10 research outputs found
A Phased Model for Network Selection based on Context
Projecte realitzat en col.laboració amb Fraunhofer FOKUS. TU BerlinL’accés a Internet s’ha convertit avui en dia en una necessitat degut a la facilitat i
comoditat que ens produeix poder enviar un mail, fer una transferència bancària,
consultar informació de tot tipus des de qualsevol lloc. El major ús de totes aquestes
aplicacions, juntament amb les múltiples possibilitats de connexió que ens ofereixen
les xarxes WiFi, WIMAX, UMTS entre d’altres, fan que sigui objecte d’estudi millorar
l’eficiència i donar més facilitats a l’usuari.
Una vegada que l’usuari selecciona una xarxa per tenir accés a Internet, els
requeriments i capacitats i la qualitat de la mateixa van canviant atenent als canvis
produïts en l’entorn. El que aquí ens interessa és saber quina xarxa és la millor en cada
moment tenint en compte els esmentats canvis. D’aquesta manera això fitxa les bases
en la creació d’un sistema capaç d’autogestionar‐se i autoconfigurar‐se.
El principal objectiu d’estudi és el desenvolupament d’un mecanisme de decisió que
executi de forma semblant a un handover vertical, un canvi de xarxa automàtic.
D’aquesta manera l’usuari té l’avantatge de gaudir de la xarxa que li proporcioni millor
connexió possible atenent als canvis de l’entorn a cada instant. Per una altra banda,
l’usuari pot treballar sense haver de preocupar‐se de la connectivitat amb
independència del lloc on es trobi, de si està en moviment, dels requeriments de
l’aplicació a executar, etc.
Dintre de l’àmbit de les comunicacions autonòmiques l’actual recerca de les xarxes
cognitives (cognitive networks) és el mitjà adequat per desenvolupar aquesta tasca.
Les xarxes cognitives apareixen com a resposta a la limitació de l’espectre i a la
ineficiència del seu ús. La característica principal d’aquestes xarxes és que poden
adaptar dinàmicament els seus paràmetres com a resposta de les necessitats dels
usuaris o dels canvis en les condicions de l’entorn. Així mateix aquestes xarxes tenen la
capacitat d’aprendre de les adaptacions realitzades i aprofitar aquest coneixement per prendre futures decisions basades en la experiència.
L’escenari en el que es centra el nostre treball és l’anomenat Always Best Connected,
concepte que es refereix no només a que l’usuari estigui sempre connectat, sinó
connectat de la millor manera possible.
Per portar a terme tot l’esmentat previament hem estudiat les característiques de
diferents models procedents d’àrees com: autonomic communications, autonomic
computing, robòtica i informàtica. A més a més hem intentat extreure influències
procedents de camps originalment no tan lligats a les tecnologies, com són la
psicologia i la pedagogia, per tal de intentar aprofundir en el desenvolupament
d’aquests sistemes amb l’ajuda de models utilitzats pels humans.
Les característiques més valuoses que hem trobat són la simplicitat, l’ús de context, la capacitat d’aprenentatge i la reconfigurabilitat. De cada model s’han extret trets valuosos pel posterior disseny de la nostra solució. Aquests models van des d’una
solució robòtica basada en un simple esquema “reb‐tracta‐actua” desenvolupat a
mitjans dels 80, fins a les darreres aportacions oferides per universitats i centres de investigació i desenvolupament en el camp de les comunicacions autònomes.
En quant a tasques i blocs comuns, trobem pràcticament en tots els models estudiats
els següents mòduls: captació de dades (sensing) i monotorització, anàlisi, ús de
context o informació de l’entorn, presa de decisions, aprenentatge i execució. El bloc
sensing és la via que utilitza el sistema per apreciar la realitat física de l’exterior.
Mitjançant múltiples tipus de dades mesurades, així com moviment, xarxes
disponibles, mètriques i topologies utilitza data‐aggregation per eliminar informació
redundant.
El mòdul d’anàlisi és el responsable de trobar els mecanismes necessaris per
interpretar les dades rebudes, filtrar‐les i correlar‐les per tal de descriure una situació.
Aquest bloc fa ús d’ontologies per representar i compartir el model de la realitat adquirit amb totes les entitats amb les que està interrelacionat. Finalment avalua
l’impacte de les decisions preses i coordina les interaccions entre els diferents sistemes
autonòmics.
Quan parlem de context ens referim a un terme essencial en la presa de decisions.
Aquest terme és utilitzat per donar un millor coneixement sobre la informació de que
es disposa, tenint en compte les circumstàncies de cada cas.
El terme context i el seu comportament són el centre d’estudi de diferents projectes
desenvolupats per la Unió Europea com són: Ambient Networks, SPICE, E2R (End to
End Reconfigurability) i MobiLife. En tots ells la utilització del context es fa en diferents capes i diferents ontologies. La recerca d’aquests projectes europeus englobats en el
Sixth Framework Programme tenen com a finalitat utilitzar el context de les situacions
per tal de donar un millor producte als usuaris finals.
La presa de decisions està formada per un grup de regles associades a un conjunt
d’accions emmagatzemades dintre d’una base de dades. Aquest mòdul proveeix al
sistema dels mecanismes i algoritmes necessaris per trobar l’acció a realitzar en cada moment. Aquí juga un factor important la interpretació de les dades rebudes en base
al context i al coneixement de la situació per aconseguir la decisió idònia.
L’aprenentatge dintre d’aquest sistema és vist com un conjunt de mecanismes que
utilitza l’experiència per predir accions futures. Aquesta capacitat és important per
millorar el rendiment i l’efectivitat de decisions passades i descobrir noves relacions per a la construcció d’un millor coneixement.
Com a darrer bloc considerem la funció d’execució com la més senzilla, que només du
a terme les decisions preses anteriorment. Una vegada executada l’acció indicada,
informa pertinentment als usuaris o administradors del resultat de les accions
realitzades.
Hem de destacar que el Phased Model for Network Selection based on context està
composat per quatre blocs: Sensing, Anàlisi‐Presa de decisions, Aprenentatge i
Execució. El que destaca respecte dels models prèviament avaluats és la construcció
d’un bloc capaç d’analitzar i prendre decisions simultàniament. Això és degut a
l’estreta col∙laboració entre ambdós mòduls amb l’objectiu d’agilitzar el rendiment.
També cal remarcar el continu feedback loop entre aquest complex mòdul i la base de
dades que emmagatzema la nova informació aportada pel bloc d’aprenentatge.
Una vegada dissenyat el model proposat, faltaria implementar pràcticament una
solució i simular la viabilitat del sistema. En aquest sentit són molts els estudis teòrics fets dins aquest camp, però encara no s’ha trobat resultat pràctic
Learning preferences for personalisation in a pervasive environment
With ever increasing accessibility to technological devices, services and applications there is also an increasing burden on the end user to manage and configure such resources. This burden will continue to increase as the vision of pervasive environments, with ubiquitous access to a plethora of resources, continues to become a reality. It is key that appropriate mechanisms to relieve the user of such burdens are developed and provided. These mechanisms include personalisation systems that can adapt resources on behalf of the user in an appropriate way based on the user's current context and goals. The key knowledge base of many personalisation systems is the set of user preferences that indicate what adaptations should be performed under which contextual situations.
This thesis investigates the challenges of developing a system that can learn such preferences by monitoring user behaviour within a pervasive environment. Based on the findings of related works and experience from EU project research, several key design requirements for such a system are identified. These requirements are used to drive the design of a system that can learn accurate and up to date preferences for personalisation in a pervasive environment. A standalone prototype of the preference learning system has been developed. In addition the preference learning system has been integrated into a pervasive platform developed through an EU research project. The preference learning system is fully evaluated in terms of its machine learning performance and also its utility in a pervasive environment with real end users
Introducing the user to the service creation world: concepts for user centric service creation, personalization and notification
The “Web 2.0” feature that most permeates the
nowadays web is “user-centricity”. Now users are not only
consumers of items (software, information, etc.), but also
creators of those items. This paper intends to push this paradigm
further, targeting mashups of telco and web services in a unique
service environment where personalised services will be
dynamically created and provisioned by end-users themselves,
regardless of ambiance and location. The paper explains how
user-centricity can be applied to the service creation world and
in general to the overall service lifecycle process. It also describes
the platform being implemented in the OPUCE project that
captures this philosophy and will be submitted to end-user
validation. Whilst focusing on intuitive editors for end-users to
compose services, additional hints are provided about
personalization and notification approaches to improve user
centricity
Context-Aware Service Creation On The Semantic Web
With the increase of the computational power of mobile devices, their new capabilities and the addition of new context sensors, it is possible to obtain more information from mobile users and to offer new ways and tools to facilitate the content creation process. All this information can be exploited by the service creators to provide mobile services with higher degree of personalization that translate into better experiences. Currently on the web, many data sources containing UGC provide access to them through classical web mechanisms (built on a small set of standards), that is, custom web APIs that promote the fragmentation of the Web. To address this issue, Tim Berners-Lee proposed the Linked Data principles to provide guidelines for the use of standard web technologies, thus allowing the publication of structured on the Web that can be accessed using standard database mechanisms. The increase of Linked Data published on the web, increases opportunities for mobile services take advantage of it as a huge source of data, information and knowledge, either user-generated or not. This dissertation proposes a framework for creating mobile services that exploit the context information, generated content of its users and the data, information and knowledge present on the Web of Data. In addition we present, the cases of different mobile services created to take advantage of these elements and in which the proposed framework have been implemented (at least partially). Each of these services belong to different domains and each of them highlight the advantages provided to their end user
Building Blocks for IoT Analytics Internet-of-Things Analytics
Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)
Building Blocks for IoT Analytics Internet-of-Things Analytics
Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)
MobiLife Service Infrastructure and SPICE Architecture Principles
MobiLife (IST-2004-511607) and SPICE (IST-2005027617) projects research for new applications, services, platforms and related architectures to provide users with an excellent user experience within future communication environments. This paper gives an overview of the service and application space for next generation mobile service provisioning and presents the MobiLife service infrastructure and SPICE architecture principles