ArchWiSeN: uma estratégia baseada em modelos para desenvolvimento de aplicações para redes de sensores e atuadores sem fio

Wireless Sensor and Actuator Networks (WSAN) are a key component in Ubiquitous Computing Systems and have many applications in different knowledge domains. Programming for such networks is very hard and requires developers to know the available sensor platforms specificities, increasing the learning curve for developing WSAN applications. In this work, an MDA (Model-Driven Architecture) approach for WSAN applications development called ArchWiSeN is proposed. The goal of such approach is to facilitate the development task by providing: (i) A WSAN domain-specific language, (ii) a methodology for WSAN application development; and (iii) an MDA infrastructure composed of several software artifacts (PIM, PSMs and transformations). ArchWiSeN allows the direct contribution of domain experts in the WSAN application development without the need of specialized knowledge on WSAN platforms and, at the same time, allows network experts to manage the application requirements without the need for specific knowledge of the application domain. Furthermore, this approach also aims to enable developers to express and validate functional and non-functional requirements of the application, incorporate services offered by WSAN middleware platforms and promote reuse of the developed software artifacts. In this sense, this Thesis proposes an approach that includes all WSAN development stages for current and emerging scenarios through the proposed MDA infrastructure. An evaluation of the proposal was performed by: (i) a proof of concept encompassing three different scenarios performed with the usage of the MDA infrastructure to describe the WSAN development process using the application engineering process, (ii) a controlled experiment to assess the use of the proposed approach compared to traditional method of WSAN application development, (iii) the analysis of ArchWiSeN support of middleware services to ensure that WSAN applications using such services can achieve their requirements ; and (iv) systematic analysis of ArchWiSeN in terms of desired characteristics for MDA tool when compared with other existing MDA tools for WSAN.

CALV3 - Uma linguagem específica de domínio para segurança em sistemas corporativos: um estudo de caso sistemático na indústria

The academic community and software industry have shown, in recent years, substantial interest in approaches and technologies related to the area of model-driven development (MDD). At the same time, continues the relentless pursuit of industry for technologies to raise productivity and quality in the development of software products. This work aims to explore those two statements, through an experiment carried by using MDD technology and evaluation of its use on solving an actual problem under the security context of enterprise systems. By building and using a tool, a visual DSL denominated CALV3, inspired by the software factory approach: a synergy between software product line, domainspecific languages and MDD, we evaluate the gains in abstraction and productivity through a systematic case study conducted in a development team. The results and lessons learned from the evaluation of this tool within industry are the main contributions of this work

LWiSSy: uma linguagem específica de domínio para modelagem de sistemas de redes de sensores e atuadores sem fio

The field of Wireless Sensor and Actuator Networks (WSAN) is fast increasing and has attracted the interest of both the research community and the industry because of several factors, such as the applicability of such networks in different application domains (aviation, civil engineering, medicine, and others). Moreover, advances in wireless communication and the reduction of hardware components size also contributed for a fast spread of these networks. However, there are still several challenges and open issues that need to be tackled in order to achieve the full potential of WSAN usage. The development of WSAN systems is one of the most relevant of these challenges considering the number of variables involved in this process. Currently, a broad range of WSAN platforms and low level programming languages are available to build WSAN systems. Thus, developers need to deal with details of different sensor platforms and low-level programming abstractions of sensor operational systems on one hand, and they also need to have specific (high level) knowledge about the distinct application domains, on the other hand. Therefore, in order to decouple the handling of these two different levels of knowledge, making easier the development process of WSAN systems, we propose LWiSSy (Domain Language for Wireless Sensor and Actuator Networks Systems), a domain specific language (DSL) for WSAN. The use of DSLs raises the abstraction level during the programming of systems and modularizes the system building in several steps. Thus, LWiSSy allows the domain experts to directly contribute in the development of WSANs without having knowledge on low level sensor platforms, and network experts to program sensor nodes to meet application requirements without having specific knowledge on the application domain. Additionally, LWiSSy enables the system decomposition in different levels of abstraction according to structural and behavioral features and granularities (network, node group and single node level programming)

Um método para desenvolvimento de abordagens generativas com composição de linguagens específicas de domínio

The software systems development with domain-specific languages has become increasingly common. Domain-specific languages (DSLs) provide increased of the domain expressiveness, raising the abstraction level by facilitating the generation of models or low-level source code, thus increasing the productivity of systems development. Consequently, methods for the development of software product lines and software system families have also proposed the adoption of domain-specific languages. Recent studies have investigated the limitations of feature model expressiveness and proposing the use of DSLs as a complement or substitute for feature model. However, in complex projects, a single DSL is often insufficient to represent the different views and perspectives of development, being necessary to work with multiple DSLs. In order to address new challenges in this context, such as the management of consistency between DSLs, and the need to methods and tools that support the development with multiple DSLs, over the past years, several approaches have been proposed for the development of generative approaches. However, none of them considers matters relating to the composition of DSLs. Thus, with the aim to address this problem, the main objectives of this dissertation are: (i) to investigate the adoption of the integrated use of feature models and DSLs during the domain and application engineering of the development of generative approaches; (ii) to propose a method for the development of generative approaches with composition DSLs; and (iii) to investigate and evaluate the usage of modern technology based on models driven engineering to implement strategies of integration between feature models and composition of DSLs

Abordagem dirigida a modelos para redes de sensores sem fio

Research on Wireless Sensor Networks (WSN) has evolved, with potential applications in several domains. However, the building of WSN applications is hampered by the need of programming in low-level abstractions provided by sensor OS and of specific knowledge about each application domain and each sensor platform. We propose a MDA approach do develop WSN applications. This approach allows domain experts to directly contribute in the developing of applications without needing low level knowledge on WSN platforms and, at the same time, it allows network experts to program WSN nodes to met application requirements without specific knowledge on the application domain. Our approach also promotes the reuse of the developed software artifacts, allowing an application model to be reused across different sensor platforms and a platform model to be reused for different applications

Abordagem de construção de arquitetura homogênea para comitês via meta-aprendizagem

In the world we are constantly performing everyday actions. Two of these actions are frequent and of great importance: classify (sort by classes) and take decision. When we encounter problems with a relatively high degree of complexity, we tend to seek other opinions, usually from people who have some knowledge or even to the extent possible, are experts in the problem domain in question in order to help us in the decision-making process. Both the classification process as the process of decision making, we are guided by consideration of the characteristics involved in the specific problem. The characterization of a set of objects is part of the decision making process in general. In Machine Learning this classification happens through a learning algorithm and the characterization is applied to databases. The classification algorithms can be employed individually or by machine committees. The choice of the best methods to be used in the construction of a committee is a very arduous task. In this work, it will be investigated meta-learning techniques in selecting the best configuration parameters of homogeneous committees for applications in various classification problems. These parameters are: the base classifier, the architecture and the size of this architecture. We investigated nine types of inductors candidates for based classifier, two methods of generation of architecture and nine medium-sized groups for architecture. Dimensionality reduction techniques have been applied to metabases looking for improvement. Five classifiers methods are investigated as meta-learners in the process of choosing the best parameters of a homogeneous committee.