Place-based knowledge networks : the case of water management in South-West Victoria, Australia

This article aims to investigate the need for effective exchanges between knowledge generators and knowledge users in water management. Firstly, we explore the use of adaptive management for water governance and then outline the communication issues of water-management knowledge at a regional scale. Central to this approach is the need to harness 'local' knowledge that can be used to develop community participation in local water governance. Accordingly, we propose a three-network communication model to illustrate the process and identify the issues of concern for developing place-based strategies. Since research plays a central role in knowledge generation, one of the first ways to proceed is to recognise local research and incorporate it into an inclusive decision-making process. One way to achieve this is through the development of regional networks that are openly available to all, and we explore this by focusing on the place of 'network thinking' at local scale using a newly developed regional network for local knowledge dissemination in south-west Victoria, Australia. We conclude that so far this new network is too heavily reliant upon one web-based tool and outline a broader range of strategies that can be used to achieve its aims.

The role of social networks at different stages of business formation

This study empirically tests the fundamental assumption that social networks are important to entrepreneurs. This assumption underpins most social network research conducted in the field of entrepreneurship and is seldom questioned. Empirical data were drawn from Australia’s participation in the Global Entrepreneurship Monitor project (GEM) from 2000- 2005 – an aggregate sample of 14,205 randomly selected Australians. The study demonstrated: (1) statistically significant differences in social networks when entrepreneurs and non-entrepreneurs are compared and (2) that the structural diversity of social networks changes during the entrepreneurial process. It was found that structural diversity was most important to entrepreneurs in the discovery stage, least important to entrepreneurs in the start-up stage and of medium importance to entrepreneurs in the young business stage.

Energy-efficient medium access control in wireless sensor networks

Medium access control for wireless sensor networks has been an active
research area in the past decade. This chapter discusses a set of important medium access control (MAC) attributes and possible design trade-offs in protocol design, with an emphasis on energy efficiency. Then we categorize existing MAC protocols into five groups, outline the representative protocols, and compare their advantages and disadvantages in the context of wireless sensor network. Finally, thoughts for practitioners are presented and open research issues are also discussed.

Evaluation of Bayesian belief networks for diagnosis of psychotic disorders

The thesis examined the inter-rater reliability and procedural validity of four computerised Bayesian belief networks (BBNs) which were developed to assist with the diagnosis of psychotic disorders. The results of this research indicated that BBNs can significantly improve diagnostic reliability and may represent an important advance over current diagnostic methods. The professional portfolio investigated, through the presentation of case studies and review of literature relevant to each case study, how comorbidity and context of depression may impact on cognitive behavioural therapy treatment.

Power saving in sensor networks via mobile base station repositioning

This research investigated the effect of dynamically repositioning the geographic location of a mobile base station within a sensor network in order to reduce energy consumption and increase network lifetime. Through simulation and experimental methodology, the proposed approach outperformed existing methods by extending network lifetime whilst reducing energy consumption.

Dynamic request-driven scheme for data collectors in sensor networks

Wireless sensor networks lifetime is prolonged through a dynamic scheme for collecting sensory information using intelligent mobile elements. The data collection routes are optimised for fast and reliable delivery. The scheme minimises high levels of energy consumption to extend the network operational time.

Input and output analysis of complex conveyor networks

Material transfer networks are at the heart of critical infrastructure in many modern service and manufacturing industries. This research identified key performance measures, while deriving generalised analysis methodologies, for simulation models. The technology was validated for international airports, and used to determine operational capacity constraints under varied demand conditions.

Fast recovery from node compromise in wireless sensor networks

Wireless Sensor Networks (WSNs) are susceptible to a wide range of security attacks in hostile environments due to the limited processing and energy capabilities of sensor nodes. Consequently, the use of WSNs in mission critical applications requires reliable detection and fast recovery from these attacks. While much research has been devoted to detecting security attacks, very little attention has been paid yet to the recovery task. In this paper, we present a novel mechanism that is based on dynamic network reclustering and node reprogramming for recovering from node compromise. In response to node compromise, the proposed recovery approach reclusters the network excluding compromised nodes; thus allowing normal network operation while initiating node recovery procedures. We propose a novel reclustering algorithm that uses 2-hop neighbourhood information for this purpose. For node reprogramming we propose the modified Deluge protocol. The proposed node recovery mechanism is both decentralized and scalable. Moreover, we demonstrate through its implementation on a TelosB-based sensor network testbed that the proposed recovery method performs well in a low-resource WSN.

Lightweight authentication for recovery in wireless sensor networks

Wireless sensor networks (WSNs) suffer from a wide range of security attacks due to their limited processing and energy capabilities. Their use in numerous mission critical applications, however, requires that fast recovery from such attacks be achieved. Much research has been completed on detection of security attacks, while very little attention has been paid to recovery from an attack. In this paper, we propose a novel, lightweight authentication protocol that can secure network and node recovery operations such as re-clustering and reprogramming. Our protocol is based on hash functions and we compare the performance of two well-known lightweight hash functions, SHA-1 and Rabin. We demonstrate that our authentication protocol can be implemented efficiently on a sensor network test-bed with TelosB motes. Further, our experimental results show that our protocol is efficient both in terms of computational overhead and execution times which makes it suitable for low resourced sensor devices.

Data monitoring sensor network for bignet research testbed

Equipped with recent advances in electronics and communication, wireless sensor networks gained a rapid development to provide reliable information with higher Quality of Service (QoS) at lower costs. This paper presents a realtime tracking system developed as a part of the ISSNIP BigNet Testbed project. Here a GPS receiver was used to acquire position information of mobile nodes and GSM technology was used as the data communication media. Moreover, Google map based data visualization software was developed to locate the mobile nodes via Internet. This system can be used to accommodate various sensors, such as temperature, pressure, pH etc., and monitor the status of the nodes.

Identification of implicit technical relationships in overlapping networks

Overlapping network techniques to expand the single sequential finish-to-start relationship between preceding and succeeding activities in scheduling construction projects have been developed for decades. The hidden logic relationships between activities and two virtual activities, Start and Finish, however limit the applications of these techniques in practice as they may lead to incorrect time parameters of some activities. In this research, a novel approach has been developed to identify those concealed relationships in a structured procedure by a two-dimensional nested diagram. An empirical study was carried out to demonstrate the network modification approach.

Security networks in the field of counter-terrorism in Australia

This thesis examines networks as a means of organising counter-terrorism in Australia and the conditions that promote the effectiveness of security networks. It presents a framework for analysing network dynamics and network effectiveness in this field that can assist researchers to better understand networks and practitioners to better manage networks.

Simulation-based learning for control of complex conveyor networks

This research investigated the problem of path planning in complex conveyor networks. A reinforcement learning approach was applied to derive a control strategy for routing traffic. The derived strategy was verified in real world systems and was found to improve network performance by prioritising traffic flows and balancing network load.

Interconnecting networks of practice for professional learning

The article explores the complementary connections between communities of practice and the ways in which individuals orchestrate their engagement with others to further their professional learning. It does so by reporting on part of a research project conducted in New Zealand on teachers’ online professional learning in a university graduate diploma program on ICT education. Evolving from social constructivist pedagogy for online professional development, the research describes how teachers create their own networks of practice as they blend online and offline interactions with fellow learners and workplace colleagues. Teachers’ perspectives of their professional learning activities challenge the way universities design formal online learning communities and highlight the potential for networked learning in the zones and intersections between professional practice and study.
The article extends the concepts of Lave and Wenger’s (1991) communities of practice social theory of learning by considering the role participants play in determining their engagement and connections in and across boundaries between online learning communities and professional practice. It provides insights into the applicability of connectivist concepts for developing online pedagogies to promote socially networked

A brief review of recent research trends on applications of computational and statistical techniques in financial and business intelligence

Artificial neural networks and statistical techniques like decision trees, discriminant analysis, logistic regression and survival analysis play a crucial role in Business Intelligence. These predictive analytical tools exploit patterns found in historical data to make predictions about future events. In this paper we have shown some recent developments of a few of these techniques in financial and business intelligence applications like fraud detection, bankruptcy prediction and credit rating scoring.