Adaptive sampling for spatial prediction in wireless sensor networks

University of Technology, Sydney. Faculty of Engineering and Information Technology.Networks of wireless sensors are increasingly exploited in crucial applications of monitoring spatially correlated environmental phenomena such as temperature, rainfall, soil ingredients, and air pollution. Such networks enable efficient monitoring and measurements can be included in developing models of the environmental fields even at unobserved locations. This requires determining the number of sensors and their sampling locations which minimize the uncertainty of predictions. Therefore, the aim of this thesis is to present novel, efficient and practically feasible approaches to sample the environments, so that the uncertainties at unobserved locations are minimized. Gaussian process (GP) is utilized to statistically model the spatial field. This thesis includes both stationary wireless sensor networks (SWSNs) and mobile robotic wireless sensor networks (MRWSNs), and thus the issues are correspondingly formulated into sensor selection and sensor placement problems, respectively. In the first part of the thesis, a novel performance metric for the sensor selection in the SWSNs, named average root mean square error, which reflects the average uncertainty of each predicted location, is proposed. In order to minimize this NP-hard and combinatorial optimization problem, a simulated annealing based algorithm is proposed; and the sensor selection problem is effectively addressed. Particularly, when considering the sensor selection in constrained environments, e.g. gas phase hydrogen sulphide in a sewage system, a modified GP with an improved covariance function is developed. An efficient mutual information maximization criterion suitable for this particular scenario is also presented to select the most informative gaseous sensor locations along the sewer system. The second part of this thesis introduces centralized and distributed methods for spatial prediction over time in the MRWSNs. For the purpose of finding the optimal sampling paths of the mobile wireless sensors to take the most informative observations at each time iteration, a sampling strategy is proposed based on minimizing the uncertainty at all unobserved locations. A novel and very efficient optimality criterion for the adaptive sampling problem is then presented so that the minimization can be addressed by a greedy algorithm in polynomial time. The solution is proven to be bounded; and computational time of the proposed algorithm is illustrated to be practically feasible for the resource-constrained MRWSNs. In order to enhance the issue of computational complexity, Gaussian Markov random field (GMRF) is utilized to model the spatial field exploiting sparsity of the precision matrix. A new GMRF optimality criterion for the adaptive navigation problem is also proposed such that computational complexity of a greedy algorithm to solve the resulting optimization is deterministic even with increasing number of measurements. Based on the realistic simulations conducted using the pre-published data sets, it has shown that the proposed algorithms are superior with appealing results

Learning Latent Distribution for Distinguishing Network Traffic in Intrusion Detection System

© 2019 IEEE. We develop a novel deep learning model, Multi-distributed Variational AutoEncoder (MVAE), for the network intrusion detection. To make the traffic more distinguishable, MVAE introduces the label information of data samples into the Kullback-Leibler (KL) term of the loss function of Variational AutoEncoder (VAE). This label information allows MVAEs to force/partition network data samples into different classes with different regions in the latent feature space. As a result, the network traffic samples are more distinguishable in the new representation space (i.e., the latent feature space of MVAE), thereby improving the accuracy in detecting intrusions. To evaluate the efficiency of the proposed solution, we carry out intensive experiments on two popular network intrusion datasets, i.e., NSL-KDD and UNSW-NB15 under four conventional classifiers including Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The experimental results demonstrate that our proposed approach can significantly improve the accuracy of intrusion detection algorithms up to 24.6% compared to the original one (using area under the curve metric)

Economic valuation of wetland ecosystem services in northeastern part of Vietnam

Coastal wetlands have been heavily exploited in the world. Valuation of ecosystem services help to provide the necessary improvements in coastal policy and management to monitor the driving forces of ecological changes in wetland ecosystems. In this study, the monetary values of wetland ecosystem services (WES) in the northeastern part of Vietnam were evaluated based on the integration of different quantitative methods, including interview, remote sensing, ecological modeling, statistic, and cost-benefit analyses. Particularly, seven wetland ecosystems and eleven services obtained from them were identified. As a result, the annual net WES value is evaluated at more than 390 million USD. The intensive and industrial aquaculture ecosystems in the northeastern part represent the highest economic value with more than 2100 USD/ha/year. A a planninga scenario was formulated to predict WES for the next ten years based on policy changes published by local managers. The framework developed here can serve as a decision support tool for environmental and economic managers in wetlands planning

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

A Condensation-Ordering Mechanism in Nanoparticle-Catalyzed Peptide Aggregation

Nanoparticles introduced in living cells are capable of strongly promoting the aggregation of peptides and proteins. We use here molecular dynamics simulations to characterise in detail the process by which nanoparticle surfaces catalyse the self- assembly of peptides into fibrillar structures. The simulation of a system of hundreds of peptides over the millisecond timescale enables us to show that the mechanism of aggregation involves a first phase in which small structurally disordered oligomers assemble onto the nanoparticle and a second phase in which they evolve into highly ordered beta-sheets as their size increases

Clinical significance of VEGF-A, -C and -D expression in esophageal malignancies

Vascular endothelial growth factors ( VEGF)- A, - C and - D are members of the proangiogenic VEGF family of glycoproteins. VEGF-A is known to be the most important angiogenic factor under physiological and pathological conditions, while VEGF-C and VEGF-D are implicated in the development and sprouting of lymphatic vessels, so called lymphangiogenesis. Local tumor progression, lymph node metastases and hematogenous tumor spread are important prognostic factors for esophageal carcinoma ( EC), one of the most lethal malignancies throughout the world. We found solid evidence in the literature that VEGF expression contributes to tumor angiogenesis, tumor progression and lymph node metastasis in esophageal squamous cell carcinoma ( SCC), and many authors could show a prognostic value for VEGF-assessment. In adenocarcinoma (AC) of the esophagus angiogenic properties are acquired in early stages, particularly in precancerous lesions like Barrett's dysplasia. However, VEGF expression fails to give prognostic information in AC of the esophagus. VEGF-C and VEGF-D were detected in SCC and dysplastic lesions, but not in normal mucosa of the esophagus. VEGF-C expression might be associated with lymphatic tumor invasion, lymph node metastases and advanced disease in esophageal SCC and AC. Therapeutic interference with VEGF signaling may prove to be a promising way of anti-angiogenic co-treatment in esophageal carcinoma. However, concrete clinical data are still pending

Direct comparison of methionine restriction with leucine restriction on the metabolic health of C57BL/6J mice

EKL was the recipient of a BBSRC postgraduate studentship. This work was funded by Tenovus Scotland project grant to MD and NM (G13/07) and BBSRC DTG. MD is also supported by the British Heart Foundation (PG/09/048/27675, PG/11/8/28703 and PG/14/43/30889) and Diabetes UK (14/0004853). NM is funded by British Heart Foundation (PG/16/90/32518).Peer reviewedPublisher PD

Long-term sediment decline causes ongoing shrinkage of the Mekong megadelta, Vietnam

Since the 1990s the Mekong River delta has suffered a large decline in sediment supply causing coastal erosion, following catchment disturbance through hydropower dam construction and sand extraction. However, our new geological reconstruction of 2500-years of delta shoreline changes show that serious coastal erosion actually started much earlier. Data shows the sandy coast bounding river mouths accreted consistently at a rate of +2 to +4 km2/year. In contrast, we identified a variable accretion rate of the muddy deltaic protrusion at Camau; it was < +1 km2/year before 1400 years ago but increased drastically around 600 years ago, forming the entire Camau Peninsula. This high level of mud supply had sharply declined by the early 20th century after a vast canal network was built on the delta. Since then the Peninsula has been eroding, promoted by the conjunction of mud sequestration in the delta plain driven by expansion of rice cultivation, and hysteresis of long-term muddy sedimentation that left the protrusion exposed to wave erosion. Natural mitigation would require substantial increases in sediment supply well above the pre-1990s levels