Lasith Fernando’s research while affiliated with RMIT University and other places

This report presents our SmartSpace event handling framework for managing smart-grids and renewable energy installations. SmartSpace provides decision support for human stakeholders. Based on different datasources that feed into our framework, a variety of analysis and decision steps are supported. These decision steps are ultimately used to provide adequate information to human stakeholders. The paper discusses potential data sources for decisions around smart energy systems and introduces a spatio-temporal modeling technique for the involved data. Operations to reason about the formalized data are provided. Our spatio-temporal models help to provide a semantic context for the data. Customized rules allow the specification of conditions under which information is provided to stakeholders. We exemplify our ideas and present our demonstrators including visualization capabilities.

In the past decade, smart energy systems and new solutions for grid technology have become an important topic. New challenges due to the emergence of renewable energy technology have appeared and have been investigated in academia and industry. This covers areas such as energy storage, strengthening of existing grid technology and prediction models for energy generation and consumption. A variety of databases have been created to support decisions around smart energy systems. Here, we are extending these existing views, by integrating the existing data and knowledge sources that have already been created for smart energy systems into a common framework. Our framework aims at supporting human stakeholders in their decisions around the planning and operation of Smart Energy systems.

This paper presents ideas towards automatic monitoring of industrial automation devices by using a cloud based monitoring infrastructure. We are in particular aiming at fine grained timed properties that can be described using formal specification techniques such as behavioral types. Possible applications are in the areas of remote maintenance and servicing as well as commissioning and testing. Our work is based in the IEC 61499 standard.

We are developing the Virtual Experiences (Vx)Lab, a research and research training infrastructure and capability platform for global collaboration. VxLab comprises labs with visualisation capabilities, including underpinning networking to global points of presence, videoconferencing and high-performance computation, simulation and rendering, and sensors and actuators such as robotic instruments locally and in connected remote labs. VxLab has been used for industry projects in industrial automation, experimental research in cloud deployment, workshops and remote capability demonstrations, teaching advanced-level courses in games development, and student software engineering projects. Our goal is for resources to become a "catalyst" for IT-driven research results both within the university and with external industry partners. Use cases include: multi-disciplinary collaboration, prototyping and troubleshooting requiring multiple viewpoints and architectures, dashboards and decision support for global remote planning and operations.

This paper describes a method for the recovering of software architectures from a set of similar (but unrelated) software products in binary form. One intention is to drive refactoring into software product lines and combine architecture recovery with run time binary analysis and existing clustering methods. Using our runtime binary analysis, we create graphs that capture the dependencies between different software parts. These are clustered into smaller component graphs, that group software parts with high interactions into larger entities. The component graphs serve as a basis for further software product line work. In this paper, we concentrate on the analysis part of the method and the graph clustering. We apply the graph clustering method to a real application in the context of automation / robot configuration software tools.

We present a method and initial results on reverse engineering the architecture of monolithic software systems. Our approach is based on analysis of system binaries resulting in a series of models, which are successively refined into a component structure. Our approach comprises the following steps: 1) instrumentation of existing binaries for dynamically generating execution traces at runtime and connected analysis, 2) static inspection of binaries, 3) interpretation using domain knowledge, and 4) identifying component boundaries using software clustering. We motivate a generic method which covers a large class of software systems, and evaluate our method on concrete software tools for industrial automation systems development, focusing on Intel x86 and Microsoft Windows-compatible applications.