Markov modelling of the IEEE 802.11 DCF for real-time applications with periodic traffic

Popular wireless network standards, such as IEEE 802.11/15/16, are increasingly adopted in real-time control systems. However, they are not designed for real-time applications. Therefore, the performance of such wireless networks needs to be carefully evaluated before the systems are implemented and deployed. While efforts have been made to model general wireless networks with completely random traffic generation, there is a lack of theoretical investigations into the modelling of wireless networks with periodic real-time traffic. Considering the widely used IEEE 802.11 standard, with the focus on its distributed coordination function (DCF), for soft-real-time control applications, this paper develops an analytical Markov model to quantitatively evaluate the network quality-of-service (QoS) performance in periodic real-time traffic environments. Performance indices to be evaluated include throughput capacity, transmission delay and packet loss ratio, which are crucial for real-time QoS guarantee in real-time control applications. They are derived under the critical real-time traffic condition, which is formally defined in this paper to characterize the marginal satisfaction of real-time performance constraints.

Visualisation modelling in 3D of an alluvial aquifer system at a valley-wide scale to understand groundwater status : Lockyer, QLD

Visualisation provides a method to efficiently convey and understand the complex nature and processes of groundwater systems. This technique has been applied to the Lockyer Valley to aid in comprehending the current condition of the system. The Lockyer Valley in southeast Queensland hosts intensive irrigated agriculture sourcing groundwater from alluvial aquifers. The valley is around 3000 km2 in area and the alluvial deposits are typically 1-3 km wide and to 20-35 m deep in the main channels, reducing in size in subcatchments. The configuration of the alluvium is of a series of elongate ���fingers���. In this roughly circular valley recharge to the alluvial aquifers is largely from seasonal storm events, on the surrounding ranges. The ranges are overlain by basaltic aquifers of Tertiary age, which overall are quite transmissive. Both runoff from these ranges and infiltration into the basalts provided ephemeral flow to the streams of the valley. Throughout the valley there are over 5,000 bores extracting alluvial groundwater, plus lesser numbers extracting from underlying sandstone bedrock. Although there are approximately 2500 monitoring bores, the only regularly monitored area is the formally declared management zone in the lower one third. This zone has a calibrated Modflow model (Durick and Bleakly, 2000); a broader valley Modflow model was developed in 2002 (KBR), but did not have extensive extraction data for detailed calibration. Another Modflow model focused on a central area river confluence (Wilson, 2005) with some local production data and pumping test results. A recent subcatchment simulation model incorporates a network of bores with short-period automated hydrographic measurements (Dvoracek and Cox, 2008). The above simulation models were all based on conceptual hydrogeological models of differing scale and detail.

Modelling web search behaviour : a user���s cognitive style perspective [Unpublished]

Many researchers have investigated and modelled aspects of Web searching. A number of studies have explored the relationships between individual differences and Web searching. However, limited studies have explored the role of users��� cognitive styles in determining Web searching behaviour. Current models of Web searching have limited consideration of users��� cognitive styles. The impact of users��� cognitive style on Web searching and their relationships are little understood or represented. Individuals differ in their information processing approaches and the way they represent information, thus affecting their performance. To create better models of Web searching we need to understand more about user���s cognitive style and their Web search behaviour, and the relationship between them. More rigorous research is needed in using more complex and meaningful measures of relevance; across a range of different types of search tasks and different populations of Internet users. The project further explores the relationships between the users��� cognitive style and their Web searching. The project will develop a model depicting the relationships between a user���s cognitive style and their Web searching. The related literature, aims and objectives and research design are discussed.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Computational approaches for modelling intrinsic noise and delays in genetic regulatory networks

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).

Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns

Almost all metapopulation modelling assumes that connectivity between patches is only a function of distance, and is therefore symmetric. However, connectivity will not depend only on the distance between the patches, as some paths are easy to traverse, while others are difficult. When colonising organisms interact with the heterogeneous landscape between patches, connectivity patterns will invariably be asymmetric. There have been few attempts to theoretically assess the effects of asymmetric connectivity patterns on the dynamics of metapopulations. In this paper, we use the framework of complex networks to investigate whether metapopulation dynamics can be determined by directly analysing the asymmetric connectivity patterns that link the patches. Our analyses focus on ���patch occupancy��� metapopulation models, which only consider whether a patch is occupied or not. We propose three easily calculated network metrics: the ���asymmetry��� and ���average path strength��� of the connectivity pattern, and the ���centrality��� of each patch. Together, these metrics can be used to predict the length of time a metapopulation is expected to persist, and the relative contribution of each patch to a metapopulation���s viability. Our results clearly demonstrate the negative effect that asymmetry has on metapopulation persistence. Complex network analyses represent a useful new tool for understanding the dynamics of species existing in fragmented landscapes, particularly those existing in large metapopulations.

Modelling the interaction of keratinocytes and fibroblasts during normal and abnormal wound healing processes

The crosstalk between fibroblasts and keratinocytes is a vital component of the wound healing process, and involves the activity of a number of growth factors and cytokines. In this work, we develop a mathematical model of this crosstalk in order to elucidate the effects of these interactions on the regeneration of collagen in a wound that heals by second intention. We consider the role of four components that strongly affect this process: transforming growth factor-beta, platelet-derived growth factor, interleukin-1 and keratinocyte growth factor. The impact of this network of interactions on the degradation of an initial fibrin clot, as well as its subsequent replacement by a matrix that is mainly comprised of collagen, is described through an eight-component system of nonlinear partial differential equations. Numerical results, obtained in a two-dimensional domain, highlight key aspects of this multifarious process such as reepithelialisation. The model is shown to reproduce many of the important features of normal wound healing. In addition, we use the model to simulate the treatment of two pathological cases: chronic hypoxia, which can lead to chronic wounds; and prolonged inflammation, which has been shown to lead to hypertrophic scarring. We find that our model predictions are qualitatively in agreement with previously reported observations, and provide an alternative pathway for gaining insight into this complex biological process.

Statistical modelling of wind effects on signal propagation for wireless sensor networks

A wireless sensor network system must have the ability to tolerate harsh environmental conditions and reduce communication failures. In a typical outdoor situation, the presence of wind can introduce movement in the foliage. This motion of vegetation structures causes large and rapid signal fading in the communication link and must be accounted for when deploying a wireless sensor network system in such conditions. This thesis examines the fading characteristics experienced by wireless sensor nodes due to the effect of varying wind speed in a foliage obstructed transmission path. It presents extensive measurement campaigns at two locations with the approach of a typical wireless sensor networks configuration. The significance of this research lies in the varied approaches of its different experiments, involving a variety of vegetation types, scenarios and the use of different polarisations (vertical and horizontal). Non���line of sight (NLoS) scenario conditions investigate the wind effect based on different vegetation densities including that of the Acacia tree, Dogbane tree and tall grass. Whereas the line of sight (LoS) scenario investigates the effect of wind when the grass is swaying and affecting the ground-reflected component of the signal. Vegetation type and scenarios are envisaged to simulate real life working conditions of wireless sensor network systems in outdoor foliated environments. The results from the measurements are presented in statistical models involving first and second order statistics. We found that in most of the cases, the fading amplitude could be approximated by both Lognormal and Nakagami distribution, whose m parameter was found to depend on received power fluctuations. Lognormal distribution is known as the result of slow fading characteristics due to shadowing. This study concludes that fading caused by variations in received power due to wind in wireless sensor networks systems are found to be insignificant. There is no notable difference in Nakagami m values for low, calm, and windy wind speed categories. It is also shown in the second order analysis, the duration of the deep fades are very short, 0.1 second for 10 dB attenuation below RMS level for vertical polarization and 0.01 second for 10 dB attenuation below RMS level for horizontal polarization. Another key finding is that the received signal strength for horizontal polarisation demonstrates more than 3 dB better performances than the vertical polarisation for LoS and near LoS (thin vegetation) conditions and up to 10 dB better for denser vegetation conditions.

Benefits and issues of urban network travel time prediction

This paper presents the benefits and issues related to travel time prediction on urban network. Travel time information quantifies congestion and is perhaps the most important network performance measure. Travel time prediction has been an active area of research for the last five decades. The activities related to ITS have increased the attention of researchers for better and accurate real-time prediction of travel time. Majority of the literature on travel time prediction is applicable to freeways where, under non-incident conditions, traffic flow is not affected by external factors such as traffic control signals and opposing traffic flows. On urban environment the problem is more complicated due to conflicting areas (intersections), mid-link sources and sinks etc. and needs to be addressed.

Efficient safety message dissemination for cooperative collision warning via context modelling

A Cooperative Collision Warning System (CCWS) is an active safety techno- logy for road vehicles that can potentially reduce traffic accidents. It provides a driver with situational awareness and early warnings of any possible colli- sions through an on-board unit. CCWS is still under active research, and one of the important technical problems is safety message dissemination. Safety messages are disseminated in a high-speed mobile environment using wireless communication technology such as Dedicated Short Range Communication (DSRC). The wireless communication in CCWS has a limited bandwidth and can become unreliable when used inefficiently, particularly given the dynamic nature of road traffic conditions. Unreliable communication may significantly reduce the performance of CCWS in preventing collisions. There are two types of safety messages: Routine Safety Messages (RSMs) and Event Safety Messages (ESMs). An RSM contains the up-to-date state of a vehicle, and it must be disseminated repeatedly to its neighbouring vehicles. An ESM is a warning message that must be sent to all the endangered vehi- cles. Existing RSM and ESM dissemination schemes are inefficient, unscalable, and unable to give priority to vehicles in the most danger. Thus, this study investigates more efficient and scalable RSM and ESM dissemination schemes that can make use of the context information generated from a particular traffic scenario. Therefore, this study tackles three technical research prob- lems, vehicular traffic scenario modelling and context information generation, context-aware RSM dissemination, and context-aware ESM dissemination. The most relevant context information in CCWS is the information about possible collisions among vehicles given a current vehicular traffic situation. To generate the context information, this study investigates techniques to model interactions among multiple vehicles based on their up-to-date motion state obtained via RSM. To date, there is no existing model that can represent interactions among multiple vehicles in a speciffic region and at a particular time. The major outcome from the first problem is a new interaction graph model that can be used to easily identify the endangered vehicles and their danger severity. By identifying the endangered vehicles, RSM and ESM dis- semination can be optimised while improving safety at the same time. The new model enables the development of context-aware RSM and ESM dissemination schemes. To disseminate RSM efficiently, this study investigates a context-aware dis- semination scheme that can optimise the RSM dissemination rate to improve safety in various vehicle densities. The major outcome from the second problem is a context-aware RSM dissemination protocol. The context-aware protocol can adaptively adjust the dissemination rate based on an estimated channel load and danger severity of vehicle interactions given by the interaction graph model. Unlike existing RSM dissemination schemes, the proposed adaptive scheme can reduce channel congestion and improve safety by prioritising ve- hicles that are most likely to crash with other vehicles. The proposed RSM protocol has been implemented and evaluated by simulation. The simulation results have shown that the proposed RSM protocol outperforms existing pro- tocols in terms of efficiency, scalability and safety. To disseminate ESM efficiently, this study investigates a context-aware ESM dissemination scheme that can reduce unnecessary transmissions and deliver ESMs to endangered vehicles as fast as possible. The major outcome from the third problem is a context-aware ESM dissemination protocol that uses a multicast routing strategy. Existing ESM protocols use broadcast rout- ing, which is not efficient because ESMs may be sent to a large number of ve- hicles in the area. Using multicast routing improves efficiency because ESMs are sent only to the endangered vehicles. The endangered vehicles can be identified using the interaction graph model. The proposed ESM protocol has been implemented and evaluated by simulation. The simulation results have shown that the proposed ESM protocol can prevent potential accidents from occurring better than existing ESM protocols. The context model and the RSM and ESM dissemination protocols can be implemented in any CCWS development to improve the communication and safety performance of CCWS. In effect, the outcomes contribute to the realisation of CCWS that will ultimately improve road safety and save lives.

Modelling web-server Flash Events

A Flash Event (FE) represents a period of time when a web-server experiences a dramatic increase in incoming traffic, either following a newsworthy event that has prompted users to locate and access it, or as a result of redirection from other popular web or social media sites. This usually leads to network congestion and Quality-of-Service (QoS) degradation. These events can be mistaken for Distributed Denial-of-Service (DDoS) attacks aimed at disrupting the server. Accurate detection of FEs and their distinction from DDoS attacks is important, since different actions need to be undertaken by network administrators in these two cases. However, lack of public domain FE datasets hinders research in this area. In this paper we present a detailed study of flash events and classify them into three broad categories. In addition, the paper describes FEs in terms of three key components: the volume of incoming traffic, the related source IP-addresses, and the resources being accessed. We present such a FE model with minimal parameters and use publicly available datasets to analyse and validate our proposed model. The model can be used to generate different types of FE traffic, closely approximating real-world scenarios, in order to facilitate research into distinguishing FEs from DDoS attacks.

Collective dynamics and control of a 3-D small-world network with time delays

The paper presents a detailed analysis on the collective dynamics and delayed state feedback control of a three-dimensional delayed small-world network. The trivial equilibrium of the model is first investigated, showing that the uncontrolled model exhibits complicated unbounded behavior. Then three control strategies, namely a position feedback control, a velocity feedback control, and a hybrid control combined velocity with acceleration feedback, are then introduced to stabilize this unstable system. It is shown in these three control schemes that only the hybrid control can easily stabilize the 3-D network system. And with properly chosen delay and gain in the delayed feedback path, the hybrid controlled model may have stable equilibrium, or periodic solutions resulting from the Hopf bifurcation, or complex stranger attractor from the period-doubling bifurcation. Moreover, the direction of Hopf bifurcation and stability of the bifurcation periodic solutions are analyzed. The results are further extended to any "d" dimensional network. It shows that to stabilize a "d" dimensional delayed small-world network, at least a "d ��� 1" order completed differential feedback is needed. This work provides a constructive suggestion for the high dimensional delayed systems.

A hybrid simulation framework to assess the impact of renewable generators on a distribution network

With an increasing number of small-scale renewable generator installations, distribution network planners are faced with new technical challenges (intermittent load flows, network imbalances���). Then again, these decentralized generators (DGs) present opportunities regarding savings on network infrastructure if installed at strategic locations. How can we consider both of these aspects when building decision tools for planning future distribution networks? This paper presents a simulation framework which combines two modeling techniques: agent-based modeling (ABM) and particle swarm optimization (PSO). ABM is used to represent the different system units of the network accurately and dynamically, simulating over short time-periods. PSO is then used to find the most economical configuration of DGs over longer periods of time. The infrastructure of the framework is introduced, presenting the two modeling techniques and their integration. A case study of Townsville, Australia, is then used to illustrate the platform implementation and the outputs of a simulation.

Reputation modelling in Citizen Science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Data reliability issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. Participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data reliability has become an urgent demand. This study aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we propose to design a reputation framework to enhance data reliability and also investigate some critical elements that should be aware of during developing and designing new reputation systems.