Nickolas J. G. Falkner

When things matter

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed.

Puzzle-Based Learning for Engineering and Computer Science

To attract, motivate, and retain students and increase their mathematical awareness and problem-solving skills, universities are introducing courses or seminars that explore puzzle-based learning. We introduce and define this learning approach with a sample syllabus and course material, describe course variations, and highlight early student feedback.

Generalized graph products for network design and analysis

Network design, as it is currently practiced, involves putting devices together to create a network. However, a network is more than the sum of its parts, both in terms of the services it provides, and the potential for bugs. Devices are important, but their combination into a network should follow from expression of high-level policy, not the minutiae of network device configuration. Ideally we want to consider the network as a whole object. In this paper we develop generalized graph products that allow the mathematical design of a network in terms of small subgraphs that directly express business policy. The result is a flexible algebraic description of networks suitable for manipulation and proof. The approach is more than just design — it allows for analysis of existing networks providing an understanding of the policies used in their construction, something which can be difficult if the original designers no longer work on that network. We apply the approach to several real world networks to demonstrate how it can provide insight, and improve design.

Collaborative learning and anxiety: a phenomenographic study of collaborative learning activities

Collaborative learning encourages deeper learning, producing significant benefit in learning outcomes. There has been an increasing trend to adopt collaborative activities, due to the expected learning benefits but also because of the expected social benefits and their impact on transition concerns. However, collaborative activities may also introduce additional stress and anxiety for students as they cope with altered participation expectations, and the need to develop collaboration, communication and management skills concurrently with their discipline skills. In this paper we describe a phenomenographic analysis of students reflections on collaborative activities, including their perceptions of the purpose of such activities, and corresponding behaviours.

Supporting and structuring “contributing student pedagogy” in Computer Science curricula

Contributing student pedagogy (CSP) builds upon social constructivist and community-based learning principles to create engaging and productive learning experiences. What makes CSP different from other, related, learning approaches is that it involves students both learning from and also explicitly valuing the contributions of other students. The creation of such a learning community builds upon established educational psychology that encourages deep learning, reflection and engagement. Our school has recently completed a review and update of its curriculum, incorporating student content-creation and collaboration into the design of key courses across the curriculum. Our experiences, based on several years of experimentation and development, support CSP-based curriculum design to reinforce the value of the student perspective, the clear description of their own transformative pathway to knowledge and the importance of establishing student-to-student networks in which students are active and willing participants. In this paper, we discuss the tools and approaches that we have employed to guide, support and structure student collaboration across a range of courses and year levels. By providing an account of our intentions, our approaches and tools, we hope to provide useful and transferrable knowledge that can be readily used by other academics who are considering this approach.

Increasing the effectiveness of automated assessment by increasing marking granularity and feedback units

Computer-based assessment is a useful tool for handling large-scale classes and is extensively used in the automated assessment of student programming assignments in Computer Science. The forms that this assessment takes, however, can vary widely from simple acknowledgement to a detailed analysis of output, structure and code. This study focusses on output analysis of submitted student assignment code and the degree to which changes in automated feedback influence student marks and persistence in submission. Data was collected over a four year period, over 22 courses but we focus on one course for this paper. Assignments were grouped by the number of different units of automated feedback that were delivered per assignment to investigate if students changed their submission behaviour or performance as the possible set of marks, that a student could achieve, changed. We discovered that pre-deadline results improved as the number of feedback units increase and that post-deadline activity was also improved as more feedback units were available.

Identifying computer science self-regulated learning strategies

Computer Science students struggle to develop fundamental programming skills and software development processes. Crucial to successful mastery is the development of discipline specific cognitive and metacognitive skills, including self-regulation. We can assist our students in the process of reflection and self-regulation by identifying and articulating successful self-regulated learning strategies for specific discipline contexts. However, in order to do so, we must develop an understanding of those discipline-specific strategies that are successful and can be readily adopted by students. In this paper, we analyse student reflections from an introductory software development course, identifying the usage of self-regulated learning strategies that are either specific to the software development domain, or articulated in that context. This study assists in the understanding of how Computer Science students develop learning skill within the discipline, and provides examples to guide the development of scaffolding activities to assist learning development.

RF-Care: Device-Free Posture Recognition for Elderly People Using A Passive RFID Tag Array

Activity recognition is a fundamental research topic for a wide range of important applications such as fall detection for elderly people. Existing techniques mainly rely on wearable sensors, which may not be reliable and practical in real-world situations since people often forget to wear these sensors. For this reason, device-free activity recognition has gained the popularity in recent years. In this paper, we propose an RFID (radio frequency identification) based, device-free posture recognition system. More specifically, we analyze Received Signal Strength Indicator (RSSI) signal patterns from an RFID tag array, and systematically examine the impact of tag configuration on system performance. On top of selected optimal subset of tags, we study the challenges on posture recognition. Apart from exploring posture classification, we specially propose to infer posture transitions via Dirichlet Process Gaussian Mixture Model (DPGMM) based Hidden Markov Model (HMM), which effectively captures the nature of uncertainty caused by signal strength varieties during posture transitions. We run a pilot study to evaluate our system with 12 orientation-sensitive postures and a series of posture change sequences. We conduct extensive experiments in both lab and real-life home environments. The results demonstrate that our system achieves high accuracy in both environments, which holds the potential to support assisted living of elderly people.

Integrating communication skills into the computer science curriculum

Computer Science majors must be able to communicate effectively. Industry surveys identify the development of communication and critical thinking skills as key to the reform of the higher education sector. However, academics are challenged by time and discipline content pressures, as well as a lack of familiarity with the teaching and assessment of communication skills content. There is considerable existing work in the area of communication skills development, positioned both in terms of curriculum guidelines for effective communication skills development, and example communication skills activities. However, this research is deficient in detailed, contextualised methodologies and frameworks for the development of communication skills within the Computer Science curriculum. We present a new methodology, building upon well established theoretical frameworks, designed to assist academics in the development of communication skills activities integrated with discipline content across the curriculum. We illustrate this methodology in the design of a CS1/CS2 communication skills course.

Model-Driven Performance Prediction of Distributed Real-Time Embedded Defense Systems

Autonomous defence systems are typically characterised by hard constraints on space, weight and power. These constraints have a strong impact on the non-functional properties, and performance, of the final system. System execution modelling tools permit early prediction of the performance of model driven systems, however the focus to date has been on understanding the performance of a model rather than determining if it meets performance requirements, and subsequently carrying out analysis to reveal the causes of any requirement violations. In this paper, we propose an integrated approach to performance prediction of model-driven distributed real time embedded defence systems. Our architectural prototyping system supports a scenario-driven experimental platform for evaluating model suitability within a set of deployment and real-time performance constraints. We present an overview of our performance prediction system, demonstrating the integration of modelling, execution and visualisation, and discuss a case study to illustrate our approach.