17,757 research outputs found
Enabling High-Level Application Development for the Internet of Things
Application development in the Internet of Things (IoT) is challenging
because it involves dealing with a wide range of related issues such as lack of
separation of concerns, and lack of high-level of abstractions to address both
the large scale and heterogeneity. Moreover, stakeholders involved in the
application development have to address issues that can be attributed to
different life-cycles phases. when developing applications. First, the
application logic has to be analyzed and then separated into a set of
distributed tasks for an underlying network. Then, the tasks have to be
implemented for the specific hardware. Apart from handling these issues, they
have to deal with other aspects of life-cycle such as changes in application
requirements and deployed devices. Several approaches have been proposed in the
closely related fields of wireless sensor network, ubiquitous and pervasive
computing, and software engineering in general to address the above challenges.
However, existing approaches only cover limited subsets of the above mentioned
challenges when applied to the IoT. This paper proposes an integrated approach
for addressing the above mentioned challenges. The main contributions of this
paper are: (1) a development methodology that separates IoT application
development into different concerns and provides a conceptual framework to
develop an application, (2) a development framework that implements the
development methodology to support actions of stakeholders. The development
framework provides a set of modeling languages to specify each development
concern and abstracts the scale and heterogeneity related complexity. It
integrates code generation, task-mapping, and linking techniques to provide
automation. Code generation supports the application development phase by
producing a programming framework that allows stakeholders to focus on the
application logic, while our mapping and linking techniques together support
the deployment phase by producing device-specific code to result in a
distributed system collaboratively hosted by individual devices. Our evaluation
based on two realistic scenarios shows that the use of our approach improves
the productivity of stakeholders involved in the application development
From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)
In this paper, we illustrate how to enhance an existing state-of-the-art
modeling language and tool for the Internet of Things (IoT), called ThingML, to
support machine learning on the modeling level. To this aim, we extend the
Domain-Specific Language (DSL) of ThingML, as well as its code generation
framework. Our DSL allows one to define things, which are in charge of carrying
out data analytics. Further, our code generators can automatically produce the
complete implementation in Java and Python. The generated Python code is
responsible for data analytics and employs APIs of machine learning libraries,
such as Keras, Tensorflow and Scikit Learn. Our prototype is available as open
source software on Github.Comment: International Conference on Model Driven Engineering Languages and
Systems (MODELS) 2020 Poster Companion (Extended Abstract
Kevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use
The creation of Domain Specific Languages(DSL) counts as one of the main
goals in the field of Model-Driven Software Engineering (MDSE). The main
purpose of these DSLs is to facilitate the manipulation of domain specific
concepts, by providing developers with specific tools for their domain of
expertise. A natural approach to create DSLs is to reuse existing modeling
standards and tools. In this area, the Eclipse Modeling Framework (EMF) has
rapidly become the defacto standard in the MDSE for building Domain Specific
Languages (DSL) and tools based on generative techniques. However, the use of
EMF generated tools in domains like Internet of Things (IoT), Cloud Computing
or Models@Runtime reaches several limitations. In this paper, we identify
several properties the generated tools must comply with to be usable in other
domains than desktop-based software systems. We then challenge EMF on these
properties and describe our approach to overcome the limitations. Our approach,
implemented in the Kevoree Modeling Framework (KMF), is finally evaluated
according to the identified properties and compared to EMF.Comment: ISBN 978-2-87971-131-7; N° TR-SnT-2014-11 (2014
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