Middleware for Mobile Sensing Applications in Urban Environments

Sensor networks represent an attractive tool to observe the physical world. Networks of tiny sensors can be used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Application-specific deployments of static-sensor networks have been widely investigated. Commonly, these networks involve a centralized data-collection point and no sharing of data outside the organization that owns it. Although this approach can accommodate many application scenarios, it significantly deviates from the pervasive computing vision of ubiquitous sensing where user applications seamlessly access anytime, anywhere data produced by sensors embedded in the surroundings. With the ubiquity and ever-increasing capabilities of mobile devices, urban environments can help give substance to the ubiquitous sensing vision through Urbanets, spontaneously created urban networks. Urbanets consist of mobile multi-sensor devices, such as smart phones and vehicular systems, public sensor networks deployed by municipalities, and individual sensors incorporated in buildings, roads, or daily artifacts. My thesis is that "multi-sensor mobile devices can be successfully programmed to become the underpinning elements of an open, infrastructure-less, distributed sensing platform that can bring sensor data out of their traditional close-loop networks into everyday urban applications". Urbanets can support a variety of services ranging from emergency and surveillance to tourist guidance and entertainment. For instance, cars can be used to provide traffic information services to alert drivers to upcoming traffic jams, and phones to provide shopping recommender services to inform users of special offers at the mall. Urbanets cannot be programmed using traditional distributed computing models, which assume underlying networks with functionally homogeneous nodes, stable configurations, and known delays. Conversely, Urbanets have functionally heterogeneous nodes, volatile configurations, and unknown delays. Instead, solutions developed for sensor networks and mobile ad hoc networks can be leveraged to provide novel architectures that address Urbanet-specific requirements, while providing useful abstractions that hide the network complexity from the programmer. This dissertation presents two middleware architectures that can support mobile sensing applications in Urbanets. Contory offers a declarative programming model that views Urbanets as a distributed sensor database and exposes an SQL-like interface to developers. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous and semantically correct interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. In addition, they allow on-demand collection of sensor data with the accuracy and at the frequency required by every application. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in ad hoc networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.

Physical Oceanographic Influences on Queensland Reef Fish and Scallops : Final Report FRDC 2013/020

The project examined coastal and physical oceanographic influences on the catch rates of coral trout (Plectropomus leopardus) and saucer scallops (Amusium balloti) in Queensland. The research was undertaken to explain variation observed in the catches, and to improve quantitative assessment of the stocks and management advice. 3.1 OBJECTIVES 1. Review recent advances in the study of physical oceanographic influences on fisheries catch data, and describe the major physical oceanographic features that are likely to influence Queensland reef fish and saucer scallops. 2. Collate Queensland’s physical oceanographic data and fisheries (i.e. reef fish and saucer scallops) data. 3. Develop stochastic population models for reef fish and saucer scallops, which can link physical oceanographic features (e.g. sea surface temperature anomalies) to catch rates, biological parameters (e.g. growth, reproduction, natural mortality) and ecological aspects (e.g. spatial distribution).

Using relationship mapping to understand sustainable housing stakeholders’ actions

The production of sustainable housing requires the cooperation of a variety of participants with different goals, needs, levels of commitment and cultures. To achieve mainstream net zero energy housing objectives, there is arguably a need for a non-linear network of collaboration between all the stakeholders. In order to create and improve such collaborative networks between stakeholders, we first need to map stakeholders’ relationships, processes, and practices. This paper discusses compares and contrasts maps of the sustainable housing production life-cycle in Australia, developed from different perspectives. The paper highlights the strengths and weaknesses of each visualization, clarifying where gaps in connectivity exist within existing industry networks. Understanding these gaps will help researchers and practitioners identify how to improve the collaboration between participants in the housing industry. This in turn may improve decision making across all stakeholder groups, leading to mainstream implementation of sustainability into the housing industry.

TCP Performance with Vertical Handoff

With the proliferation of wireless and mobile devices equipped with multiple radio interfaces to connect to the Internet, vertical handoff involving different wireless access technologies will enable users to get the best of connectivity and service quality during the lifetime of a TCP connection. A vertical handoff may introduce an abrupt, significant change in the access link characteristics and as a result the end-to-end path characteristics such as the bandwidth and the round-trip time (RTT) of a TCP connection may change considerably. TCP may take several RTTs to adapt to these changes in path characteristics and during this interval there may be packet losses and / or inefficient utilization of the available bandwidth. In this thesis we study the behaviour and performance of TCP in the presence of a vertical handoff. We identify the different handoff scenarios that adversely affect TCP performance. We propose several enhancements to the TCP sender algorithm that are specific to the different handoff scenarios to adapt TCP better to a vertical handoff. Our algorithms are conservative in nature and make use of cross-layer information obtained from the lower layers regarding the characteristics of the access links involved in a handoff. We evaluate the proposed algorithms by extensive simulation of the various handoff scenarios involving access links with a wide range of bandwidth and delay. We show that the proposed algorithms are effective in improving the TCP behaviour in various handoff scenarios and do not adversely affect the performance of TCP in the absence of cross-layer information.

Climate change impact assessment: Uncertainty modeling with imprecise probability

Hydrologic impacts of climate change are usually assessed by downscaling the General Circulation Model (GCM) output of large-scale climate variables to local-scale hydrologic variables. Such an assessment is characterized by uncertainty resulting from the ensembles of projections generated with multiple GCMs, which is known as intermodel or GCM uncertainty. Ensemble averaging with the assignment of weights to GCMs based on model evaluation is one of the methods to address such uncertainty and is used in the present study for regional-scale impact assessment. GCM outputs of large-scale climate variables are downscaled to subdivisional-scale monsoon rainfall. Weights are assigned to the GCMs on the basis of model performance and model convergence, which are evaluated with the Cumulative Distribution Functions (CDFs) generated from the downscaled GCM output (for both 20th Century [20C3M] and future scenarios) and observed data. Ensemble averaging approach, with the assignment of weights to GCMs, is characterized by the uncertainty caused by partial ignorance, which stems from nonavailability of the outputs of some of the GCMs for a few scenarios (in Intergovernmental Panel on Climate Change [IPCC] data distribution center for Assessment Report 4 [AR4]). This uncertainty is modeled with imprecise probability, i.e., the probability being represented as an interval gray number. Furthermore, the CDF generated with one GCM is entirely different from that with another and therefore the use of multiple GCMs results in a band of CDFs. Representing this band of CDFs with a single valued weighted mean CDF may be misleading. Such a band of CDFs can only be represented with an envelope that contains all the CDFs generated with a number of GCMs. Imprecise CDF represents such an envelope, which not only contains the CDFs generated with all the available GCMs but also to an extent accounts for the uncertainty resulting from the missing GCM output. This concept of imprecise probability is also validated in the present study. The imprecise CDFs of monsoon rainfall are derived for three 30-year time slices, 2020s, 2050s and 2080s, with A1B, A2 and B1 scenarios. The model is demonstrated with the prediction of monsoon rainfall in Orissa meteorological subdivision, which shows a possible decreasing trend in the future.

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA)problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max-min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga-Bhadra river system in India.

Communities of wood-inhabiting fungi : Ecological requirements and responses to forest management and fragmentation

Despite much research on forest biodiversity in Fennoscandia, the exact mechanisms of species declines in dead-wood dependent fungi are still poorly understood. In particular, there is only limited information on why certain fungal species have responded negatively to habitat loss and fragmentation, while others have not. Understanding the mechanisms behind species declines would be essential for the design and development of ecologically effective and scientifically informed conservation measures, and management practices that would promote biodiversity in production forests. In this thesis I study the ecology of polypores and their responses to forest management, with a particular focus on why some species have declined more than others. The data considered in the thesis comprise altogether 98,318 dead-wood objects, with 43,085 observations of 174 fungal species. Out of these, 1,964 observations represent 58 red-listed species. The data were collected from 496 sites, including woodland key habitats, clear-cuts with retention trees, mature managed forests, and natural or natural-like forests in southern Finland and Russian Karelia. I show that the most relevant way of measuring resource availability can differ to a great extent between species seemingly sharing the same resources. It is thus critical to measure the availability of resources in a way that takes into account the ecological requirements of the species. The results show that connectivity at the local, landscape and regional scales is important especially for the highly specialized species, many of which are also red-listed. Habitat loss and fragmentation affect not only species diversity but also the relative abundances of the species and, consequently, species interactions and fungal successional pathways. Changes in species distributions and abundances are likely to affect the food chains in which wood-inhabiting fungi are involved, and thus the functioning of the whole forest ecosystem. The findings of my thesis highlight the importance of protecting well-connected, large and high-quality forest areas to maintain forest biodiversity. Small habitat patches distributed across the landscape are likely to contribute only marginally to protection of red-listed species, especially if habitat quality is not substantially higher than in ordinary managed forest, as is the case with woodland key habitats. Key habitats might supplement the forest protection network if they were delineated larger and if harvesting of individual trees was prohibited in them. Taking the landscape perspective into account in the design and development of conservation measures is critical while striving to halt the decline of forest biodiversity in an ecologically effective manner.

Quarantine and surveillance strategies for plant pathogen detection and control

Incursions of plant pests and diseases pose serious threats to food security, agricultural productivity and the natural environment. One of the challenges in confidently delimiting and eradicating incursions is how to choose from an arsenal of surveillance and quarantine approaches in order to best control multiple dispersal pathways. Anthropogenic spread (propagules carried on humans or transported on produce or equipment) can be controlled with quarantine measures, which in turn can vary in intensity. In contrast, environmental spread processes are more difficult to control, but often have a temporal signal (e.g. seasonality) which can introduce both challenges and opportunities for surveillance and control. This leads to complex decisions regarding when, where and how to search. Recent modelling investigations of surveillance performance have optimised the output of simulation models, and found that a risk-weighted randomised search can perform close to optimally. However, exactly how quarantine and surveillance strategies should change to reflect different dispersal modes remains largely unaddressed. Here we develop a spatial simulation model of a plant fungal-pathogen incursion into an agricultural region, and its subsequent surveillance and control. We include structural differences in dispersal via the interplay of biological, environmental and anthropogenic connectivity between host sites (farms). Our objective was to gain broad insights into the relative roles played by different spread modes in propagating an invasion, and how incorporating knowledge of these spread risks may improve approaches to quarantine restrictions and surveillance. We find that broad heuristic rules for quarantine restrictions fail to contain the pathogen due to residual connectivity between sites, but surveillance measures enable early detection and successfully lead to suppression of the pathogen in all farms. Alternative surveillance strategies attain similar levels of performance by incorporating environmental or anthropogenic dispersal risk in the prioritisation of sites. Our model provides the basis to develop essential insights into the effectiveness of different surveillance and quarantine decisions for fungal pathogen control. Parameterised for authentic settings it will aid our understanding of how the extent and resolution of interventions should suitably reflect the spatial structure of dispersal processes.

A conditional random field-based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin

Downscaling to station-scale hydrologic variables from large-scale atmospheric variables simulated by general circulation models (GCMs) is usually necessary to assess the hydrologic impact of climate change. This work presents CRF-downscaling, a new probabilistic downscaling method that represents the daily precipitation sequence as a conditional random field (CRF). The conditional distribution of the precipitation sequence at a site, given the daily atmospheric (large-scale) variable sequence, is modeled as a linear chain CRF. CRFs do not make assumptions on independence of observations, which gives them flexibility in using high-dimensional feature vectors. Maximum likelihood parameter estimation for the model is performed using limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization. Maximum a posteriori estimation is used to determine the most likely precipitation sequence for a given set of atmospheric input variables using the Viterbi algorithm. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework. The model is used to project the future cumulative distribution function of precipitation. Uncertainty in precipitation prediction is addressed through a modified Viterbi algorithm that predicts the n most likely sequences. The model is applied for downscaling monsoon (June-September) daily precipitation at eight sites in the Mahanadi basin in Orissa, India, using the MIROC3.2 medium-resolution GCM. The predicted distributions at all sites show an increase in the number of wet days, and also an increase in wet day precipitation amounts. A comparison of current and future predicted probability density functions for daily precipitation shows a change in shape of the density function with decreasing probability of lower precipitation and increasing probability of higher precipitation.

Understanding Protein Structure from a Percolation Perspective

Underlying the unique structures and diverse functions of proteins area vast range of amino-acid sequences and a highly limited number of folds taken up by the polypeptide backbone. By investigating the role of noncovalent connections at the backbone level and at the detailed side-chain level, we show that these unique structures emerge from interplay between random and selected features. Primarily, the protein structure network formed by these connections shows simple (bond) and higher order (clique) percolation behavior distinctly reminiscent of random network models. However, the clique percolation specific to the side-chain interaction network bears signatures unique to proteins characterized by a larger degree of connectivity than in random networks. These studies reflect some salient features of the manner in which amino acid sequences select the unique structure of proteins from the pool of a limited number of available folds.

Rapid quenching and glass formation in chalcogenide glasses: Preparation and properties of Ge-As-Te glasses over an extended composition ranges

In Ge-As-Te system, the glass forming region determined by normal melt quenching method has two regions (GFR I and GFR II) separated by few compositions gap. With a simple laboratory built twin roller apparatus, we have succeeded in preparing Ge7.5AsxTe92.5-x glasses over extended composition ranges. A distinct change in T-g is observed at x = 40, exactly at which the separation of the glass forming regions occur indicating the changes in the connectivity and the rigidity of the structural network. The maximum observed in glass transition (T-g) at x = 55 corresponding to the average coordination number (Z(av)) = 2.70 is an evidence for the shift of the rigidity percolation threshold (RPT) from Z(av) = 2.40 as predicted by the recent theories. The glass forming tendency (K-gl) and Delta T (=T-c-T-g) is low for the glasses in the GFR I and high for the glasses in the GFR II.

Evidence of an intermediate phase in ternary Ge7Se93-xSbx glasses

The compositional dependence of thermal properties, such as glass transition temperature (T-g), non-reversing enthalpy change (Delta H-NR) and the specific heat capacity change (Delta C-p) of melt quenched Ge7Se93-xSbx (21 a parts per thousand currency sign x a parts per thousand currency sign 31) glasses, has been studied using alternating differential scanning calorimetry (ADSC) which is analogous to modulated differential scanning calorimetry (MDSC). The glass transition temperature, T-g, which is a measure of global connectivity of the glass, has been found to increase with the addition of Sb. In addition, a change in slope has been observed in the composition dependence of T-g at an average coordination aOE (c) r > = 2.40. The experimentally observed compositional variation of glass transition temperature, has been compared with the theoretical predictions from the stochastic agglomeration theory (SAT) and has been found to be consistent. Further, a narrow thermally reversing window is seen in the compositional variation of the relaxation enthalpy (Delta H-NR), which is centered around aOE (c) r > = 2.40. The change in specific heat capacity (Delta C-p) at T-g is also found to exhibit a distinct minima at aOE (c) r > = 2.40, suggesting that the structural rearrangements for the liquid in the glass transition region are minimized around aOE (c) r > = 2.4.

Coupled Solution for Forced Recharge in Confined Aquifers

Analytical solutions for forced well recharge currently in use were initially developed for pumping scenarios and applied for recharge cases assuming that radial flow in the recharge well replicates a mirror image of that in to a pumping well. Moreover these solutions were not extended to multiaquifer systems. Well bore numerical solutions were generally not considering the effect of well bore interaction, which has a significant effect in the case of a recharge well. In the present paper, improved analytical solutions are developed for a well fully penetrating either single or multiaquifers in respect.to of well storage, well loss, and interactions between the individual aquifers through well bore. The solution developed for constant and variable rates of injection and well loss is applied to the experimental data of the Hansol well injection project near the city of Ahmedabad in the Gujarat state in India. The paper also discusses the difference in well hydraulics of recharge and recovery wells.

Regulation of GABAergic neuron identity and diversity in the developing midbrain

Gamma-aminobutyric acid (GABA) is the most abundant inhibitory neurotransmitter in the vertebrate brain. In the midbrain, GABAergic neurons contribute to the regulation of locomotion, nociception, defensive behaviours, fear and anxiety, as well as sensing reward and addiction. Despite the clinical relevance of this group of neurons, the mechanisms regulating their development are largely unknown. In addition, their migration and connectivity patterns are poorly characterized. This study focuses on the molecular mechanisms specifying the GABAergic fate, and the developmental origins of midbrain GABAergic neurons. First, we have characterized the function of a zink-finger transcription factor Gata2. Using a tissue-specific mutagenesis in mouse midbrain and anteror hindbrain, we showed that Gata2 is a crucial determinant of the GABAergic fate in midbrain. In the absence of Gata2, no GABAergic neurons are produced from the otherwise competent midbrain neuroepithelium. Instead, the Gata2-mutant cells acquire a glutamatergic neuron phenotype. Ectopic expression of Gata2 was also sufficient to induce GABAergic in chicken midbrain. Second, we have analyzed the midbrain phenotype of mice mutant for a proneural gene Ascl1, and described the variable and region-dependent requirements for Ascl1 in the midbrain GABAergic neurogenesis. These studies also have implications on the origin of distinct anatomical and functional GABAergic subpopulations in midbrain. Third, we have identified unique developmental properties of GABAergic neurons that are associated with the midbrain dopaminergic nuclei, the substantia nigra pars reticulata (SNpr) and ventral tegmental area (VTA). Namely, the genetic regulation of GABAergic fate in these cells is distinct from the rest of midbrain. In accordance to this phenomenon, our detailed fate-mapping analyses indicated that the SNpr-VTA GABAergic neurons are generated outside midbrain, in the neuroepithelium of anterior hindbrain.

Management of semi-natural grasslands for butterfly and moth communities

This work focuses on the factors affecting species richness, abundance and species composition of butterflies and moths in Finnish semi-natural grasslands, with a special interest in the effects of grazing management. In addition, an aim was set at evaluating the effectiveness of the support for livestock grazing in semi-natural grasslands, which is included in the Finnish agri-environment scheme. In the first field study, butterfly and moth communities in resumed semi-natural pastures were com-pared to old, annually grazed and abandoned previous pastures. Butterfly and moth species compo-sition in restored pastures resembled the compositions observed in old pastures after circa five years of resumed cattle grazing, but diversity of butterflies and moths in resumed pastures remained at a lower level compared with old pastures. None of the butterfly and moth species typical of old pas-tures had become more abundant in restored pastures compared with abandoned pastures. There-fore, it appears that restoration of butterfly and moth communities inhabiting semi-natural grass-lands requires a longer time that was available for monitoring in this study. In the second study, it was shown that local habitat quality has the largest impact on the occurrence and abundance of butterflies and moths compared to the effects of grassland patch area and connec-tivity of the regional grassland network. This emphasizes the importance of current and historical management of semi-natural grasslands on butterfly and moth communities. A positive effect of habitat connectivity was observed on total abundance of the declining butterflies and moths, sug-gesting that these species have strongest populations in well-connected habitat networks. Highest species richness and peak abundance of most individual species of butterflies and moths were generally observed in taller grassland vegetation compared with vascular plants, suggesting a preference towards less intensive management in insects. These differences between plants and their insect herbivores may be understood in the light of both (1) the higher structural diversity of tall vegetation and (2) weaker tolerance of disturbances by herbivorous insects due to their higher trophic level compared to plants. The ecological requirements of all species and species groups inhabiting semi-natural grasslands are probably never met at single restricted sites. Therefore, regional implementation of management to create differently managed areas is imperative for the conservation of different species and species groups dependent on semi-natural grasslands. With limited resources it might be reasonable to focus much of the management efforts in the densest networks of suitable habitat to minimise the risk of extinction of the declining species.