Real-time Data Collection Research Articles

Best Practices and Recommendations for Research Using Virtual Real-Time Data Collection: Protocol for Virtual Data Collection Studies.

The COVID-19 pandemic and the subsequent need for social distancing required the immediate pivoting of research modalities. Research that had previously been conducted in person had to pivot to remote data collection. Researchers had to develop data collection protocols that could be conducted remotely with limited or no evidence to guide the process. Therefore, the use of web-based platforms to conduct real-time research visits surged despite the lack of evidence backing these novel approaches. This paper aims to review the remote or virtual research protocols that have been used in the past 10 years, gather existing best practices, and propose recommendations for continuing to use virtual real-time methods when appropriate. Articles (n=22) published from 2013 to June 2023 were reviewed and analyzed to understand how researchers conducted virtual research that implemented real-time protocols. "Real-time" was defined as data collection with a participant through a live medium where a participant and research staff could talk to each other back and forth in the moment. We excluded studies for the following reasons: (1) studies that collected participant or patient measures for the sole purpose of engaging in a clinical encounter; (2) studies that solely conducted qualitative interview data collection; (3) studies that conducted virtual data collection such as surveys or self-report measures that had no interaction with research staff; (4) studies that described research interventions but did not involve the collection of data through a web-based platform; (5) studies that were reviews or not original research; (6) studies that described research protocols and did not include actual data collection; and (7) studies that did not collect data in real time, focused on telehealth or telemedicine, and were exclusively intended for medical and not research purposes. Findings from studies conducted both before and during the COVID-19 pandemic suggest that many types of data can be collected virtually in real time. Results and best practice recommendations from the current protocol review will be used in the design and implementation of a substudy to provide more evidence for virtual real-time data collection over the next year. Our findings suggest that virtual real-time visits are doable across a range of participant populations and can answer a range of research questions. Recommended best practices for virtual real-time data collection include (1) providing adequate equipment for real-time data collection, (2) creating protocols and materials for research staff to facilitate or guide participants through data collection, (3) piloting data collection, (4) iteratively accepting feedback, and (5) providing instructions in multiple forms. The implementation of these best practices and recommendations for future research are further discussed in the paper. DERR1-10.2196/53790.

The value of novel technologies in context to performance measurement and management: A systematic review and future research directions

The purpose of this paper is to systematically review research related to the value of new technologies in the context of performance measurement and management. The review synthesizes findings from previous studies to provide a comprehensive understanding of the current state-of-the-art of various novel technologies and how they assist performance measurement and management across various industries. Scopus was selected as the main electronic database for this study. As a principal result, we found that research related to the value of novel technologies in the context of performance measurement and management is formed around six research streams: automation techniques, predictive analytics, real-time data collection, visualization, collaboration tools, and blockchain technology. As a conclusion, the research presents that novel technologies can be used to track and monitor performance, automate processes, and streamline operations. The findings of this review will bring more coherent value to the current state of the literature and contribute to a diverse body of knowledge.

Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility

Shared mobility is one of the smart city applications in which traditional individually owned vehicles are transformed into shared and distributed ownership. Ensuring the safety of both drivers and riders is a fundamental requirement in shared mobility. This work aims to design and implement an adequate framework for shared mobility within the context of a smart city. The characteristics of shared mobility are identified, leading to the proposal of an effective solution for real-time data collection, tracking, and automated decisions focusing on safety. Driver and rider safety is considered by identifying dangerous driving behaviors and the prompt response to accidents. Furthermore, a trip log is recorded to identify the reasons behind the accident. A prototype implementation is presented to validate the proposed framework for a delivery service using motorbikes. The results demonstrate the scalability of the proposed design and the integration of the overall system to enhance the rider’s safety using machine learning techniques. The machine learning approach identifies dangerous driving behaviors with an accuracy of 91.59% using the decision tree approach when compared against the support vector machine and K-nearest neighbor approaches.

Generally Genius: A Generals.io Agent Development and Data Collection Framework

We present an agent development and data collection framework for Generals.io (GIO)--a real-time strategy game with imperfect information in which players attempt to gain control of opponents' starting positions within a 2D grid world. The framework provides event-based communication amongst several modules implemented as microservices, enabling real-time data collection from GIO's streaming data. Its modular design facilitates rapid bot development and testing, while the emphasis on data collection makes it easy to analyze agent performance. We use this framework in a case study of a top-performing GIO agent called Flobot. Our analysis demonstrates that Flobot's performance varies based on its starting position. Based on the analysis performed with our framework, we propose a modification to Flobot's pathfinding algorithm. Statistical tests show that the new algorithm results in a significant reduction in performance variance.

Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm

In response to the challenge of collecting behavioral data on Amur tigers living in forests, a remote real-time data collection approach is proposed. In this article, a novel attention mechanism named CBAM-E is introduced, and CBAM-E as well as the CIoU loss function are incorporated into the YOLOX object detection algorithm, resulting in a new YOLOX model. The new model demonstrates significant performance improvements over the original model, with the mAP0.5 detection accuracy metric rising from 97.32 to 98.18%, indicating a boost of 0.86%, and the mAP0.75 metric increasing from 75.10 to 78.70%, marking an enhancement of 3.60%. The enhanced algorithm is subsequently applied to remote terminal information collection, offering a reference for detection algorithms in the study of wild behaviors of Amur tigers in forests, biodiversity conservation, and the collection of related field data about Amur tigers in the wild.

Web Server Based Data Monitoring System of PV Panels Using S7-1200 PLC

This This paper presents the design and implementation of a solar panel data monitoring system using a Web Server as a real-time operating system. The system is built via the Siemens S7-1200 Programmable Logic Controller (PLC) and programmed using TIA (Totally Integrated Automation) Portal V17 software. This approach enables real-time data collection and visualization of essential parameters such as voltage, current, power output, temperature, irradiance, and environmental conditions. The S7-1200 PLC serves as the central control unit, interfacing with the PV panels and gathering data. The acquired data is then transmitted to a web server, which hosts a user-friendly interface accessible through web browsers. Users can remotely monitor the performance of PV panels, track historical data trends, and receive alerts in case of anomalies. This web server-based system offers a convenient and efficient solution for effective PV panel management, enabling users to optimize energy production, detect issues promptly, and make informed decisions for maintenance and operational improvements.

Technology Focus: Offshore Drilling and Completion (October 2023)

Optimizing and continually increasing efficiencies at every stage of the drilling and completion process are central themes in the papers showcased in this feature. Innovating our way through difficult times is what the oil and gas industry excels at, especially during challenging periods. During the COVID-19 pandemic, the offshore energy industry faced immense struggles with high upfront capital investment and long lead times in uncertain circumstances with extremely low oil prices. The challenges brought about by the pandemic served as a backdrop to many of these innovations. Companies were compelled to discover creative solutions to maintain efficiency, reduce costs, and ensure safety during an era of unprecedented disruption. Innovation in advanced analytics and machine-learning algorithms is used to optimize drilling-center locations, leading to cost reduction. This development became even more critical during the pandemic as companies aimed to maximize efficiency and minimize expenses. The execution of large-scale projects such as the Johan Sverdrup development highlights innovation in project management, enabling timely and cost-effective operations in a challenging environment. The emphasis on digital solutions and fiber-optic technology in Guyana’s offshore operations illustrates the industry’s adaptation to the pandemic by enabling remote operations and real-time data collection while maintaining efficiency in a new discovery. Innovations in riserless light well intervention systems are particularly relevant during the pandemic. These systems improved efficiency and safety for offshore interventions, enabling the industry to operate with minimal personnel onboard. The focus on well-planning strategies to overcome challenges in deep depleted reservoirs includes approaches that optimize drilling in complex conditions, which is crucial when efficiency is paramount. Retrofitting autonomous inflow control devices proved an effective approach in implementing state-of-the-art technology to improve reservoir sweep and productivity. Moving forward, this innovative spirit in our industry as seen in these papers is a testament to our resilience during challenging times. In the coming years, this ability to sustainably increase recovery efficiencies to ensure energy security is crucial to shaping the future of energy production. Recommended additional reading at OnePetro: www.onepetro.org. SPE 212499 4D Radar Imaging of Wellbore Geometry While Drilling by Lars Øy, Well ID, et al. OTC 30979 Guyana Operations and First Oil by Mike F. Ryan, ExxonMobil, et al. SPE 212915 Enhancing Riserless Light Well Intervention Operability From Operator Perspective by Michael Long Ge, BP, et al. SPE 214057 Challenges of Drilling Deep Wells in a Complex Overburden With Severe Depletion and Experiences From Caspian Sea by Anwar Juma Al Saadi, Dragon Oil, et al.

Effective Risk Assessment and Management with IoT in Supply Chains

Supply chain risk management is a critical area in today’s globalized economy, and with the development of Internet of Things (IoT) technology, revolutionary changes are taking place in risk assessment and management methods. This paper aims to explore effective risk assessment and management with IoT in supply chains in China, emphasizing the crucial role of IoT technology in real-time data collection, predictive analytics, and traceability. We delve into the limitations of traditional risk assessment methods and how IoT enhances supply chain visibility and transparency. Furthermore, we speculate on the future applications of IoT technology in risk management, including autonomous vehicles, smart packaging, and supply chain simulations. This paper also focuses on the convergence of IoT with emerging technologies and the role of government and industry in shaping IoT adoption in supply chain risk management. Through this paper, we encourage organizations to harness IoT technology to enhance supply chain resilience and mitigate potential risks.

Internet of Things and Blockchain-Based Smart Contracts: Enabling Continuous Risk Monitoring and Assessment in Peer-to-Peer Lending

ABSTRACT Peer-to-peer (P2P) lending enables individuals and small companies to finance and invest without the intermediation of financial institutions. However, this business model is also associated with high delinquency risk and a lack of risk monitoring and control capabilities. This paper explores the potential of the Internet of Things (IoT), blockchain, smart contract technologies, and the Continuous Risk Monitoring and Assessment (CRMA) framework to re-engineer risk monitoring and control for P2P lending. We conducted a case study of a large Chinese P2P lending company to identify problems in its current risk monitoring and control processes and to design an IoT-smart contract CRMA system to continuously monitor and respond to delinquency risk via real-time data collection, automatic loan settlement, and in-time risk disclosure. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M40; M41; M49.

Efficient offloading in disaster-affected areas using unmanned aerial vehicle-assisted mobile edge computing: A gravitational search algorithm-based approach

The collection and processing of real-time data from a disaster-affected area is challenging. Unmanned aerial vehicles (UAVs) can efficiently gather the data and then transfer it to the edge servers (ESs) to timely initiate the rescue process. Consideration of energy and delay in a UAV-assisted edge network is very important, as both the UAVs and the smart mobile devices (SMDs) in the network have energy constraints and low processing capacity. Offloading is a promising technique to preserve the precious energy of the SMDs. In this research, gravitational search algorithm (GSA)-based offloading is presented for UAV-assisted mobile edge computing (MEC)-enabled disaster-affected areas. The problem is first mathematically formulated and shown to be computationally hard. Efficient encoding of agents (solution vectors) is given for the offloading problem. Fitness function is designed by considering the energy, delay, and load balancing of the ESs. The proposed GSA is executed by considering multiple disaster scenarios, and its performance is compared with other evolutionary algorithms (EAs) like the genetic algorithm (GA), particle swarm optimization (PSO), and fireworks algorithm (FWA). It has been observed that the GSA outperforms the other EAs in almost all the considered experiment scenarios. GSA claims a 30%–40% improvement for delay, 3%–5% for energy consumption, and more than 40% for load balancing. Statistical and convergence analyses are also conducted. The convergence of the GSA is found to be faster than that of the other EAs.

Efficient plastic categorization for recycling and real-time annotated data collection with TensorFlow object detection model

Plastic waste management is the major global issue, and recycling has become a necessary solution to mitigate the impact of plastic waste on the environment. Recycling plastic can significantly reduce pollution by diverting plastic waste from landfills, where it can take hundreds of years to decompose and release harmful chemicals and greenhouse gases. Several systems developed for segregating the municipal solid waste, only few focused on categorizing plastic waste. To address these issues, a plastic waste detection system using TensorFlow pre-trained object detection and MobileNet V2 has been proposed. This work is mainly focused on plastic waste such as PET, HDPE, PVC, LDPE, PP and PS. The proposed system can detect plastic waste category in real time and store the detection information as annotation files in various formats such as json, Pascal voc, and txt. The model saves the detection matrix only when the confidence of prediction is greater than threshold value. This data can be used for fine tuning the model as well as training the new model. To validate the dataset generated by the object detection model, a sample of 54 images annotated by the model is used to train the new model and to ensure that the model is learning from dataset. Furthermore, the proposed system promotes recycling, contributing to the reduction of environmental pollution.

Development a Software Defined Network (SDN) with Internet of Things (IoT) Security for Medical Issues

Because providing billions of objects with network connectivity, the Internet of Things (IoT) enables the collection and transfer of real-time data for intelligent applications. Therefore, IoT enables remote access and control of connected devices when there is a sufficient network infrastructure. Additionally, the introduction of software-defined networking (SDN) presents capabilities that allow internet providers and users to control and connect network equipment wirelessly, even as enabling a global perspective on the network, that has previously become a soaring interest area due to its extensive use for various applications and systems, including wireless sensor nodes, medical equipment, delicate home sensor systems, and some other connected IoT devices. In order to create worldwide connectivity between the Internet of Things (IoT) and based on the SDN architecture in the medical contexts, this paper's contribution is to outline some relevant directions. Additionally, we provide a model based on software defined network principles that depicts interactions between a group of people each of whom have a Nano network within their bodies and the medical services via the local network of a medical institution. For everybody electrical engineers to data engineers the requirement to integrate everything in a global setting is a significant problem. As a result, the cloud is useful for handling the instantaneous sharing of information. In terms of health care, the effort is also stated in terms of IoT architecture and services. IoT's current prospects for the healthcare sector are quite promising. Due to its capacity for sensing and measuring, it is also highly well-liked. From the smallest sensor to the massive amounts of data gathered, this revolution is completely changing how we view healthcare.

Automated vehicle wheelbase measurement using computer vision and view geometry

For different transportation agencies that monitor vehicle overloads, develop policies to mitigate the impact of vehicles on infrastructure, and provide the necessary data for road maintenance, they all rely on precise, detailed and real-time vehicle data. Currently, real-time collection of vehicle data (type, axle load, geometry, etc) is typically performed through weigh-in-motion (WIM) stations. In particular, the bridge WIM (BWIM) technology, which uses instrumented bridges as weighing platforms, has proven to be the most widely used inspection method. For most of the BWIM algorithms, the position of the vehicle’s axle (i.e. vehicle wheelbase) needs to be measured before calculating the axle load, and the identification of the axle load is very sensitive to the accuracy of the vehicle wheelbase. In addition, the vehicle’s wheelbase is also important data when counting stochastic traffic flow and classifying passing vehicles. When performing these statistics, the amount of data is often very large, and the statistics can take years or even decades to complete. Traditional manual inspection and recording approaches are clearly not up to the task. Therefore, to achieve automatic measurement of the on-road vehicles’ wheelbase, a framework based on computer vision and view geometry is developed. First, images of on-road vehicles are captured. From the images, the vehicle and wheel regions can be accurately detected based on the You Only Look Once version 5 (YOLOv5) architecture. Then, the residual unified network model is improved and an accurate semantic segmentation of the wheel within the bounding box is performed. Finally, a view geometry-based algorithm is developed for identifying vehicle wheelbase. The accuracy of the proposed method is verified by comparing the identified results with the true wheelbases of both two-axle vehicles and multi-axis vehicles. To further validate the effectiveness and robustness of the framework, the effects of important factors, such as camera position, vehicle angle, and camera resolution, are investigated through parametric studies. To illustrate its superiority, the developed vehicle wheelbase measurement algorithm is compared with two other advanced vehicle geometry parameter identification algorithms and the results show that the developed algorithm outperforms the other two methods in terms of the degree of automation and accuracy.

Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology

Sustainable agriculture is a pivotal driver of a nation’s economic growth, especially considering the challenge of providing food for the world’s expanding population. Agriculture remains a cornerstone of many nations’ economies, so the need for intelligent, sustainable farming practices has never been greater. Agricultural industries worldwide require sophisticated systems that empower farmers to manage their crops efficiently, reduce water wastage, and optimize yield quality. Yearly, substantial crop losses occur due to unpredictable environmental changes, with improper irrigation practices being a leading cause. In this paper, we introduce an innovative irrigation time control system for smart farming. This system leverages fuzzy logic to regulate the timing of irrigation in cotton crop fields, effectively curbing water wastage while ensuring that crops receive neither too little nor too much water. Additionally, our system addresses a common agricultural challenge: whitefly infestations. Users can adjust climatic parameters, such as temperature and humidity, through our system, which minimizes both whitefly populations and water consumption. We have developed a portable measurement technology that includes air humidity sensors, temperature sensors, and rain sensors. These sensors interface with an Arduino platform, allowing real-time climate data collection. This collected climate data is then sent to the fuzzy logic control system, which dynamically adjusts irrigation timing in response to changing environmental conditions. Our system incorporates an algorithm that generates highly effective (IF-THEN) fuzzy logic rules, significantly improving irrigation efficiency by reducing overall irrigation duration. By automating the irrigation process and precisely delivering the right amount of water, our system eliminates the need for human intervention, rendering the agricultural system more dependable in achieving successful crop yields. Water supply commences when the environmental conditions reach specific thresholds and halts when the requisite climate conditions are met, maintaining an optimal environment for crop growth.

Development of a Smart Air Quality Monitoring System Using Wireless Sensors

With the fast-growing urbanization and increased industrialization around cities in Nigeria, the quality of the air we breathe has become a paramount concern. There is a need for an accurate, real-time air quality monitoring system to monitor and evaluate the air quality we inhale. This article presents the development of a Smart Air Quality Monitoring System (SAQMS) utilizing wireless sensor technology. The SAQMS was designed to provide a comprehensive and intelligent solution for monitoring air quality index, including but not limited to particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon monoxide (CO), temperature and humidity. Leveraging wireless sensor nodes and advanced IOT, the system offers real-time data collection and analysis, enabling immediate response to environmental changes. Key features of the SAQMS include a network of wireless sensors (MQ 135 and DHT11) strategically deployed across targeted areas, data transmission through secure communication protocols, cloud-based data storage and processing, and a user-friendly interface for data visualization and interpretation. As we confront the challenges of air pollution and its impact on human well-being, the development of the SAQMS presents a significant step towards informed decision-making and a more sustainable future as the experiment shows our developed system can detect air quality in parts per million (ppm), with other environmental factors such as temperature and relative humidity.

Catalyzing Drug Safety: Harnessing IoT and Block chain Technology and its Synergy in Pharmacovigilance

The convergence of Internet of Things (IoT) and blockchain technologies is catalysing a paradigm shift in the field of pharmacovigilance. This article provides an overview of the application of IoT and blockchain in pharmacovigilance, highlighting their transformative potential in enhancing patient safety and drug efficacy monitoring. IoT devices enable real-time data collection, enabling prompt identification of adverse events, while blockchain ensures data integrity and security. Together, they create a robust ecosystem for pharmacovigilance, allowing for transparent reporting, streamlined adverse event management, and proactive measures to safeguard public health. This paper explores key use cases, benefits, challenges, and prospects of this innovative approach, emphasizing its pivotal role in shaping the future of drug safety and regulatory compliance in the pharmaceutical industry.

Hand for the World

The Hand For The World-Disaster Relief Management System (DRMS) is a comprehensive software project designed to streamline and optimize disaster response and relief efforts. Disasters, such as natural calamities and emergencies, can cause significant damage and disrupt the lives of individuals and communities. The DRMS aims to provide a centralized platform for effective coordination, communication, and resource management during these critical situations. The system offers several key features to facilitate efficient disaster relief operations. Firstly, it enables real-time data collection and analysis by integrating various sources of information, including satellite imagery, weather data, and social media feeds. This data is processed and presented in a user-friendly manner, allowing decision-makers to quickly assess the situation and allocate resources accordingly. Secondly, the DRMS provides a communication channel for different stakeholders involved in disaster management, including government agencies, emergency responders, and volunteers. The platform facilitates instant messaging, voice calls, and video conferencing, enabling seamless collaboration and information exchange. It also supports the dissemination of public alerts and warnings to affected areas, ensuring timely and accurate communication with the affected population. Furthermore, the DRMS incorporates a comprehensive resource management module that enables efficient tracking and allocation of resources, such as food, water, medical supplies, and personnel. This module maintains an inventory of available resources, tracks their movement and utilization, and generates reports to ensure transparency and accountability in resource management

Mini air conditioning design with two levels of thermoelectric cooler module: cooling heat pipe

As the tropical country, the use of air conditioning (AC) in Indonesia is still high. This will affect energy consumption and global warming issue. This study aims to make solution of the AC issue with a design of the mini-AC using thermoelectrical cooler (TEC) technology and determine the output temperature result of the design, then to find out the coefficient of performance (COP) of the TEC cooler. The mini-AC design or method uses two TEC with additional heat sink, heat pipe, and fan to help release temperature to environment. The temperature measuring uses an 11-point thermocouple with real-time data collection using BTM-4208sd logger. The results of the study concluded that the mini-AC could work well even though one of the TECs was below its best performance. The use of heat pipes can help reduce heat quickly from TEC heat side. The best COP of TEC with ideal heat reduction is 0.69 from the upper position (72 W), and the highest at lower position (48 W) with 2.19.

Models for forecasting the traffic flow within the city of Ljubljana

Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data.

Development of The Navigator: A Lagrangian sensing system to characterize surface freshwater ecosystems

Most freshwater aquatic studies rely on Eulerian monitoring, i.e., water quality and quantity are monitored using grab samples or semi-continuous sensors deployed at fixed cross-sections. While Eulerian monitoring is practical, it provides a limited understanding of spatial and temporal heterogeneity. We designed and built The Navigator, a Lagrangian (i.e., along a flow path) monitoring system that offers cost-effective solutions for in-situ, real-time data collection in surface freshwater ecosystems. The Navigator features a suite of technologies, including an autonomous surface vehicle with GPS and LTE connectivity, water quality sensors, a depth sonar, a camera, and a webpage dashboard to visualize real-time data. With these technologies, The Navigator provides insight into where, how, and why water quality and quantity change over time and space as it moves with the current or follows user-specified pathways. We tested The Navigator monitoring water quality parameters at high spatial-temporal resolution in multiple surface water bodies in New Mexico (USA) to: (1) identify water quality changes associated with land use changes along a 7th-order reach in the Rio Grande, (2) identify the fate of wildfire disturbances ∼175 km downstream of a burned watershed affected by the largest wildfire ever recorded in the state, (3) monitor the water quality of a recreational fishing pond in the City of Albuquerque. Our three successful tests confirm that The Navigator is an affordable (USD 5,101 in 2023) monitoring system that can be used to address questions involving mass and energy balances in surface waters.