Δομή λεξικού για ασύρματα δίκτυα

Η κατανάλωση ενέργειας και η αποδοτηκότητα πρόσβασης είναι δύο βασικοί στόχοι και ανταγωνιστικοί στην ασύρματη εκπομπή δεδομένων. Για να αντιμετωπιστεί το ενεργειακό πρόβλημα στην ακολουθιακή αναζήτηση μαζί με τα δεδομένα έχουν προστεθεί δείκτες ( index ). Στην εργασία αυτή προτείνουμε ένα παραμετροποιήσημο αλγόριθμο εκπομπής δεδομένων τον Interpolation Index. Ο αλγόριθμος έχει δυνατότητα να βελτιστοποιήσει το χρόνο αναζήτησης κρατώντας σταθερό τον χρόνο συντονισμού (tuning time ) και αντίστροφα

Managing Data Replication in Mobile Ad-Hoc Network Databases

A Mobile Ad-hoc Network (MANET) is a collection of wireless autonomous nodes without any fixed backbone infrastructure. All the nodes in MANET are mobile and power restricted and thus, disconnection and network partitioning occur frequently. In addition, many MANET database transactions have time constraints. In this paper, a Data REplication technique for real-time Ad-hoc Mobile databases (DREAM) is proposed that addresses all those issues. It improves data accessibility while considering the issue of energy limitation by replicating hot data items at servers that have higher remaining power. It addresses disconnection and network partitioning by introducing new data and transaction types and by considering the stability of wireless link. It handles the real-time transaction issue by replicating data items that are accessed frequently by firm transactions before those accessed frequently by soft transactions. DREAM is prototyped on laptops and PDAs and compared with two existing replication techniques using a military database application. The results show that DREAM performs the best in terms of percentage of successfully executed transactions, servers’ and clients’ energy consumption, and balance of energy consumption distribution among servers

PROPUESTA DE UNA ESTRATEGIA DE ELECCIÓN DE REDES BASADA EN CONTEXTO PARA MEJORAR EL AHORRO DE ENERGÍA EN DISPOSITIVOS MÓVILES CON SISTEMA OPERATIVO ANDROID EN LA CIUDAD DE AREQUIPA

SISTEMA OPERATIVO ANDROID TECNOLOGÍAS INALAMBRICAS 3G LTE WIMAX WIFI MERCADO DE TELEFONÍA MÓVIL EN EL PERÚ TECNOLOGÍA BATERÍA DE ION DE LITIO COMPUTACIÓN CONSCIENTE DEL CONTEXTO APLICACIONES CONSCIENTES DEL CONTEXTO ARQUITECTURA RECUPERACIÓN DE DATOS PRIMARIOS PRE PROCESAMIENTO ALMACENAMIENTO Y ADMINISTRACIÓN CADENAS DE MARKOV CADENAS DE MARKOV FINITAS TELEMEDICINA APLICACIONES DE LA TELEMEDICINA FUNCIONAMIENTO DE UN SISTEMA DE TELEMEDICINA DISEÑO E IMPLEMENTACIÓN DE LA PROPUESTA ANÁLISIS DE CONSUMO WIFI Y 3G DISEÑO E IMPLEMENTACIÓN DE LA SOLUCIÓN MODULO DE ELECCIÓN DE REDES MODULO DE PREDICCIÓN HERRAMIENTA MEDICIÓN: POWERTUTOR VALIDACIÓN DE LA PROPUESTA: USUARIOS EXPERTOS DISPOSITIVOS UTILIZADOS ANÁLISIS DE LOS RESULTADOS DE LAS PRUEBAS CON USUARIOS EXPERTO

A Framework for Generic and Energy Efficient Context Recognition for Personal Mobile Devices

The advancements in the field of mobile computing over the last decade have enabled the scientific community to expedite the theoretical and experimental work to achieve the vision of ubiquitous computing. As ubiquitous computing aims to provide seamless and distraction free task support to its users, one of the essential pieces of information required by the ubiquitous computing systems to do so is the context of its users. Context of a user can be defined as the information that describes the task the user is performing and the environment in which the user is currently present. Among various platforms that are commonly used to determine user's context, the personal mobile devices like smart phones stand out as one of the most widely used and widely evaluated ones. However, despite numerous advantages that are provided by modern day personal mobile devices, such as high computational and communication capabilities, variety of on-board sensors to capture raw data related to user's motion and environment, high resolution displays to enable interaction with other services and systems, these devices suffer from limited battery resources. In contrast to the advancements in other domains, the advancements in the battery domain have not been up to the mark. Consequently, the context recognition applications developed for these devices suffer from the trade-off between achieving accuracy and longevity of other device's basic operations. As a result, most of the existing context recognition applications for these devices are fine tuned for specific context types and thereby lacks generality. The situation gets worse when a number of context recognition applications are executed simultaneously, thus competing for limited resources and consuming the device's battery additively. To address the aforementioned issues, this thesis provides a generic and energy efficient context recognition framework for personal mobile devices. The main contribution consists of a generic framework to support development of context recognition applications supported by algorithms to achieve their energy efficient execution. The proposed framework consists of two systems namely the component system and the activation system. The component system allows developers to create context recognition applications using a component abstraction. This enables runtime analysis of applications' structures to adopt our novel energy efficiency mechanism. The activation system uses a state machine abstraction to allow context dependent activation of context recognition configurations pertaining to relevant user's tasks such that only needed configurations are executed to determine only the relevant context characteristics, thereby enabling energy efficiency. The activation system also provides generic applicability of four different energy efficiency techniques, already used in different existing systems but mostly for specific context characteristics. To aid rapid prototyping, both systems are equipped with off-line development tools. The tools include graphical editors and a component tool-kit. The graphical editors allow developers to create component configurations used by the component system and state machines used by the activation system. These editors enable developers to create component configurations and state machines by simply dragging, dropping and connecting different models used in our component and state machine abstractions. These tools also provide validation and code generation utilities. In addition to the graphical editor, the framework provides a component tool-kit which consists of a number of already implemented sensing, preprocessing and classification components which can be re-used in new applications. In order to provide the energy efficient execution of context recognition applications, the thesis introduces a novel energy efficiency technique called configuration folding. Configuration folding analyses structures of simultaneously executing context recognition applications to identify redundant functionalities between them and as an output produces a single redundancy free context recognition configuration which holds the structural integrity of all applications. Consequently, the overall energy expenditure is reduced compared to the original expenditure when redundant functionalities are not removed. The experimental evaluation of configuration folding on test applications shows energy savings between 13 and 48 %. The thesis also investigates optimization possibilities in configuration folding in case the redundant functionalities between the applications differ in parametrization. Towards this end, the thesis identifies commonly used parameters in context recognition applications and defines relations between them. Finally, an extended version of configuration folding is introduced to handle the differences in parametrization. The evaluation of the extended version of configuration folding on test scenarios shows energy saving of up to 45%. The contributions in this thesis have been evaluated extensively. The framework has been used in number of European Commission (EC) projects and in student projects and theses at the University of Duisburg-Essen, Germany. Using the component system and the activation system, a number of applications have been developed in those projects. Some of these applications include crowd density estimation in buses, bus ride detections, navigation application for buses in Madrid, user movement detection, user localization, fall detection application etc. Moreover, the component system, the activation system and the configuration folding technique have been published in different prestigious conferences and workshops

Mobile-based online data mining : outdoor activity recognition

One of the unique features of mobile applications is the context awareness. The mobility and power afforded by smartphones allow users to interact more directly and constantly with the external world more than ever before. The emerging capabilities of smartphones are fueling a rise in the use of mobile phones as input devices for a great range of application fields; one of these fields is the activity recognition. In pervasive computing, activity recognition has a significant weight because it can be applied to many real-life, human-centric problems. This important role allows providing services to various application domains ranging from real-time traffic monitoring to fitness monitoring, social networking, marketing and healthcare. However, one of the major problems that can shatter any mobile-based activity recognition model is the limited battery life. It represents a big hurdle for the quality and the continuity of the service. Indeed, excessive power consumption may become a major obstacle to broader acceptance context-aware mobile applications, no matter how useful the proposed service may be. We present during this thesis a novel unsupervised battery-aware approach to online recognize users’ outdoor activities without depleting the mobile resources. We succeed in associating the places visited by individuals during their movements to meaningful human activities. Our approach includes novel models that incrementally cluster users’ movements into different types of activities without any massive use of historical records. To optimize battery consumption, our approach behaves variably according to users’ behaviors and the remaining battery level. Moreover, we propose to learn users’ habits in order to reduce the activity recognition computation. Our innovative battery-friendly method combines activity recognition and prediction in order to recognize users’ activities accurately without draining the battery of their phones. We show that our approach reduces significantly the battery consumption while keeping the same high accuracy. Une des caractéristiques uniques des applications mobiles est la sensibilité au contexte. La mobilité et la puissance de calcul offertes par les smartphones permettent aux utilisateurs d’interagir plus directement et en permanence avec le monde extérieur. Ces capacités émergentes ont pu alimenter plusieurs champs d’applications comme le domaine de la reconnaissance d’activités. Dans le domaine de l'informatique omniprésente, la reconnaissance des activités humaines reçoit une attention particulière grâce à son implication profonde dans plusieurs problématiques de vie quotidienne. Ainsi, ce domaine est devenu une pièce majeure qui fournit des services à un large éventail de domaines comme la surveillance du trafic en temps réel, les réseaux sociaux, le marketing et la santé. Cependant, l'un des principaux problèmes qui peuvent compromettre un modèle de reconnaissance d’activité sur les smartphones est la durée de vie limitée de la batterie. Ce handicap représente un grand obstacle pour la qualité et la continuité du service. En effet, la consommation d'énergie excessive peut devenir un obstacle majeur aux applications sensibles au contexte, peu importe à quel point ce service est utile. Nous présentons dans de cette thèse une nouvelle approche non supervisée qui permet la détection incrémentale des activités externes sans épuiser les ressources du téléphone. Nous parvenons à associer efficacement les lieux visités par des individus lors de leurs déplacements à des activités humaines significatives. Notre approche comprend de nouveaux modèles de classification en ligne des activités humaines sans une utilisation massive des données historiques. Pour optimiser la consommation de la batterie, notre approche se comporte de façon variable selon les comportements des utilisateurs et le niveau de la batterie restant. De plus, nous proposons d'apprendre les habitudes des utilisateurs afin de réduire la complexité de l’algorithme de reconnaissance d'activités. Pour se faire, notre méthode combine la reconnaissance d’activités et la prédiction des prochaines activités afin d’atteindre une consommation raisonnable des ressources du téléphone. Nous montrons que notre proposition réduit remarquablement la consommation de la batterie tout en gardant un taux de précision élevé