Highly distributed environments (HDEs) are deployment environments that include powerful and robust machines in addition to resource-constrained and mobile devices such as laptops, personal digital assistants (or PDAs), smart-phones, GPS devices, sensors, etc. Developing software for HDEs is fundamentally different from the software development for central systems and stable distributed systems. This argument is discussed deeply and in-details throughout this dissertation. HDE applications are challenged by two problems: unreliable networks, and heterogeneity of hardware and software. Both challenges need careful handling, where the system must continue functioning and delivering the expected QoS. This dissertation is a direct response to the mentioned challenges of HDEs. The contribution of this dissertation is the cloud component model and its related formal language and tools. This is the general title. However, and to make this contribution clear, we prefer to present it in the following detailed form: (1) We propose a paradigm shift from distribution transparency to localization acknowledgment being the first class concern. (2) To achieve the above mentioned objective, we propose a novel component model called cloud component (CC). (3) In this dissertation we propose a new approach to assemble CCs using systematic methodology that maintains the properties of CC model. (4) Cloud component development process and cloud component based systems development process. (5) Location modeling and advanced localization for HDEs are the pivotal key in our contribution. (6) Formal language to model single CC, CC assembly, CC development process, and CC based systems. (7) We finally present our fully-developed supporting tools: the cloud component management system CCMS, and the Registry utility. To respond to the challenges posed by HDEs, and to maintain expected software quality at the user endpoint, we think we need to pass a ¿paradigm shift¿ from the way software is designed and implemented currently to our new vision that this dissertation is devoted to. This is a paradigm shift from distribution transparency to localization acknowledgment being the first class concern. The contribution in this thesis has several faces as explained above, but still, these faces are cohesive. Each of these faces form a partial contribution, however, this partial contribution does not mean anything if isolated from the overall proposal. Moreover, the merit of the overall proposal can not be grasped by reading one partial contribution. The merit of the proposal is evident only if all parts of this work are cohesively organized. Finally, we claim that our proposal spans the entire software development process for HDEs, from requirements to deployment and runtime management.

In last few years, the use of wireless sensor networks and cell phones has become ubiquitous; fusing these technologies in the field of business will open up new possibilities. To fill this lacuna, I propose a novel idea where the combination of these will facilitate companies to receive feedback on their products and services. System's unobtrusive sensors will not only collect shopping, mobile usage data from consumers but will also make effective use of this information to increase revenue, cut costs, etc.; the use of mobile agent based data mining allows analyzing the data from different dimensions, categorizing it on factors such as product positioning, promotion of goods, etc. as in the case of a shopping store. Additionally, because of the dynamic mining system the companies get on-the-scene recommendation of products rather than off-the-scene. In this thesis, a novel distributed pervasive mining system is proposed to get dynamic shopping information and mobile device usage of the customers.

This thesis introduces LIFT, a combination of a cluster-based approach with a cross-layer scheme in order to discover services in MANET. In this proposal, High Capability Devices (HCD) are differentiated from Limited Capability Devices (LCD). HCD are set up as the cluster leaders in each cluster so as to perform most of the service discovery activities. Thus, LIFT manages local traffic instead of global traffic. Consequently, messages, energy, computing processes, and bandwidth were reduced due to the optimum usage of network resources. In order to know if LIFT achieves its goal to minimize resources, we have compared LIFT with another well-known solution (AODV-SD) in terms of control message overhead, energy consumption, PDR, throughput, hop count average, NRL, end-to-end delay, and service acquisition time. After carrying out many trials and simulations, LIFT improved previous results in the area. / La tesis presenta a LIFT, una solución para descubrir servicios en MANET que combina un enfoque basado en cluster con un esquema cross-layer. En esta propuesta se diferencian los dispositivos de capacidades superiores (HCD) de los dispositivos de capacidades limitadas (LCD). Los HCD se establecen como líderes en cada cluster para ejecutar la mayoría de las actividades de descubrimiento de servicios. De esta forma, LIFT maneja tráfico local en vez de tráfico global. Por tanto, se reduce el consumo de mensajes, energía y cómputo al hacer uso óptimo de los recursos de la red. Para saber si LIFT logra el objetivo de minimizar recursos, lo hemos comparado contra otra solución (AODV-SD) en aspectos como sobrecarga de paquetes de control, consumo de energía, PDR, throughput, promedio de saltos, NRL, retardo extremo a extremo y tiempo de adquisición de servicios. Después de muchas pruebas y simulaciones, LIFT mejora resultados anteriores en este campo

The dream of weaving technology into our everyday fabric of life is recently being made possible by advances in ubiquitous computing and sensor technologies. Countless sensors of various sizes have made their way into everyday commercial applications. Many projects aim to explore new ways to utilize these new technologies to aid and interact with the general population. Context-aware systems use available context information to assist users automatically, without explicit user input. By inferring user intent and configuring the system proactively for each user, context-aware systems are an integral part of achieving user-friendly ubiquitous computing environments. <br /><br /> A common issue with building a distributed context-aware system is the need to develop a supporting infrastructure providing features such as storage, distributed messaging, and security, before the real work on processing context information can be done. This thesis proposes a coordination framework that provides an effective common foundation for context-aware systems. The separation between the context-processing logic component and the underlying supporting foundation allows researchers to focus their energy at the context-processing part of the system, instead of spending their time re-inventing the supporting infrastructure. <br /><br /> As part of an ongoing project, Impress, the framework uses the open standard, Jabber, as its communication protocol. The Publish-Subscribe (pubsub) extension to Jabber provides interesting features that match those needed by a context-aware system. The main contribution of this thesis is the design and implementation of a coordination framework, called the Impress Context Store, that provides an effective common foundation for context-aware systems. The separation between the context-processing logic and the underlying supporting foundation allows researchers to focus their energy at the context-processing part of the system, instead of spending their time re-inventing the supporting infrastructure.

The dream of weaving technology into our everyday fabric of life is recently being made possible by advances in ubiquitous computing and sensor technologies. Countless sensors of various sizes have made their way into everyday commercial applications. Many projects aim to explore new ways to utilize these new technologies to aid and interact with the general population. Context-aware systems use available context information to assist users automatically, without explicit user input. By inferring user intent and configuring the system proactively for each user, context-aware systems are an integral part of achieving user-friendly ubiquitous computing environments. <br /><br /> A common issue with building a distributed context-aware system is the need to develop a supporting infrastructure providing features such as storage, distributed messaging, and security, before the real work on processing context information can be done. This thesis proposes a coordination framework that provides an effective common foundation for context-aware systems. The separation between the context-processing logic component and the underlying supporting foundation allows researchers to focus their energy at the context-processing part of the system, instead of spending their time re-inventing the supporting infrastructure. <br /><br /> As part of an ongoing project, Impress, the framework uses the open standard, Jabber, as its communication protocol. The Publish-Subscribe (pubsub) extension to Jabber provides interesting features that match those needed by a context-aware system. The main contribution of this thesis is the design and implementation of a coordination framework, called the Impress Context Store, that provides an effective common foundation for context-aware systems. The separation between the context-processing logic and the underlying supporting foundation allows researchers to focus their energy at the context-processing part of the system, instead of spending their time re-inventing the supporting infrastructure.

The emergence of pervasive computing power and networking infrastructure is enabling new applications. Still, many milestones need to be reached before pervasive computing becomes an integral part of our lives. An important missing piece is the middleware that allows developers to easily create interesting pervasive computing applications. This dissertation explores the middleware needs of distributed pervasive applications. The main contributions of this thesis are the design, implementation, and evaluation of two systems: D-Stampede and Crest. D-Stampede allows pervasive applications to access live stream data from multiple sources using time as an index. Crest allows applications to organize historical events, and to reason about them using time, location, and identity. Together they meet the important needs of pervasive computing applications. D-Stampede supports a computational model called the thread-channel graph. The threads map to computing devices ranging from small to high-end processing elements. Channels serve as the conduits among the threads, specifically tuned to handle time-sequenced streaming data. D-Stampede allows the dynamic creation of threads and channels, and for the dynamic establishment (and removal) of the plumbing among them. The Crest system assumes a universe that consists of participation servers and event stores, supporting a set of applications. Each application consists of distributed software entities working together. The participation server helps the application entities to discover each other for interaction purposes. Application entities can generate events, store them at an event store, and correlate events. The entities can communicate with one another directly, or indirectly through the event store. We have qualitatively and quantitatively evaluated D-Stampede and Crest. The qualitative aspect refers to the ease of programming afforded by our programming abstractions for pervasive applications. The quantitative aspect measures the cost of the API calls, and the performance of an application pipeline that uses the systems.

In the last decade, with the extensive use of mobile electronic and wireless communication devices, there is a growing need for context aware applications and many pervasive computing applications have become integral parts of our daily lives. Context aware recommender systems are one of the popular ones in this area. Such systems surround the users and integrate with the environment / hence, they are aware of the users&#039 / context and use that information to deliver personalized recommendations about everyday tasks. In this manner, predicting user&rsquo / s next activity preferences with high accuracy improves the personalized service quality of context aware recommender systems and naturally provides user satisfaction. Predicting activities of people is useful and the studies on this issue in ubiquitous environment are considerably insufficient. Thus, this thesis proposes an activity prediction model to forecast a user&rsquo / s next activity preference using past preferences of the user in certain contexts and current contexts of user in ubiquitous environment. The proposed model presents a new approach for activity prediction by taking advantage of ontology. A prototype application is implemented to demonstrate the applicability of this proposed model and the obtained outputs of a sample case on this application revealed that the proposed model can reasonably predict the next activities of the users.

In this thesis work, a certificate based authentication model supported by mobile devices is provided for ubiquitous computing environments. The model primarily aims to create an infrastructure for controlling and regulating access requests through mobile devices to local resources and services. The model also allows users from different domains to use local resources and services within the scope of agreements between domains. In addition to conceptual description of the model, a real prototype implementation is developed and successful application of the model is demonstrated. Within the prototype implementation, a mobile application is developed for access requests and sensors are used as representative local resources. Sample cases applied on the prototype demonstrate applicability and feasibility of the model.

Significant developments in microprocessor and sensor technology make wirelessly connected small computing devices widely available / hence they are being used frequently to collect data from the environment. In this study, we construct a framework in order to extract high level information in an environment containing such pervasive computing devices. In the framework, raw data originating from wireless sensors are collected using an event driven system and converted to simple events for transmission over a network to a central processing unit. We also utilize complex event processing approach incorporating temporal constraints, aggregation and sequencing of events in order to define complex events for extracting high level information from the collected simple events. We develop a prototype using easily accessible hardware and set it up in a classroom within our university. The results demonstrate the feasibility of our approach, ease of deployment and successful application of the complex event processing framework.

The requirements of modern e-learning techniques change. Aspects such as community interaction, flexibility, pervasive learning and increasing mobility in communication habits become more important. To meet these challenges e-learning platforms must provide support on mobile learning. Most approaches try to adopt centralised and static e-learning mechanisms to mobile devices. However, often technically it is not possible for all kinds of devices to be connected to a central server. Therefore we introduce an application of a mobile e-learning network which operates totally decentralised with the help of an underlying ad hoc network architecture. Furthermore the concept of ad hoc messaging network (AMNET) is used as basis system architecture for our approach to implement a platform for pervasive mobile e-learning.