Congestion control mechanism for sensor-cloud Infrastructure

 This thesis has developed a sensor-Cloud system that integrates WBANs with Cloud computing to enable real-time sensor data collection, storage, processing, sharing and management. As the main contribution of this study, a congestion detection and control protocol is proposed to ensure acceptable data flows are maintained during the network lifetime.

Data rich digital architectural environments

The development of data rich digital environments for the construction industry has been problematic despite the initial optimism when their application to design and construction was first considered. This paper reviews the current state of the art research into the application of information technology in design and construction and identifies the more critical issues in its adoption. In conclusion the paper then proposes a preliminary theoretical model being developed as a research tool for investigation into highly detailed information flows in a case study building renovation project. This investigation aims to track the detailed information flows and knowledge system used by the stakeholders in the building project.

Process model to support data rich digital environments

This paper proposes a theoretical process model and the associated detailed information structure which reflects the complexity of information, stakeholder interaction and intellectual property concerns which are currently seen in the construction industry. This is being developed and tested against a field study renovation project. The field study project identifies information flows and interactions between stakeholders such as designers, project managers, clients, contractors, subcontractors and suppliers. The process model which is being established shows very high levels of complexity in dependencies and interdependencies between implicit and explicit information within the project design and construction teams. Without an understanding of these detailed and complex process interactions, proposals for the application of ICT to the construction industry will not reflect the requirements of those for whom they are being developed.

Multiple lines of evidence for the beneficial effects of environmental flows in two lowland rivers in Victoria, Australia

The aim of this study was to identify whether environmental flows released into two lowland rivers (the Glenelg and Wimmera Rivers, western Victoria, Australia) during the spring to autumn period had successfully ameliorated the negative effects of multiple human impacts. Macroinvertebrates and a range of physico-chemical variables were sampled from three reaches in each river. Both rivers were sampled during three environmental release seasons with average-sized releases (1997-1998, 1998-1999 and 2001-2002) and two drought seasons with limited releases (1999-2000 and 2000-2001). The effects of releasing average-sized environmental flows on macroinvertebrates and physico-chemical variables were assessed by comparison with data from the two drought seasons. For the Glenelg River, data from a reference season prior to the release of environmental flows (1995-1996) was also compared to data from the five environmental flow seasons. Multivariate analyses revealed four pieces of evidence indicating that the release of environmental flows effectively slowed the process of environmental degradation in the Glenelg River but not in the Wimmera River: (1) the magnitude of the river discharge was dependent on the size of environmental flow releases; (2) in the Wimmera River, water quality deteriorated markedly during the two drought seasons and correlated strongly with macroinvertebrate assemblage structure, but this was not observed in the Glenelg River; (3) the taxonomic composition of the macroinvertebrate assemblages among contrasting flow release seasons reflected the severe deterioration in water quality of the Wimmera River; (4) despite two drought seasons with minimal environmental flow releases, the macroinvertebrate assemblage in the Glenelg River did not differ from the average-release seasons, nor did it return to a pre-environmental flows condition. Therefore, it appears that environmental flow releases did sustain the macroinvertebrate assemblage and maintain reasonable water quality in the Glenelg River. However, in the Wimmera River, release volumes were too small to maintain low salinities and were associated with marked changes in the macroinvertebrate assemblage. Therefore, there are multiple lines of evidence that environmental flow releases of sufficient magnitude may slow the process of degradation in a regulated lowland river.

Complexity of data rich architectural environments

This research focused on developing a detailed understanding of current organisational interactions and information flows in a construction industry based field study of a refurbishment projects of a 100 year old warehouse building. From this, conclusions are then drawn regarding the application of information and communication technologies (ICT) to the industry.

Number plate survey methodology for long-distance traffic flows

The lack of comprehensive data on transport operations is a long- standing problem in transport research. Information on road transport in particular has proved difficult to obtain. This Paper documents a study which was aimed at developing and testing a technique to estimate long-distance passenger and freight movements based on direct observation of vehicle movements.

Relationships, the role of individuals and knowledge flows in the internationalization of service firms

This article develops a new conceptual model of knowledge flows within international service firms. Our model takes explicit account of the critical role of relationships and the individual as being central to the process of knowledge transfer for service firms. The model is then validated with data collected from five international service firms using critical event analysis techniques. The findings reinforce our contention that the individual plays a critical role in the process of knowledge transfer and that relationships form an integral part of this process. The implications of this finding are also discussed.

Classification of correlated internet traffic flows

A critical problem for Internet traffic classification is how to obtain a high-performance statistical feature based classifier using a small set of training data. The solutions to this problem are essential to deal with the encrypted applications and the new emerging applications. In this paper, we propose a new Naive Bayes (NB) based classification scheme to tackle this problem, which utilizes two recent research findings, feature discretization and flow correlation. A new bag-of-flow (BoF) model is firstly introduced to describe the correlated flows and it leads to a new BoF-based traffic classification problem. We cast the BoF-based traffic classification as a specific classifier combination problem and theoretically analyze the classification benefit from flow aggregation. A number of combination methods are also formulated and used to aggregate the NB predictions of the correlated flows. Finally, we carry out a number of experiments on a large scale real-world network dataset. The experimental results show that the proposed scheme can achieve significantly higher classification accuracy and much faster classification speed with comparison to the state-of-the-art traffic classification methods.

International business cycles and remittance flows

In this paper, we study the macroeconomic determinants of remittance flows. We place particular attention to fluctuations in remittance flows over the international business cycles. Estimating a dynamic panel data model using the system-GMM method over the period 1970–2007, we document that remittance inflows decrease with home country volatility. Contrarily, remittance inflows increase with the volatility in host countries, especially for middle-income countries. Lower interest rates in host countries lead to larger remittance outflows. Trade and capital account openness are the most important factors that determine both remittance inflows and outflows. We conclude that macroeconomic factors of both home and host countries are important for understanding remittance flows.

A MapReduce-based nearest neighbor approach for big-data-driven traffic flow prediction

In big-data-driven traffic flow prediction systems, the robustness of prediction performance depends on accuracy and timeliness. This paper presents a new MapReduce-based nearest neighbor (NN) approach for traffic flow prediction using correlation analysis (TFPC) on a Hadoop platform. In particular, we develop a real-time prediction system including two key modules, i.e., offline distributed training (ODT) and online parallel prediction (OPP). Moreover, we build a parallel k-nearest neighbor optimization classifier, which incorporates correlation information among traffic flows into the classification process. Finally, we propose a novel prediction calculation method, combining the current data observed in OPP and the classification results obtained from large-scale historical data in ODT, to generate traffic flow prediction in real time. The empirical study on real-world traffic flow big data using the leave-one-out cross validation method shows that TFPC significantly outperforms four state-of-the-art prediction approaches, i.e., autoregressive integrated moving average, Naïve Bayes, multilayer perceptron neural networks, and NN regression, in terms of accuracy, which can be improved 90.07% in the best case, with an average mean absolute percent error of 5.53%. In addition, it displays excellent speedup, scaleup, and sizeup.

Towards practical and near-optimal coflow scheduling for data center networks

In current data centers, an application (e.g., MapReduce, Dryad, search platform, etc.) usually generates a group of parallel flows to complete a job. These flows compose a coflow and only completing them all is meaningful to the application. Accordingly, minimizing the average Coflow Completion Time (CCT) becomes a critical objective of flow scheduling. However, achieving this goal in today's Data Center Networks (DCNs) is quite challenging, not only because the schedule problem is theoretically NP-hard, but also because it is tough to perform practical flow scheduling in large-scale DCNs. In this paper, we find that minimizing the average CCT of a set of coflows is equivalent to the well-known problem of minimizing the sum of completion times in a concurrent open shop. As there are abundant existing solutions for concurrent open shop, we open up a variety of techniques for coflow scheduling. Inspired by the best known result, we derive a 2-approximation algorithm for coflow scheduling, and further develop a decentralized coflow scheduling system, D-CAS, which avoids the system problems associated with current centralized proposals while addressing the performance challenges of decentralized suggestions. Trace-driven simulations indicate that D-CAS achieves a performance close to Varys, the state-of-the-art centralized method, and outperforms Baraat, the only existing decentralized method, significantly.