2011 Strategic roadmap for Australian research infrastructure

The 2011 Roadmap articulates the priority research infrastructure areas of a national scale (capability areas) to further develop Australia’s research capacity and improve innovation and research outcomes over the next five to ten years. The capability areas have been identified through considered analysis of input provided by stakeholders, in conjunction with specialist advice from Expert Working Groups   It is intended the Strategic Framework will provide a high-level policy framework, which will include principles to guide the development of policy advice and the design of programs related to the funding of research infrastructure by the Australian Government. Roadmapping has been identified in the Strategic Framework Discussion Paper as the most appropriate prioritisation mechanism for national, collaborative research infrastructure. The strategic identification of Capability areas through a consultative roadmapping process was also validated in the report of the 2010 NCRIS Evaluation. The 2011 Roadmap is primarily concerned with medium to large-scale research infrastructure. However, any landmark infrastructure (typically involving an investment in excess of $100 million over five years from the Australian Government) requirements identified in this process will be noted. NRIC has also developed a ‘Process to identify and prioritise Australian Government landmark research infrastructure investments’ which is currently under consideration by the government as part of broader deliberations relating to research infrastructure. NRIC will have strategic oversight of the development of the 2011 Roadmap as part of its overall policy view of research infrastructure

Towards automatic apparatus integration in Automation Remote Laboratories

International audienceIn the context of ELearning, remote handson training has become an insisting need as in traditional learning, especially in scientific and technical disciplines. Electronic Laboratories (ELabs) have been growing for the last few years. LIESP team started in 2002 a research aiming to provide a framework which helps towards exchanging ELab learning scenarios when they fit to similar apparatuses (same functions, maybe not the same hardware). Meanwhile, LIMOS team focused on a design process to automate PLC code generation to help to design and generate programs for industrial discrete systems. This paper presents a project of merging these approaches to help ELab designers to design and integrate apparatuses into ELab frameworks when these apparatuses are discrete systems

Overview of modern teaching equipment that supports distant learning

Laboratory is a key element of engineering and applied sciences educational systems. With the development of Internet and connecting IT technologies, the appearance of remote laboratories was inevitable. Virtual laboratories are also available; they place the experiment in a simulated environment. However, this writing focuses on remote experiments not virtual ones. From the students’ point of view, it is a great help not only for those enrolling in distant or online courses but also for those studying in a more traditional way. With the spread of smart, portable devices capable of connection to the internet, students can expand or restructure time spent on studying. This is a huge help to them and also allows them to individually divide their time up, to learn how to self-study. This independent approach can prepare them for working environments. It offers flexibility and convenience to the students. From the universities’ point of view, it helps reduce maintenance costs and universities can share experiments which also helps the not so well-resourced educational facilities

Harnessing the Benefits of Open Electronics in Science

Freely and openly shared low-cost electronic applications, known as open electronics, have sparked a new open-source movement, with much un-tapped potential to advance scientific research. Initially designed to appeal to electronic hobbyists, open electronics have formed a global community of "makers" and inventors and are increasingly used in science and industry. Here, we review the current benefits of open electronics for scientific research and guide academics to enter this emerging field. We discuss how electronic applications, from the experimental to the theoretical sciences, can help (I) individual researchers by increasing the customization, efficiency, and scalability of experiments, while improving data quantity and quality; (II) scientific institutions by improving access and maintenance of high-end technologies, visibility and interdisciplinary collaboration potential; and (III) the scientific community by improving transparency and reproducibility, helping decouple research capacity from funding, increasing innovation, and improving collaboration potential among researchers and the public. Open electronics are powerful tools to increase creativity, democratization, and reproducibility of research and thus offer practical solutions to overcome significant barriers in science.Comment: 20 pages, 3 figure, 2 table

Enabling peer-to-peer remote experimentation in distributed online remote laboratories

Remote Access Laboratories (RALs) are online platforms that allow human user interaction with physical instruments over the Internet. Usually RALs follow a client-server paradigm. Dedicated providers create and maintain experiments and corresponding educational content. In contrast, this dissertation focuses on a Peer-to-Peer (P2P) service model for RALs where users are encouraged to host experiments at their location. This approach can be seen as an example of an Internet of Things (IoT) system. A set of smart devices work together providing a cyber-physical interface for users to run experiments remotely via the Internet. The majority of traditional RAL learning activities focus on undergraduate education where hands-on experience such as building experiments, is not a major focus. In contrast this work is motivated by the need to improve Science, Technology, Engineering and Mathematics (STEM) education for school-aged children. Here physically constructing experiments forms a substantial part of the learning experience. In the proposed approach, experiments can be designed with relatively simple components such as LEGO Mindstorms or Arduinos. The user interface can be programed using SNAP!, a graphical programming tool. While the motivation for the work is educational in nature, this thesis focuses on the technical details of experiment control in an opportunistic distributed environment. P2P RAL aims to enable any two random participants in the system - one in the role of maker creating and hosting an experiment and one in the role of learner using the experiment - to establish a communication session during which the learner runs the remote experiment through the Internet without requiring a centralized experiment or service provider. The makers need to have support to create the experiment according to a common web based programing interface. Thus, the P2P approach of RALs requires an architecture that provides a set of heterogeneous tools which can be used by makers to create a wide variety of experiments. The core contribution of this dissertation is an automaton-based model (twin finite state automata) of the controller units and the controller interface of an experiment. This enables the creation of experiments based on a common platform, both in terms of software and hardware. This architecture enables further development of algorithms for evaluating and supporting the performance of users which is demonstrated through a number of algorithms. It can also ensure the safety of instruments with intelligent tools. The proposed network architecture for P2P RALs is designed to minimise latency to improve user satisfaction and learning experience. As experiment availability is limited for this approach of RALs, novel scheduling strategies are proposed. Each of these contributions has been validated through either simulations, e.g. in case of network architecture and scheduling, or test-bed implementations, in case of the intelligent tools. Three example experiments are discussed along with users' feedback on their experience of creating an experiment and using others’ experimental setup. The focus of the thesis is mainly on the design and hosting of experiments and ensuring user accessibility to them. The main contributions of this thesis are in regards to machine learning and data mining techniques applied to IoT systems in order to realize the P2P RALs system. This research has shown that a P2P architecture of RALs can provide a wide variety of experimental setups in a modular environment with high scalability. It can potentially enhance the user-learning experience while aiding the makers of experiments. It presents new aspects of learning analytics mechanisms to monitor and support users while running experiments, thus lending itself to further research. The proposed mathematical models are also applicable to other Internet of Things applications

Towards Understanding Systems Through User Interactions

Modern computer systems are complex. Even in the best of conditions, it can be difficult to understand the behavior of the system and identify why certain actions are occurring. Existing systems attempt to provide insight by reviewing the effects of actions on the system and estimating their cause. As computer systems are strongly driven by actions of the user, we propose an approach to identify processes which have interacted with the user and provide data to which system behaviors were caused by the user. We implement three sensors within the graphical user interface capable of extracting the necessary information to identify these processes. We show our instrumentation is effective in characterizing applications with an on-screen presence, and provide data towards the determination of user intentions. We prove that our method for obtaining the information from the user interface can be done in an efficient manner with minimal overheads

Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

A Distributed Sensor Data Search Platform for Internet of Things Environments

Recently, the number of devices has grown increasingly and it is hoped that, between 2015 and 2016, 20 billion devices will be connected to the Internet and this market will move around 91.5 billion dollars. The Internet of Things (IoT) is composed of small sensors and actuators embedded in objects with Internet access and will play a key role in solving many challenges faced in today's society. However, the real capacity of IoT concepts is constrained as the current sensor networks usually do not exchange information with other sources. In this paper, we propose the Visual Search for Internet of Things (ViSIoT) platform to help technical and non-technical users to discover and use sensors as a service for different application purposes. As a proof of concept, a real case study is used to generate weather condition reports to support rheumatism patients. This case study was executed in a working prototype and a performance evaluation is presented.Comment: International Journal of Services Computing (ISSN 2330-4472) Vol. 4, No.1, January - March, 201

Bibliographic and Text Analysis of Research on Implementation of the Internet of Things to Support Education

The Internet of Things (IoT) has pervaded practically all aspects of our lives. In this exploratory study, we survey its applications in the field of education. It is evident that technology in general, and, in particular IoT, has been increasingly altering the educational landscape. The goal of this paper is to review the academic literature on IoT applications in education to provide an understanding of the transformation that is underway. Using topic modeling and keyword co-occurrence analysis techniques, we identified five dominant clusters of research. Our findings demonstrate that IoT research in education has mainly focused on the technical aspects; however, the social aspects remain largely unexplored. In addition to providing an overview of IoT research on education, this paper offers suggestions for future research