad expresadas mediante gráficas y establecer las conclusiones respectivas. Es por esto que esta investigación se centrará en definir, modelar y simular mediante los métodos de agrupación k-means, k-medoids e ILP (Programación Lineal Entera por sus siglas en inglés) expresados como algoritmos matemáticos en una red de quinientos (500) medidores inteligentes en un área residencial urbana a los cuales denominaremos usuarios, teniendo presente para cada método las restricciones y funciones objetivo de dicha red, garantizando un índice de cobertura del 100% de usuarios ubicados en los clústeres (agrupaciones) y de esta manera brindando una solución óptima en cuanto a tiempo, menor número de agrupaciones, y distribución equitativa de medidores en los diferentes grupos

Investigation of indoor positioning based on WLAN 802.11

The need for location based services has dramatically increased within the past few years, especially with the popularity and capability of mobile device such as smart phones and tablets. The limitation of GPS for indoor positioning has seen an increase of indoor positioning based on Wireless Local Area Network 802.11.\ud This thesis reviews the various different techniques used by applications to determine one’s location through the measurement of Wi-Fi signals. It particularly focuses on the Cisco Context-Aware Mobility which provides a Real Time Location System solution based on Wi-Fi. It details the implementation of an Android application, developed to communicate with the Cisco Context-Aware Mobility to visually display the location of the mobile device. The application was tested in a production environment. Limitations in the production environment along with the diagnostic capabilities of the Context-Aware Mobility were identified

Adaptive indoor positioning system based on locating globally deployed WiFi signal sources

Recent trends in data driven applications have encouraged expanding location awareness to indoors. Various attributes driven by location data indoors require large scale deployment that could expand beyond specific venue to a city, country or even global coverage. Social media, assets or personnel tracking, marketing or advertising are examples of applications that heavily utilise location attributes. Various solutions suggest triangulation between WiFi access points to obtain location attribution indoors imitating the GPS accurate estimation through satellites constellations. However, locating signal sources deep indoors introduces various challenges that cannot be addressed via the traditional war-driving or war-walking methods. This research sets out to address the problem of locating WiFi signal sources deep indoors in unsupervised deployment, without previous training or calibration. To achieve this, we developed a grid approach to mitigate for none line of site (NLoS) conditions by clustering signal readings into multi-hypothesis Gaussians distributions. We have also employed hypothesis testing classification to estimate signal attenuation through unknown layouts to remove dependencies on indoor maps availability. Furthermore, we introduced novel methods for locating signal sources deep indoors and presented the concept of WiFi access point (WAP) temporal profiles as an adaptive radio-map with global coverage. Nevertheless, the primary contribution of this research appears in utilisation of data streaming, creation and maintenance of self-organising networks of WAPs through an adaptive deployment of mass-spring relaxation algorithm. In addition, complementary database utilisation components such as error estimation, position estimation and expanding to 3D have been discussed. To justify the outcome of this research, we present results for testing the proposed system on large scale dataset covering various indoor environments in different parts of the world. Finally, we propose scalable indoor positioning system based on received signal strength (RSSI) measurements of WiFi access points to resolve the indoor positioning challenge. To enable the adoption of the proposed solution to global scale, we deployed a piece of software on multitude of smartphone devices to collect data occasionally without the context of venue, environment or custom hardware. To conclude, this thesis provides learning for novel adaptive crowd-sourcing system that automatically deals with tolerance of imprecise data when locating signal sources

A STUDY ON RECEIVED SIGNAL STRENGTH BASED INDOOR LOCALIZATION

Ph.DDOCTOR OF PHILOSOPH

SECURE TRACKING SYSTEM FOR NEXT GENERATION CIT PRODUCTS

The Cash in Transit (CIT) industry demands reliable and innovative products from its suppliers to ensure safety and reliability within the industry. Product innovation has been directed at a bespoke tracking system for the Cash in Transit industry, which can meet its stringent requirements and excel above the capabilities of standard, readily available tracking systems. The presented research has investigated the state of the art in tracking and localisation systems and has highlighted Wi-Fi as a potential novel Cash in Transit tracking solution. With research into 2.4GHz Wi-Fi and the effects in a CIT environment, the technology has been understood and demonstrated in terms of its advantages and weaknesses when applied to CIT. The research has shown that 2.4GHz Wi-Fi is a novel and viable solution for both wide area tracking and localised tracking of a Cash in Transit security box by testing innovative ways of detecting theft using 2.4GHz Wi-Fi in a set of specific real-world scenarios. An embedded tracking system was developed and a thorough evaluation undertaken using a series of practical usage scenarios. The results show the proposed tracking capability is very effective and ready for initial effective use within a Cash in Transit security box