A Data-driven, High-performance and Intelligent CyberInfrastructure to Advance Spatial Sciences

abstract: In the field of Geographic Information Science (GIScience), we have witnessed the unprecedented data deluge brought about by the rapid advancement of high-resolution data observing technologies. For example, with the advancement of Earth Observation (EO) technologies, a massive amount of EO data including remote sensing data and other sensor observation data about earthquake, climate, ocean, hydrology, volcano, glacier, etc., are being collected on a daily basis by a wide range of organizations. In addition to the observation data, human-generated data including microblogs, photos, consumption records, evaluations, unstructured webpages and other Volunteered Geographical Information (VGI) are incessantly generated and shared on the Internet. Meanwhile, the emerging cyberinfrastructure rapidly increases our capacity for handling such massive data with regard to data collection and management, data integration and interoperability, data transmission and visualization, high-performance computing, etc. Cyberinfrastructure (CI) consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high-performance networks to improve research productivity and enable breakthroughs that are not otherwise possible. The Geospatial CI (GCI, or CyberGIS), as the synthesis of CI and GIScience has inherent advantages in enabling computationally intensive spatial analysis and modeling (SAM) and collaborative geospatial problem solving and decision making. This dissertation is dedicated to addressing several critical issues and improving the performance of existing methodologies and systems in the field of CyberGIS. My dissertation will include three parts: The first part is focused on developing methodologies to help public researchers find appropriate open geo-spatial datasets from millions of records provided by thousands of organizations scattered around the world efficiently and effectively. Machine learning and semantic search methods will be utilized in this research. The second part develops an interoperable and replicable geoprocessing service by synthesizing the high-performance computing (HPC) environment, the core spatial statistic/analysis algorithms from the widely adopted open source python package – Python Spatial Analysis Library (PySAL), and rich datasets acquired from the first research. The third part is dedicated to studying optimization strategies for feature data transmission and visualization. This study is intended for solving the performance issue in large feature data transmission through the Internet and visualization on the client (browser) side. Taken together, the three parts constitute an endeavor towards the methodological improvement and implementation practice of the data-driven, high-performance and intelligent CI to advance spatial sciences.Dissertation/ThesisDoctoral Dissertation Geography 201

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

Emerging Informatics

The book on emerging informatics brings together the new concepts and applications that will help define and outline problem solving methods and features in designing business and human systems. It covers international aspects of information systems design in which many relevant technologies are introduced for the welfare of human and business systems. This initiative can be viewed as an emergent area of informatics that helps better conceptualise and design new world-class solutions. The book provides four flexible sections that accommodate total of fourteen chapters. The section specifies learning contexts in emerging fields. Each chapter presents a clear basis through the problem conception and its applicable technological solutions. I hope this will help further exploration of knowledge in the informatics discipline

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

Geographic Information Systems for Real-Time Environmental Sensing at Multiple Scales

The purpose of this investigation was to design, implement, and apply a real-time geographic information system for data intensive water resource research and management. The research presented is part of an ongoing, interdisciplinary research program supporting the development of the Intelligent River® observation instrument. The objectives of this research were to 1) design and describe software architecture for a streaming environmental sensing information system, 2) implement and evaluate the proposed information system, and 3) apply the information system for monitoring, analysis, and visualization of an urban stormwater improvement project located in the City of Aiken, South Carolina, USA. This research contributes to the fields of software architecture and urban ecohydrology. The first contribution is a formal architectural description of a streaming environmental sensing information system. This research demonstrates the operation of the information system and provides a reference point for future software implementations. Contributions to urban ecohydrology are in three areas. First, a characterization of soil properties for the study region of the City of Aiken, SC is provided. The analysis includes an evaluation of spatial structure for soil hydrologic properties. Findings indicate no detectable structure at the scales explored during the study. The second contribution to ecohydrology comes from a long-term, continuous monitoring program for bioinfiltration basin structures located in the study area. Results include an analysis of soil moisture dynamics based on data collected at multiple depths with high spatial and temporal resolution. A novel metric is introduced to evaluate the long-term performance of bioinfiltration basin structures based on soil moisture observation data. Findings indicate a decrease in basin performance over time for the monitored sites. The third contribution to the field of ecohydrology is the development and application of a spatially and temporally explicit rainfall infiltration and excess model. The model enables the simulation and visualization of bioinfiltration basin hydrologic response at within-catchment scales. The model is validated against observed soil moisture data. Results include visualizations and stormwater volume calculations based on measured versus predicted bioinfiltration basin performance over time

Design and Implementation of an IoT Platform for Flood Prediction

Flooding, a major natural calamity, severely threatens communities and infrastructures in areas susceptible to floods. Consequently, implementing an Internet of Things (IoT)-based flood monitoring system becomes crucial. Existing flood monitoring systems lack a comprehensive and scalable IoT platform to collect real-time data from diverse sensors efficiently, visualize flood information, and provide accurate water level forecasts. This thesis proposes a complete system designed to address the challenges associated with efficient data collection and flood monitoring from diverse IoT sensors. Our proposition involves creating and deploying a centralized system known as HYDROSIGHT, which facilitates the real-time gathering, monitoring, and visualization of flooding-related sensor data. HYDROSIGHT system also provides a log monitoring feature for effective debugging and troubleshooting. The IoT environment for flood monitoring and prediction system was designed to promote sustainability and autonomy by preferring sensors with minimal footprints and compatibility with solar panels. The system architecture leverages a 4G network for seamless data transmission. To validate the practical applicability of the proposed design,HYDROSIGHT system was tested at two municipalities of Quebec, namely Terrebonne, and Lac-Supérieur. In addition, the platform was also deployed at the Ericsson facility in Montreal to test the 5G capabilities. The deployment in these locations allowed us to evaluate the performance and effectiveness of the HYDROSIGHT system in a real flood monitoring environment. In addition to implementing the IoT testbed, a preliminary machine learning tool was developed on water level forecasting. In this experiment, we opted for an online machine-learning approach, recognizing the significance of real-time updates and low computational resources of IoT devices. Leveraging the constantly updating data from HYDROSIGHT, we trained and tested our online machine-learning model, enhancing its forecasting capabilities. We conducted a comparative analysis to understand the advantages of online machine learning over traditional batch learning. This analysis involved examining the water level forecasting results obtained from both methods using time series data from the HYDROSIGHT system deployed at Lac-Supérieur in Quebec

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

Relating Ancient Maya Land Use Legacies To The Contemporary Forest Of Caracol, Belize

Human land use legacies have significant and long lasting impacts across landscapes. However, investigating the impacts of ancient land use legacies ( \u3e 400 years) remains problematic due to the difficulty in detecting ancient land uses, especially those beneath dense canopies. The city of Caracol, one of the most important Maya archaeological sites in Belize, was abandoned after the collapse of the Maya civilization (ca. A.D. 900), leaving behind numerous structures, causeways, and agricultural terraces that persist beneath the dense tropical forest of western Belize. LiDAR (Light Detection and Ranging) technology enables detection of below canopy Maya archaeological features, providing an ideal opportunity to study the effects of ancient land use legacies on contemporary tropical forest composition. LiDAR also provided us with a detailed record of the 3-dimensional forest structure over the 200 km2 study area. This allowed the investigation how ancient land uses continue to impact both forest composition, in terms of tree species, and forest structure. I recorded tree species over four land use categories: 1) structures, 2) causeways, 3) terraced, and 4) non-terraced land. Using non-metric multidimensional scaling (NMS) and multiresponse permutation procedures (MRPP) to test for differences between the classes, I found significantly distinct tree communities associated with the presence of terraces and the underlying topography. Terraced slopes appear to function as micro-valleys on the side of a hill, creating an environmental bridge between slope and valley tree communities. Tree species composition over causeways and structures was also found to be significantly different from terraced and non-terraced plots. Forest structure was assessed by extracting LiDAR points for terraced (n=150) and nonterraced (n=150) 0.25 ha plots. I calculated average canopy height, canopy closure, and vertical diversity from the height bins of the LiDAR points, using slope, elevation, and aspect as covariates. Using PerMANOVA I determined that forest structure over terraces was significantly different from non-terraced land. Terraces appear to mediate the effect of slope, resulting in less structural variation between slope and non-sloped land. These results led to the conclusion that human land uses abandoned \u3e1000 years ago continue to impact the contemporary forests