Cloud-based Monitoring Research Articles

MPPT Solar Controller: A Theoretical Design and Prototype Development

This paper discusses the theoretical design and ongoing prototype development of a Maximum Power Point Tracking (MPPT) solar controller system aimed at efficient energy harvesting and smart load management. The system is designed to optimize energy extraction from a solar panel using MPPT algorithms, with smart load management for real-time power distribution. The controller incorporates cloud-based monitoring, AI optimization, and a user-friendly interface. The system is expected to deliver robust, efficient, and safe operation under varying environmental conditions

A computer vision and point cloud-based monitoring approach for automated construction tasks using full-scale robotized mobile cranes

A computer vision and point cloud-based monitoring approach for automated construction tasks using full-scale robotized mobile cranes

A Cloud Infrastructure for Health Monitoring in Emergency Response Scenarios.

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system's reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts.

Leveraging deep learning and computer vision technologies to enhance management of coastal fisheries in the Pacific region

This paper presents the design and development of a coastal fisheries monitoring system that harnesses artificial intelligence technologies. Application of the system across the Pacific region promises to revolutionize coastal fisheries management. The program is built on a centralized, cloud-based monitoring system to automate data extraction and analysis processes. The system leverages YoloV4, OpenCV, and ResNet101 to extract information from images of fish and invertebrates collected as part of in-country monitoring programs overseen by national fisheries authorities. As of December 2023, the system has facilitated automated identification of over six hundred nearshore finfish species, and automated length and weight measurements of more than 80,000 specimens across the Pacific. The system integrates other key fisheries monitoring data such as catch rates, fishing locations and habitats, volumes, pricing, and market characteristics. The collection of these metrics supports much needed rapid fishery assessments. The system’s co-development with national fisheries authorities and the geographic extent of its application enables capacity development and broader local inclusion of fishing communities in fisheries management. In doing so, the system empowers fishers to work with fisheries authorities to enable data-informed decision-making for more effective adaptive fisheries management. The system overcomes historically entrenched technical and financial barriers in fisheries management in many Pacific island communities.

MAAS - Monitoring as a Service through Payment Gateway

Abstract: This paper describes the creation of a cloud-based Monitoring as a Service (MaaS) platform designed specifically for thorough and safe payment gateway monitoring. Scalability, strong security, and real-time transaction insights are provided by the MaaS project through the use of the ELK Stack (Elasticsearch, Logstash, Kibana). Flexibility in monitoring modules allows for the smooth integration of the ELK Stack for effective analysis and display of vital payment gateway data. An interface that is easy to use enables operators to keep an eye on transactions and spot possible problems. Further integration with internal security and fraud detection tools is made easier by thoroughly described APIs. The goal of this MaaS platform is to improve oversight capabilities for crucial payment gateway operations by providing a consolidated, secure monitoring solution. Future development will involve adding machine learning for anomaly detection and extending data source support to cover other payment channels.

Ship Operation Analysis and Optimization via Mobile Application

This paper is devoted to studying the structure and prospects of ship operations analysis and optimization via mobile applications, focusing on integrating multiple existing onboard monitoring and control systems. The main parts of the paper describe the current state of the most essential components of future overall shipping and ship design optimization using onboard and cloud-based monitoring systems from a dual transition point of view. Special attention is paid to the ship’s and its equipment’s efficiency improvements, fuel consumption and emissions reduction, cost-effectiveness enhancement, metrological accuracy, and compliance with current regulations. Timely development and deployment of the proposed onboard monitoring systems, in combination with up-to-date mobile applications and cloud computing, should play a crucial role in promoting sustainable and environmentally friendly shipping practices, improving operational performance, and reducing risks to human life at sea and the environmental impact of shipping. Another objective of this research is to review the current state of infrastructure used for fuel control, focusing on measurement systems and related analytics using appropriate mobile applications. The recommendations for integrating new and adopting existing monitoring systems and equipment for promising alternative fuels must be given to meet current regulations and provide required safety levels and measurement quality.

Enhancing Turbine Blade Manufacturing through MEMS-Based Milling Monitoring

Manufacturing steam turbine blades with intricate shapes poses a substantial challenge. These blades significantly impact turbine efficiency, reliability, and productivity. Their precise formation through milling processes demands vigilant monitoring, especially concerning the cutting tool’s condition, which is responsible for shaping the blade profiles and ensuring high-quality outcomes. Direct tool monitoring, however, disrupts productivity, prompting the need for an indirect Tool Condition Monitoring (TCM) system. Such a system becomes essential for detecting tool wear and damage early, ensuring dimensional accuracy and surface smoothness. This experiment monitors tool conditions by analyzing vibrations generated by an endmill cutter while machining martensitic stainless steel (MSS) AISI 420. The setup employs a cost-effective MEMS vibration sensor, the ADXL 345, and Raspberry Pi for real-time online TCM functionality as the signal processor. This study delves into the sensor’s ability to capture vibrations representing actual tool conditions, focusing on the affordability and effectiveness of MEMS-based monitoring. The successful real-time implementation of MEMS-based monitoring could be an easily accessible gateway for manufacturing industries to embrace cloud-based monitoring systems, aligning with current technological trends. This research aims to underscore the viability and potential adoption of affordable MEMS sensors for comprehensive tool condition monitoring, offering a seamless transition toward cloud-integrated monitoring systems for manufacturing industries.

MAAS- Monitoring as a Service through Payment Gateway

Abstract: This paper describes the creation of a cloud-based Monitoring as a Service (MaaS) platform designed specifically for thorough and safe payment gateway monitoring. Scalability, strong security, and real-time transaction insights are provided by the MaaS project through the use of the ELK Stack (Elasticsearch, Logstash, Kibana). Flexibility in monitoring modules allows for the smooth integration of the ELK Stack for effective analysis and display of vital payment gateway data. An interface that is easy to use enables operators to keep an eye on transactions and spot possible problems. Further integration with internal security and fraud detection tools is made easier by thoroughly described APIs. The goal of this MaaS platform is to improve oversight capabilities for crucial payment gateway operations by providing a consolidated, secure monitoring solution. Future development will involve adding machine learning for anomaly detection and extending data source support to cover other payment channels.

Applying the MIMO BP Neural Network and Cloud-Based Monitoring of Thermal Behavior for High-Speed Motorized Spindle Units

Understanding the temperature–working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This paper presents an in-depth analysis of the thermal equilibrium and deformation characteristics of a high-speed motorized spindle unit utilized in grinding machine tools. Through a series of thermal equilibrium experiments and meticulous data acquisition, the study investigates the nuanced influence of various working conditions, including spindle speeds, coolant types, and coolant flow rates, on spindle temperatures and thermal deformations. Leveraging the power of Artificial Neural Networks (ANNs), predictive models are meticulously developed to accurately forecast spindle behavior. Subsequently, the models are seamlessly transitioned to a cloud computing infrastructure to ensure remote accessibility and scalability, facilitating real-time monitoring and forecasting of spindle performance. The validity and reliability of the predictive models are rigorously assessed through comparison with experimental data, demonstrating excellent agreement and high accuracy in forecasting spindle thermal behavior. Furthermore, the study underscores the critical role of key working condition variables as precise predictors of spindle temperature and thermal deformation, emphasizing their significance in optimizing overall spindle efficiency and performance. This comprehensive analysis offers valuable insights and practical implications for enhancing spindle operation and advancing the field of grinding machine tools.

A Digitally Enabled, Pharmacist service to detecT medicine harms in residential aged care (nursing home) (ADEPT): protocol for a feasibility study

IntroductionThis feasibility study aims to develop and test a new model of practice in Australia using digital technologies to enable pharmacists to monitor early signs and symptoms of medicine-induced harms...

Decoding Type 2 Diabetes Through Point-of-Care Testing, Cloud-based Monitoring, and Generative Augmented Retrieval Model-driven Virtual Diabetes Education: A Comprehensive Approach to Glycemic Control

Abstract In a type 2 diabetes study, participants in the intervention group, exposed to artificial intelligence-driven diabetes nudges, exhibited remarkable ameliorations compared to the control cohort receiving standard care. Notable biochemical improvements included a significant mean reduction in hemoglobin A1C levels by 1.25, indicating enhanced glycemic control over the protracted term. Furthermore, the intervention group manifested a substantial plasma glucose delta of 26.1, denoting improvements in short-term glycemic indices. In-depth analyses unearthed favorable impacts on various glycemic parameters: A noteworthy glycemic cap enhancement of 0.97, a pronounced postprandial glucose decrement by 49.48, and a discernible glucose score reduction of 3.94. In addition, the intervention engendered favorable shifts in dietary metrics, as evidenced by the food zone improvement (6.49) and food comparison elevation (6.12). These findings, bolstered by robust statistical analyses and graphically depicted trends, underscore the intervention’s efficacy in instigating comprehensive advancements in glycemic control, both immediate and sustained, as denoted by the intricate web of medical parameters assessed in this comprehensive investigation.

IoT-Based Big Data Secure Transmission and Management over Cloud System: A Healthcare Digital Twin Scenario

The Internet of Things (IoT) was introduced as a recently developed technology in the telecommunications field. It is a network made up of real-world objects, things, and gadgets that are enabled by sensors and software that can communicate data with one another. Systems for monitoring gather, exchange, and process video and image data captured by sensors and cameras across a network. Furthermore, the novel concept of Digital Twin offers new opportunities so that new proposed systems can work virtually, but without differing in operation from a “real” system. This paper is a meticulous survey of the IoT and monitoring systems to illustrate how their combination will improve certain types of the Monitoring systems of Healthcare–IoT in the Cloud. To achieve this goal, we discuss the characteristics of the IoT that improve the use of the types of monitoring systems over a Multimedia Transmission System in the Cloud. The paper also discusses some technical challenges of Multimedia in IoT, based on Healthcare data. Finally, it shows how the Mobile Cloud Computing (MCC) technology, settled as base technology, enhances the functionality of the IoT and has an impact on various types of monitoring technology, and also it proposes an algorithm approach to transmitting and processing video/image data through a Cloud-based Monitoring system. To gather pertinent data about the validity of our proposal in a more safe and useful way, we have implemented our proposal in a Digital Twin scenario of a Smart Healthcare system. The operation of the suggested scenario as a Digital Twin scenario offers a more sustainable and energy-efficient system and experimental findings ultimately demonstrate that the proposed system is more reliable and secure. Experimental results show the impact of our proposed model depicts the efficiency of the usage of a Cloud Management System operated over a Digital Twin scenario, using real-time large-scale data produced from the connected IoT system. Through these scenarios, we can observe that our proposal remains the best choice regardless of the time difference or energy load.

A Duo Autoencoder-SVM based Approach for Secure Performance Monitoring of Industrial Conveyor Belt System

Process industries are fascinated by cyber-physical systems because of the potential to integrate physical systems and the cyber realm, resulting in efficient remote monitoring and control. The conveyor belt system has many critical parameters that require continuous attention, necessitating cyber-physical remote monitoring. Due to cloud-based monitoring of parameters, the system is vulnerable to cyber threats. The proposed technique combines a sparse autoencoder and support vector machine (SVM) to detect false data injection attacks (FDIAs) in the presence of sensor bias fault. The sparse autoencoder extracts sparse features and learns anomaly-free dynamics from the input sensor readings. Then, the trained SVM distinguishes attacks and fault by analysing reconstruction residuals of each measurement reading. The residuals also give an idea about the magnitude of abnormality. The proposed method's efficacy is evaluated in terms of accuracy, precision and false-alarm rate with the help of fault and FDIAs models.

Cloud-Based Monitoring System for Foam Content at the Wellhead of Foam Drainage Gas Production

To achieve automatic online monitoring of foam content in wellhead pipelines during the foam drainage gas production of shale gas, a cloud-based monitoring system for the foam content at the wellhead of foam drainage gas production is proposed. The system mainly comprises a terminal foam content detection sensor and a cloud platform, which relies on a wireless network for data communication. In this system, the foam content detection sensor uses the absorption of infrared light by the foam in the pipeline as a basic detection principle. Furthermore, the sensor uses signal-processing circuits to convert infrared light of different intensities into model voltage signals of different sizes and uses LoRa wireless networks to transfer the measurement data to a server for storage in a time-series database while displaying real-time foam content data on a web page. The monitoring system was tested in a field, and the relationship model between the foam content and the detection voltage signal was fitted based on the test data. Based on this model, and then tested and compared with the manual sampling results, the results of the 40 data samples tested showed that the monitoring system was effective and met the field working conditions. The foam content calculated by the fitted model compared with the manually measured foam content showed an error range of 0.11%–15.37%. The test results show that the system has functional requirements to monitor foam content in the field, contributing to the automation, networking, and cloud-based monitoring of the foam content at the wellhead.

Dump Yard Fire Extinguishing System Using IoT

Nagpur municipal corporation (NMC) is the best waste disposal company. Nagpur municipal corporation (NMC) collects everything from household waste and stores all the garbage in different landfills. But these landfills pose a major problem for storing this waste safely as it catches fire very quickly. These toxic gases such as dioxins, furans, mercury and polychlorinated biphenyls are then released into the atmosphere and pollute the air with an unpleasant smell. The design we propose is based on a fire alarm and a landfill fire extinguisher. When garbage is gathered for the dump, our project flashes a warning. Smoke alarm, temperature sensor, and Wi-Fi module powered by batteries Our sensor checks for smoke and temperature; if either is present, an alarm will be shown on the signal from the Wi-Fi module fi that is linked to a junkyard. An IoT-based fire extinguishing system is proposed for dump yards, providing an efficient and unique approach to prevent and extinguish fire outbreaks using sensors, actuators, and a cloud-based monitoring system. This research presents an innovative IoT-based fire extinguishing system for dump yards that incorporates sensor nodes to detect fire, actuator nodes to discharge extinguishing agents, and a cloud-based monitoring system for remote monitoring and management. The proposed system provides an efficient and effective approach to prevent and control fire outbreaks in dump yards, ensuring safety and environmental protection. This paper presents a novel IoT-based fire extinguishing system for dump yards, which utilizes sensor nodes to detect fire outbreaks and actuator nodes to deploy extinguishing agents. The system also includes a cloud-based monitoring and control system that enables remote monitoring and management of the system. The proposed system aims to provide an efficient and reliable approach to prevent and extinguish fire outbreaks in dump yards.

Distributed fibre optic inclinometer with cloud-based monitoring system

Geotechnical instrumentation and monitoring are important aspect of controlling subsurface ground movement, particularly in deep excavations and forewarning of geological disasters such as landslides, land subsidence, ground fissure, and surface collapse. A borehole inclinometer is an example of the established method to monitor such ground movements. For real-time data, a series of in-placed inclinometer probes are installed inside the borehole casing to provide the lateral displacement response over time. The limitation of the conventional system is the high cost and the practicality of inserting a high number of sensing probes into the casing for better measurement accuracy. Recent advancement in Distributed Optical Fibre Strain Sensing (DOFS) enables the incorporation of DOFS as a fibre optic (FO) inclinometer with lateral displacements to be plotted at centimetres spacing and is unaffected by the gauge lengths between the probes. This study enhances the FO inclinometer by developing a web-based application (SmartGeopipe©) to automate data acquisition which also includes complex data processing for the final pipe deflection profile visualisation. The FO inclinometer comprised of optical cables attached externally onto a remodified inclinometer casing and connected to a Brillouin Optical Time Domain Analysis (BOTDA) strain analyser. A standard installation procedure was established to safely secure the sensors during the casing installation process inside the borehole. SmartGeopipe© software is written using a predominantly monolithic Python / Cython backend and a multi-page frontend built with HTML, CSS, and JavaScript ES5 languages. All the computation work and data storing are done in a cloud server and SQL database, respectively. A case study of a station box excavation as part of the Mass Rapid Transit (MRT) tunnelling project was chosen to validate the performance of the FO inclinometer and the web application. Data from the FO inclinometer corroborated well with the manual measurements of the inclinometer probe. This ability supports the realization of an early warning system as an engineering control for hazard management in civil engineering infrastructure projects.

DNS Tunnelling, Exfiltration and Detection over Cloud Environments

The domain name system (DNS) protocol is fundamental to the operation of the internet, however, in recent years various methodologies have been developed that enable DNS attacks on organisations. In the last few years, the increased use of cloud services by organisations has created further security challenges as cyber criminals use numerous methodologies to exploit cloud services, configurations and the DNS protocol. In this paper, two different DNS tunnelling methods, Iodine and DNScat, have been conducted in the cloud environment (Google and AWS) and positive results of exfiltration have been achieved under different firewall configurations. Detection of malicious use of DNS protocol can be a challenge for organisations with limited cybersecurity support and expertise. In this study, various DNS tunnelling detection techniques were utilised in a cloud environment to create an effective monitoring system with a reliable detection rate, low implementation cost, and ease of use for organisations with limited detection capabilities. The Elastic stack (an open-source framework) was used to configure a DNS monitoring system and to analyse the collected DNS logs. Furthermore, payload and traffic analysis techniques were implemented to identify different tunnelling methods. This cloud-based monitoring system offers various detection techniques that can be used for monitoring DNS activities of any network especially accessible to small organisations. Moreover, the Elastic stack is open-source and it has no limitation with regards to the data that can be uploaded daily.

Development Of A Cloud-Based Condition Monitoring Scheme For Distribution Transformer Protection

Distribution transformers are a necessity to ensure a reliable power supply to consumers and their inability to function properly or even breakdown should be avoided due to the high cost of replacing them. Distribution transformers are large in numbers and randomly distributed in cities and there is a need to accurately monitor their daily/hourly performance. To achieve this, real-time monitoring of the transformer’s health status is proposed rather than the use of the traditional approach involving physical inspection and testing which is slow, tedious and time-consuming. This paper presents a cloud-based monitoring scheme applied to a prototype distribution transformer. A 10kVA, 0.415 kV prototype distribution transformer has been acquired and connected to three residences for data acquisition. A data acquisition system has been developed to monitor and record the parameters of the prototype transformer for 14 days. The parameters, monitored in real-time include load current, phase voltage, transformer oil level, ambient temperature and oil temperature. The acquired real-time data of the transformer is validated with the standard measuring instrument. An algorithm was developed to transmit and log the data to ThinkSpeak cloud server via node MCU (ESP 8266). Results obtained in this study, which can be visualized via the graphical user interface of ThinkSpeak, indicate that the proposed scheme can acquire vital data from the distribution transformers and transmit the information to the monitoring centre.

Towards a Conceptual Framework of Using Technology to Support Smart Construction: The Case of Modular Integrated Construction (MiC)

Construction is a major source of carbon emissions. Moreover, it faces various other sustainability challenges, such as construction waste, construction noise, vehicular traffic near construction sites, dust and other air and water pollutants, and safety and well-being of construction workers. Poorly designed and constructed buildings will continue to affect the well-being of their occupants and overall energy efficiency throughout the building lifecycle. Hence, accelerating the transformation of the construction industry towards smart construction or Construction 4.0 is an important topic. The ways that technology can help to achieve smart construction, especially with the adoption of construction methods with increasing construction modularity, should be further explored. Focusing on modular integrated construction (MiC), this paper examines the following questions: (1) How has technology been applied to support MiC development and smart construction in Hong Kong? (2) What are the lessons learned? A case study approach of a building information model (BIM)-enabled multifunctional blockchain-based digital platform is adopted to allow us to systematically consider (1) the main objectives and scope, (2) the stakeholders involved, (3) the key outcomes and processes, (4) the applications of blockchain technology, and (5) the integration with other digital software and management platforms in practice. Drawing upon the experience, we propose a generic four-stage approach in understanding and facilitating the adoption of relevant technology towards smart construction. At Stage One, the technologies of BIM, RFID, and blockchain are applied to support the core elements of MiC production: just-in-time transportation and on-site installation. At Stage Two, the digital platform is extended to serve as an interface for third parties, notably government; monitoring, authentication, and certifications for information sharing; visualization; and real-time monitoring and updating of MiC projects. At Stage Three, the system focuses on people in the construction process, aiming to enhance the safety and well-being of workers and drivers throughout the construction process. Different Internet-of-Thing devices and sensors, construction robotics, closed-circuit television, dashboards, and cloud-based monitoring are deployed. At Stage Four, the full construction lifecycle is the focus, whereby a centralized smart command theatre is set up with multiple sources of data in a city information model.

MACHINE LEARNING AND IOT-BASED LI-ION BATTERY CLOUD MONITORING SYSTEM FOR 5G BASE STATIONS

With the accelerated construction of 5G and IoT, more and more 5G base stations are erected. However, with the increase of 5G base stations, the power management of 5G base stations becomes progressively a bottleneck. In this paper, we solve the problem of 5G base station power management by designing a 5G base station lithium battery cloud monitoring system. In this paper, first, the lithium battery acquisition hardware is designed. Second, a new communication protocol is established based on Modbus. Third, the windows desktop upper computer software and the cloud-based monitoring system are designed. Finally, this paper designs the improved ResLSTM algorithm which is fused with ResNet algorithm based on Stacked LSTM. The algorithm designed in this paper is tested in comparison with SVM and LSTM. The performance of the algorithm designed in this paper is better than SVM and LSTM. Furthermore, the communication test, as well as the training and testing of the ResLSTM algorithm are outstanding. The 5G base station lithium-ion battery cloud monitoring system designed in this paper can meet the requirements. It has great significance for engineering promotion. More importantly, the ResLSTM algorithm designed in this paper can better guide the method of lithium-ion battery SOC estimation.