Building a personal symbolic space model from GSM CellID Positioning Data

Série : Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 7The context in which a person uses a mobile context-aware application can be described by many dimensions, including the, most popular, location and position. Some of the data used to describe these dimensions can be acquired directly from sensors or computed by reasoning algorithms. In this paper we propose to contextualize the mobile user of context-aware applications by describing his/her location in a symbolic space model as an alternative to the use of a position represented by a pair of coordinates in a geometric absolute referential. By exploiting the ubiquity of GSM networks, we describe a method to progressively create this symbolic and personal space model, and propose an approach to compute the level of familiarity a person has with each of the identified places. The validity of the developed model is evaluated by comparing the identified places and the computed values for the familiarity index with a ground truth represented by GPS data and the detailed agenda of a few persons

Navigation based on symbolic space models

Existing navigation systems are very appropriate for car navigation, but lack support for convenient pedestrian navigation and cannot be used indoors due to GPS limitations. In addition, the creation and the maintenance of the required models are costly and time consuming, and are usually based on proprietary data structures. In this paper we describe a navigation system based on a human inspired symbolic space model. We argue that symbolic space models are much easier to create and to maintain, and that they can support routing applications based on self-locating through the recognition of nearby features. Our symbolic space model is supported by a federation of servers where the spatial descriptions are stored, and which provide interfaces for feeding and querying the model. Local models residing in different servers may be connected between them, thus contributing to the system scalability.Fundação para a Ciência e a Tecnologia (FCT

Dealing with multiple source spatio-temporal data in urban dynamics analysis

Capturing, representing, modelling and visualizing the dynamics of urban mobility have been attracting the interest of the research community recently. One of the drivers for recent work in this area is the availability of large datasets representing many aspects of the urban dynamics. Applications for these studies are diverse and include urban planning, security, intelligent transportation systems and many others. Quite often, the proposed approaches are highly dependent on the data type. This paper describes the definition of a set of basic concepts for the representation and processing of spatio-temporal data, sufficiently flexible to deal with various types of mobility data and to support multiple forms of processing and visualization of the urban mobility. A place learning algorithm is also described to illustrate the flexibility of the proposed framework. Available results obtained by the integration of geometric and symbolic data reveal the adequacy of the proposed concepts, and uncover new possibilities for the fusion of heterogeneous datasets.Research group supported by FEDER Funds through the COMPETE and National Funds through FCT – Fundação para a Ciência e a Tecnologia under the Project: FCOMP-01-FEDER-0124-022674

Enhancing the location-context through inference over positioning data

The current ubiquity of wireless communication networks is enabling new ways to compute the position of mobile devices and their users. One good example is the Place Lab system that exploits the beacons broadcast by many wireless networks to estimate the geographic position of mobile users. Although position is a fundamental dimension in the user’s context, other location-related dimensions of the context are also important for the development of innovative context-aware applications. In this paper we propose a new architecture for context acquisition and management, as a new layer added over the Place Lab system, where inference techniques over the basic positioning data are used to enrich the context with new dimensions without compromising user privacy

Large scale movement analysis from WiFi based location data

Understanding and modeling the way humans move in urban contexts is beneficial for many applications. The recent advances on positioning technologies, namely those based on the ubiquity of wireless networks, is facilitating the observation of people for human motion analysis. In this paper we present the result of a large scale work conducted to study the human mobility in a University’s campuses. The study was conducted along several months, using data collected from thousands of users that freely moved inside the numerous buildings existent in two University campuses and a few other buildings in the city center. A Wi-Fi infrastructure of more than 550 access points provides Internet access to the academic community. We tracked the user movements by logging the devices connected to each access point. Based on that data, an analysis process that highlights the relationships between space features and human motion has been developed. In this paper we introduce the concepts of “place connectivity” and “flow across a boundary” to model these relationships. Results show the mobility patterns detected, which are the attraction places along the day, and what places are more strongly connected. This paper also includes an analysis of the short and long term movements between places. With this study we extended our understanding of the life in the campus, enabling us to feel the campus “pulse”.This work was supported by the FEDER program through the COMPETE and the Portuguese Science and Technology Foundation (FCT), within the context of projects SUM – Sensing and Understanding human Motion dynamics (reference PTDC/EIA-EIA/113933/2009) and TICE.Mobilidade (COMPETE 13843)

From the conception to the definition of a new service: the case of the European GeoPKDD project”

La tesi affronta il processo che parte dalla generazione di un nuovo servizio di tipo technology push ed arriva fino alla sua definizione, attraverso l’analisi del lavoro svolto per WIND Telecomunicazioni s.p.a. nell’ambito del progetto Europeo GeoPKDD. Dopo un inquadramento teorico sulle metodologie di sviluppo di nuovi servizi e sulle peculiarità di uno sviluppo technology push rispetto al caso market pull, il lavoro si concentra sul processo che, partendo dalla generazione di nuove idee basate sulla tecnologia GeoPKDD, si è concluso con la definizione delle specifiche finali da implementare nel servizio finale

Développement d'une méthode de géolocalisation à l'intérieur de bâtiments par classification des fingerprints GSM et fusion de données de capteurs embarqués

GPS has long been used for accurate and reliable outdoor localization, but it cannot operate in indoor environments, which suggests developing indoor localization methods that can provide seamless and ubiquitous services for mobile users.In this thesis, indoor localization is realized making use of received signal strength fingerprinting technique based on the existing GSM networks. A room is defined as the minimum location unit, and support vector machine are used as a mean to discriminate the rooms by classifying received signal strengths from very large number of GSM carriers. At the same time, multiple sensors, such as accelerometer and gyroscope, are widely available for modern mobile devices, which provide additional information that helps location determination. The hybrid approach that combines the GSM fingerprinting results with mobile sensor and building layout information using a particle filter provides a more accurate and fine-grained localization result.The results of experiments under realistic conditions demonstrate that correct room number can be obtained 94% of the time provided the derived model is used before significant received signal strength drift sets in. Furthermore, if the training data is sampled over a few days, the performance can remain stable exceeding 80% over a period of months, and can be further improved with various post-processing techniques. Moreover, including the mobile sensors allows the system to localize the mobile trajectory coordinates with high accuracy and reliability.L’objet de cette thèse est l’étude de la localisation et de la navigation à l’intérieur de bâtiments à l’aide des signaux disponibles dans les systèmes mobiles cellulaires et, en particulier, les signaux GSM.Le système GPS est aujourd’hui couramment utilisé en extérieur pour déterminer la position d’un objet, mais les signaux GPS ne sont pas adaptés à la localisation en intérieurIci, la localisation en intérieur est obtenue à partir de la technique des «empreintes» de puissance des signaux reçus sur les canaux utilisés par les réseaux GSM. Elle est réalisée à l’échelle de la pièce. Une classification est effectuée à partir de machines à vecteurs supports et les descripteurs utilisés sont les puissances de toutes les porteuses GSM. D’autres capteurs physiques disponibles dans les téléphones portables fournissent des informations utiles pour déterminer la position ou le déplacement de l’utilisateur. Celles-ci, ainsi que la cartographie de l’environnement, sont associées aux résultats obtenus à partir des «empreintes» GSM au sein de filtres particulaires afin d’obtenir une localisation plus précise, et sous forme de coordonnées continues.Les résultats obtenus montrent que l’utilisation des seules empreintes GSM permet de déterminer la pièce correcte dans 94% des cas sur une durée courte et que les performances restent stables pendant plusieurs mois, de l’ordre de 80%, si les données d’apprentissage sont enregistrées sur quelques jours. L’association de la cartographie du lieu et des informations issues des autres capteurs aux données de classification permettent d’obtenir les coordonnées de la trajectoire du système mobile avec une bonne précision et une bonne fiabilité

Facilitating the development of location-based experiences

Location-based experiences depend on the availability and reliability of wireless infrastructures such as GPS, Wi-Fi, or mobile phone networks; but these technologies are not universally available everywhere and anytime. Studies of deployed experiences have shown that the characteristics of wireless infrastructures, especially their limited coverage and accuracy, have a major impact on the performance of an experience. It is in the designers’ interest to be aware of technological restrictions to their work. Current state of the art authoring tools for location-based experiences implement one common overarching model: the idea of taking a map of the physical area in which the experience is to take place and then somehow placing virtual trigger zones on top of it. This model leaves no space for technological shortcomings and assumes a perfect registration between the real and the virtual. In order to increase the designers’ awareness of the technology, this thesis suggests revealing the wireless infrastructures at authoring time through appropriate tools and workflows. This is thought to aid the designers in better understanding the characteristics of the underlying technology and thereby enable them to deal with potential problems before their work is deployed to the public. This approach was studied in practice by working with two groups of professional artists who built two commercially commissioned location-based experiences, and evaluated using qualitative research methods. The first experience is a pervasive game for mobile phones called ‘Love City’ that relies on cellular positioning. The second experience is a pervasive game for cyclists called ‘Rider Spoke’ that relies on Wi-Fi positioning. The evaluation of these two experiences revealed the importance of an integrated suite of tools that spans indoors and outdoors, and which supports the designers in better understanding the location mechanism that they decided to work with. It was found that designers can successfully create their experiences to deal with patchy, coarse grained, and varying wireless networks as long as they are made aware of the characteristics

Sensing and Visualizing Social Context from Spatial Proximity

The concept of pervasive computing, as introduced by Marc Weiser under the name ubiquitous computing in the early 90s, spurred research into various kinds of context-aware systems and applications. There is a wide range of contextual parameters, including location, time, temperature, devices and people in proximity, which have been part of the initial ideas about context-aware computing. While locational context is already a well understood concept, social context---based on the people around us---proves to be harder to grasp and to operationalize. This work continues the line of research into social context, which is based on the proximity and meeting patterns of people in the physical space. It takes this research out of the lab and out of well controlled situations into our urban environments, which are full of ambiguity and opportunities. The key to this research is the tool that caused dramatic change in individual and collective behavior during the last 20 years and which is a manifestation of many of the ideas of the pervasive computing paradigm: the mobile phone. In this work, the mobile is regarded as a proxy for people. Through it, the social environment becomes accessible to digital measurement and processing. To understand the large amount of data that now becomes available to automatic measurement, we will turn to the discipline of social network analysis. It provides powerful methods, that are able to condense data and extract relevant meaning. Visualization helps to understand and interpret the results. This thesis contains a number of experiments, that demonstrate how the automatic measurement of social proximity data through Bluetooth can be used to measure variables of personal behavior, group behavior and the behavior of groups in relation to places. The principal contributions are: * A methodology to visualize personal social context by using an ego proximity network. Specific episodes can be localized and compared. * method to compare different days in terms of social context, e.g. to support automatic diary applications. * A method to compose social geographic maps. Locations of similar social context are detected and combined. * Functions to measure short-term changes in social activity, based on the distinction between strange and familiar devices. * The characterization of Bluetooth inquiries for social proximity sensing. * A dataset of Bluetooth sightings from an ego perspective in seven different settings. Additionally, some settings feature multiple stationary scanners and Cell-ID measurements. * Soft- and hardware to capture, collect, store and analyze Bluetooth proximity data