Analytical Functions for 200 West Pump-and-Treat SCADA Sensor Data

During the last decade the emergence of Internet of Things (IoT) based applications inspired the world by providing state of the art solutions to many common problems. From traffic management systems to urban cities planning and development, IoT based home monitoring systems, and many other smart applications. Regardless of these facilities, most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets. In order to address this problem, this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities. This hybrid model consists of; convolution neural network and binary long short term model for the object detection to ensure safety of the homes while IoT based hardware components like; Raspberry Pi, Amazon Web services cloud, and GSM modems for remotely accessing and controlling of the home appliances. An android application is developed and deployed on Amazon Web Services (AWS) cloud for the remote monitoring of home appliances. A GSM device and Message queuing telemetry transport (MQTT) are integrated for communicating with the connected IoT devices to ensure the online and offline communication. For object detection purposes a camera is connected to Raspberry Pi using the proposed hybrid neural network model. The applicability of the proposed model is tested by calculating results for the object at varying distance from the camera and for different intensity levels of the light. Besides many applications the proposed model promises for providing optimum results for the small amount of data and results in high recognition rates of 95.34% compared to the conventional recognition model (k nearest neighbours) recognition rate of 76%.

The rapid evolution of Amazon Web Services (AWS) cloud technologies continues to reshape enterprise computing environments across global industries. This technical review examines recent innovations in AWS services that are transforming organizational capabilities and competitive positioning. Beginning with computational performance advancements through AWS Graviton processors and specialized High-Performance Computing offerings, the article explores how these ARM-based architectures deliver enhanced efficiency across diverse workloads. The expanding AI and Machine Learning ecosystem, particularly through AWS Bedrock's foundation model integration and SageMaker's democratized ML tools, enables organizations to implement sophisticated intelligence capabilities without extensive expertise. Serverless computing developments, including Lambda Function URLs and SnapStart features, alongside visual workflow orchestration through Step Functions, have simplified application development while improving performance characteristics. Container orchestration enhancements facilitate consistent hybrid deployments across on-premises and cloud environments. Looking forward, AWS continues strategic investments in quantum computing initiatives, sustainability practices, and multi-cloud compatibility tools that position the platform at the forefront of cloud innovation. These advancements collectively enable organizations to achieve greater operational agility, cost efficiency, and innovation capacity while addressing evolving challenges in security, compliance, and technical workforce development.

This study delves into the optimization strategies of Amazon Web Services (AWS) cloud computing solutions tailored specifically for businesses. It provides an in-depth exploration of the transformative impact that cloud computing has had on modern business operations, emphasizing the pivotal role played by AWS in delivering scalable, flexible, and cost-effective resources. With projections indicating the global public cloud services market's growth to $332.3 billion in 2021, it becomes evident that cloud solutions are increasingly relied upon, with AWS commanding a significant 32% market share. This paper highlights the importance of optimizing AWS solutions, considering its extensive range of services designed to meet diverse business needs. However, transitioning to cloud environments brings challenges related to data security, compliance, integration, and migration complexities, as evidenced by pertinent studies and literature reviews. This research synthesizes insights gleaned from various studies, emphasizing the adaptability and cost-effectiveness of AWS while stressing the critical role of robust security measures in its implementation across industries and geographic regions. Understanding prevalent business requisites and challenges in cloud adoption, including scalability, cost efficiency, reliability, availability, and compliance, is essential for organizations seeking to harness cloud technology effectively. Furthermore, this paper provides an overview of AWS's services encompassing computing, storage, databases, machine learning, and AI, showcasing how these empower businesses to streamline operations, foster innovation, and scale dynamically. It elucidates successful instances where AWS was strategically implemented in Netflix, Airbnb, Jollibee Group, and Capital One, demonstrating its impact on scalability, innovation, and service enhancement. The paper outlines anticipated trends in AWS and cloud computing, focusing on user-friendly tools, advancements in machine learning, infrastructure improvements, increased automation, and deeper integration of AI and advanced analytics. Ultimately, this research emphasizes business's need to optimize AWS solutions, navigate complexities, foster innovation, and achieve operational excellence in an increasingly competitive market.

Enterprise IT infrastructures experience a transformation because of rapid cloud computing adoption, especially Amazon Web Services (AWS), which delivers scalability, flexibility, and cost efficiency. AWS users encounter difficulties optimizing their performance because they face problematic situations combining workload variability with complex resource distribution systems and delay issues. The implementation of Artificial Intelligence constructs an evaluation process to boost performance optimization in Amazon Web Services (AWS) cloud systems. AI-powered insights implemented through machine learning algorithms with predictive analytics capabilities and automation functionality improve workload distribution and enhance auto-scaling efficiency while reducing operational expenses. Processor analysis of historical data combined with real-time metrics allows AI algorithms to make proactive decisions about resource administration, detect anomalies, and perform innovative management. The research evaluates performance-altering AI-based approaches that enhance AWS services, including EC2, Lambda, and Sage Maker. Implementing AI-based optimizations generates copious improvements in computational efficiency and cost performance based on experimental data. Thanks to AI, cloud performance management strategies will fundamentally transform since they endorse efficient AWS environments that align with business requirements.

Depending on the requirements, the deployment of containerized applications for monitoring Internet of Things devices can be done in Cloud or on the Edge. This paper discusses the design of a containerized application monitoring temperature and humidity for IoT devices in three implementations: (1) balenaCloud for edge deployment; (2) Amazon Web Services cloud with EC2 instances; and (3) dedicated Amazon Web Services IoT service. The experiments showed that there are no significant performance differences between Edge and Cloud deployments regarding the resource consumption. However, for fast and almost effortless action, Amazon Web Services IoT is the go-to option when it comes to serverless applications based on Message Queuing Telemetry Transport. For all-in-one automated fleet management balenaCloud seems to be a good tool for IoT developers.

Coal seam gas (CSG) is a naturally occurring methane gas found in most coal seams and is similar to conventional natural gas. CSG is a major source of energy, accounting for about 15% of Australia's electricity generation. Also, CSG is an integral part of the gas industry in eastern Australia, particularly in Queensland. Origin Energy is the upstream operator for a leading joint venture in Australia and is responsible for the development of its CSG fields in the Surat and Bowen basins as well as the main transmission pipeline that transports the gas to the LNG facility on Curtis Island near Gladstone, Queensland. Origin Energy has been exploring ways to leverage modern technology solutions to increase field staff productivity, reduce operating costs per well, and improve asset reliability while also bringing more wells into production to meet growing customer demands. Field operators drive up to 100km per day and may have up to 20 well visits per day as part of a routine inspection that involves visual checks of the site to gain situational awareness. In recent years, with advancements in technologies such as edge compute (IoT), modern network connectivity options (satellite, narrow-band LTE) and Machine learning (no-code/low-code solutions) leveraging Cloud infrastructure early experiments are suggesting the implementation of safe, reliable, low-cost remote monitoring capabilities at scale is a possibility. This paper discusses how Amazon Web Services (AWS) collaborated with Origin Energy to deploy a trial focusing on remote monitoring of well pads using a camera solution. This trial captures images from the well pad at regular intervals, which when scaled out will help reduce the need for routine inspection of well pads by field operator at the current interval. This trial has also developed a data repository of all the images in the AWS Cloud and is designing a data processing pipeline to label images and use low-code/no-code ML to detect visual anomalies. In addition to the Origin Energy use cases (described in use case section 2.1 and 2.2) AWS has also helped an operator in North America (use case section 2.3) with building Machine learning models using Amazon SageMaker for anomaly detection. Hence, based on these projects it has become evident that using IoT, Machine Learning, and Cloud computing can accelerate innovation that drives down cost and increase operational safety of managing Oil & Gas assets remotely.

This research paper evaluates the advantages of Amazon Web Services (AWS) Cloud compared to conventional on-premise answers, highlighting the important thing services that incorporate the AWS platform. Launched in March 2006, AWS has grown drastically, with 94% of enterprises and 89% of organizations having as a minimum one utility or a part of their infrastructure in the cloud as of 2023. Additionally, 20% of enterprises are expected to adopt cloud programs and systems in the next year. AWS serves over one million active enterprise customers globally, imparting a complete suite of cloud-primarily based products, consisting of computing, data storage, databases, analytics, networking, mobile, developer tools, management tools, Internet of Things (IoT), security, and applications. These offerings are available on-demand, with a pay-as-you-go pricing model, and may be deployed fast without direct capital cost. This flexibility and scalability benefit start-ups, small and medium-sized businesses, and public sector customers, allowing them to respond swiftly to dynamic business requirements

This study aims to reduce the cost of using computing services on AWS. Cost reduction is needed because there is a possibility that the total cost of using cloud services exceeds the estimated budget. One type of EC2 that offers a large discount is the Spot Instance. The downside of this type of EC2 is that AWS reserves the right to stop it at any time. The proposed solution is an automation system to select and run EC2 Spot Instance types based on price, discount, amount of memory, and vCPU usage from previous instances. The automation system is built with the implementation of fuzzy inference system and Best-Worst Method (BWM). All input data is obtained using the Boto3 SDK. System deployment is done in Lambda functions. This Lambda function is automatically executed whenever a Spot Instance is terminated by AWS. The EventBridge service will catch the event and then trigger the Lambda to run. System testing was run for 4 (four) days with event simulation using the Send Events feature. From these tests it is known that the automation system can select the appropriate instance and generate a total cost of $3.85 (USD). After calculating the total cost with regular EC2 estimation (On Demand), the cost is reduced by 71.28%. This number proved to be 4.28% greater than previous similar studies.

Abstract—In this article I have deployed a web-based applica- tion to Docker using Amazon web service (EC2 Instance). Elastic Cloud Compute service is used to the run the docker applications on scalable clusters. Amazon EC2 is fully managed and secured Container orchestration service that makes to deploy, manage and scale Dockerized web application. Docker containers on an EC2 instance to run a university management system website on the Amazon Web Services (AWS) cloud. For educational institutions, a university management system is a crucial tool that enables administrators to manage student records, course information, and other crucial data. The introduction to Docker, a containerization technology that makes it simple to build and manage web applications, follows a discussion of the advantages of hosting a university management system website on the cloud. The next section of the paper focuses on the AWS EC2 service and its capabilities, such as its capability for flexible computing and scalability. The paper also offers a step-by-step tutorial on how to use Docker to deploy a university administration system website on an EC2 instance, including instructions on how to set up the EC2 instance, install and configure Docker, and deploy the website code in a Docker container. Elastic Load Balancer and Auto Scaling, two AWS services, are used in the process of setting up security and scalability for the website. The benefits of using Docker to host a university administration system website on AWS EC2 are outlined in the paper’s conclusion, along with predictions for the development of cloud-based web application hosting. This research paper offers a thorough overview of the steps involved in hosting a university management system website using Docker on AWS EC2, making it a useful tool for educational institutions looking to improve their information management procedures and take advantage of cloud computing and Docker- Containerization.

We present a user-friendly, cloud-based facility for quantifying methane emissions with 0.25° × 0.3125° (≈ 25 × 25 km2) resolution by inversion of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI). The facility is built on an Integrated Methane Inversion optimal estimation workflow (IMI 1.0) and supported for use on the Amazon Web Services (AWS) cloud. It exploits the GEOS-Chem chemical transport model and TROPOMI data already resident on AWS, thus avoiding cumbersome big-data download. Users select a region and period of interest, and the IMI returns an analytical solution for the Bayesian optimal estimate of emissions on the 0.25° × 0.3125° grid including error statistics, information content, and visualization code for inspection of results. An out-of-the-box inversion with rectilinear grid and default prior emission estimates can be conducted with no significant learning curve. Users can also configure their inversions to infer emissions for irregular regions of interest, swap in their own prior emission inventories, and modify inversion parameters. Inversion ensembles can be generated at minimal additional cost once the Jacobian matrix for the analytical inversion has been constructed. A preview feature allows users to determine the TROPOMI information content for their region and time period of interest before actually performing the inversion. The IMI is heavily documented and is intended to be accessible by researchers and stakeholders with no expertise in inverse modelling or high-performance computing. We demonstrate the IMI’s capabilities by applying it to estimate methane emissions from the US oil-producing Permian Basin in May 2018.

9 - Digital Twin Development and cloud deployment for a DC Motor Control embedded system

<strong class="journal-contentHeaderColor">Abstract.</strong> We present a user-friendly, cloud-based facility for quantifying methane emissions with 0.25<span class="inline-formula">^∘</span> <span class="inline-formula">×</span> 0.3125<span class="inline-formula">^∘</span> (<span class="inline-formula">≈</span> 25 <span class="inline-formula">km</span> <span class="inline-formula">×</span> 25 <span class="inline-formula">km</span>) resolution by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI). The facility is built on an Integrated Methane Inversion optimal estimation workflow (IMI 1.0) and supported for use on the Amazon Web Services (AWS) cloud. It exploits the GEOS-Chem chemical transport model and TROPOMI data already resident on AWS, thus avoiding cumbersome big-data download. Users select a region and period of interest, and the IMI returns an analytical solution for the Bayesian optimal estimate of period-average emissions on the 0.25<span class="inline-formula">^∘</span> <span class="inline-formula">×</span> 0.3125<span class="inline-formula">^∘</span> grid including error statistics, information content, and visualization code for inspection of results. The inversion uses an advanced research-grade algorithm fully documented in the literature. An out-of-the-box inversion with rectilinear grid and default prior emission estimates can be conducted with no significant learning curve. Users can also configure their inversions to infer emissions for irregular regions of interest, swap in their own prior emission inventories, and modify inversion parameters. Inversion ensembles can be generated at minimal additional cost once the Jacobian matrix for the analytical inversion has been constructed. A preview feature allows users to determine the TROPOMI information content for their region and time period of interest before actually performing the inversion. The IMI is heavily documented and is intended to be accessible by researchers and stakeholders with no expertise in inverse modelling or high-performance computing. We demonstrate the IMI's capabilities by applying it to estimate methane emissions from the US oil-producing Permian Basin in May 2018.

BackgroundWhole-genome sequencing (WGS) plays an increasingly important role in clinical practice and public health. Due to the big data size, WGS data analysis is usually compute-intensive and IO-intensive. Currently it usually takes 30 to 40 h to finish a 50× WGS analysis task, which is far from the ideal speed required by the industry. Furthermore, the high-end infrastructure required by WGS computing is costly in terms of time and money. In this paper, we aim to improve the time efficiency of WGS analysis and minimize the cost by elastic cloud computing.ResultsWe developed a distributed system, GT-WGS, for large-scale WGS analyses utilizing the Amazon Web Services (AWS). Our system won the first prize on the Wind and Cloud challenge held by Genomics and Cloud Technology Alliance conference (GCTA) committee. The system makes full use of the dynamic pricing mechanism of AWS. We evaluate the performance of GT-WGS with a 55× WGS dataset (400GB fastq) provided by the GCTA 2017 competition. In the best case, it only took 18.4 min to finish the analysis and the AWS cost of the whole process is only 16.5 US dollars. The accuracy of GT-WGS is 99.9% consistent with that of the Genome Analysis Toolkit (GATK) best practice. We also evaluated the performance of GT-WGS performance on a real-world dataset provided by the XiangYa hospital, which consists of 5× whole-genome dataset with 500 samples, and on average GT-WGS managed to finish one 5× WGS analysis task in 2.4 min at a cost of $3.6.ConclusionsWGS is already playing an important role in guiding therapeutic intervention. However, its application is limited by the time cost and computing cost. GT-WGS excelled as an efficient and affordable WGS analyses tool to address this problem. The demo video and supplementary materials of GT-WGS can be accessed at https://github.com/Genetalks/wgs_analysis_demo.

Diabetes is a widespread chronic metabolic disorder that affects a substantial portion of the global population and requires diligent management and continuous medical supervision. Traditional glucose monitoring methods often involve invasive procedures that cause discomfort and increase the risk of infection. With the rapid advancements of the Internet of Things (IoT) and cloud computing, healthcare is evolving for early disease detection and personalized treatment solutions. The aim of this research is to develop a system based on the Internet of Things (IoT) for non-invasive diabetes detection using Photoplethysmography (PPG) signals. Our system leverages machine learning models, such as XGBoost, Random Forest, Logistic Regression, and SVM, to accurately classify diabetic patients using statistical features collected from PPG signals and metadata. To ensure model robustness, we employed a fivefold cross-validation approach. The hardware technology includes a portable, cost-effective system with a simple NIR sensor and a high-performing ESP32 module that minimizes user discomfort and ensures easy integration. Deployed on the Amazon Web Services (AWS) cloud platform, our system enables real-time data collection and analysis, providing users with a convenient web application for monitoring their diabetic status. The performance of the developed system has been tested with two different datasets: the PPG-BP dataset and the Mazandaran dataset, achieving 87.88% and 90% accuracy, respectively. The results demonstrate the potential of the designed system to revolutionize diabetes management by providing a non-invasive, accessible, and accurate solution for early-stage detection.

Management of networked Internet of Things (IoT) infrastructure with in-network processing capabilities is becoming increasingly difficult due to the volatility of the system with low-cost resource-constraint devices. Traditional software-defined networking (SDN) based management systems are not suitable to handle the plug and play nature of such systems. Therefore, in this paper, we propose Aloe, an elastically auto-scalable SDN orchestration framework. Instead of using service grade SDN controller applications, Aloe uses multiple lightweight controller instances to exploit the capabilities of in-network processing infrastructure. The proposed framework ensures the availability and significant reduction in flow-setup delay by deploying instances near the resource constraint IoT devices dynamically. Aloe supports fault-tolerance and can recover from network partitioning by employing self-stabilizing placement of migration capable controller instances. The performance of the proposed system is measured by using an in-house testbed along with a large scale deployment in Amazon web services (AWS) cloud platform. The experimental results from these two testbed show significant improvement in response time for standard IoT based services. This improvement of performance is due to the reduction in flow-setup time. We found that Aloe can improve flow-setup time by around 10%-30% in comparison to one of the state of the art orchestration framework.