Real-time Monitoring Platform Research Articles

Information processing in Internet of Things using big data analytics

With innovation in persistent technologies, such as wearable sensor gadgets, sensor devices, and wireless ad-hoc communication networks connect everyday life things to the Internet, normally referred to as Internet of Things (IoT). IoT is observed as an active entity for design and development of smart and context awareness services and applications in the area of business, science and engineering discipline. These applications and services could vigorously respond to the surroundings transformation and users’ preference. Developing a scalable system for data analysis, processing and mining of enormous real world based datasets has turned into one of the demanding problems that faces both system research scholars and data management research scholars. Employing big data analytics with IoT technologies is one of the ways for handling the timely analyzing information (i.e., data, events) streams. In this paper, we propose an integrated approach that coalesce IoT systems with big data tools into a holistic platform for real-time and continuous data monitoring and processing. We propose Fog assisted IoT based Smart and real time healthcare information processing (SRHIP) system in which large amounts of data generated by IoT sensor devices are offloaded at Fog cloud form data analytics and processing with minimum delay. The processed data is then transferred to a centralized cloud system for further analysis and storage. In this work, we introduce a Fog-assisted model with big data environment for data analytic of real time data with remote monitoring and discuss our plan for evaluating its efficacy in terms of several performance metrics such as transmission cost, storage cost, accuracy, specificity, sensitivity and F-measure. The proposed SRHIP system needs less transmission cost of 40.10% in comparison to SPPDA, 100% fewer bytes are compromised in comparison to GCEDA. Our proposed system data size reduction of 60% reduction due to proposed compression scheme in comparison to other benchmark strategies that offer 40% of reduction.

3D Printed Bioresponsive Devices with Selective Permeability Inspired by Eggshell Membrane for Effective Biochemical Conversion

Eggshell membrane has selective permeability that enables gas or liquid molecules to pass through while effectively preventing migration of microbial species. Herein, inspired by the architecture of the eggshell membrane, we employ three-dimensional (3D) printing techniques to realize bioresponsive devices with excellent selective permeability for effective biochemical conversion. The fabricated devices show 3D conductive carbon nanofiber membranes in which precultured microbial cells are controllably deployed. The resulting outcome provides excellent selective permeability between chemical and biological species, which enables acquisition of target responses generated by biological species confined within the device upon input signals. In addition, electrically conductive carbon nanofiber networks provide a platform for real-time monitoring of metabolism of microbial cells in the device. The suggested platform represents an effort to broaden microbial applications by constructing biologically programmed devices for desired responses enabled by designated deployment of engineered cells in a securely confined manner within enclosed membranes using 3D printing methods.

Research on Cluster Monitoring and Prediction Platform based on Zabbix Technology

With the rapid development of Internet and computer technology, cluster technology has been widely applied to the explosive growth of network data. With the successful construction of the cluster and the continuous expansion of nodes, the reliability and stability of the cluster itself has become an important factor that cannot be ignored, so various monitoring systems emerge. Based on the above reasons, this paper proposes a real-time monitoring and prediction platform for enterprise users based on Zabbix. The monitoring and prediction platform monitors all kinds of indexes in the cluster in real time and predicts important performance indexes in real time. When monitoring or predicting the abnormal situation, the system can warn the users, which is conducive to the real-time understanding of the cluster operation. The short-term real-time prediction model of cluster monitoring index is introduced in detail in this paper. Finally, the real-time monitoring function of indicators is designed and analyzed based on the prediction model.

Colorimetric sensor array based on silver deposition of gold nanorods for discrimination of Chinese white spirits

The rapid and effective discrimination of Chinese white spirits is of great significance for quality control in the white spirit industry. Herein, we proposed a colorimetric sensor for white spirits based on silver deposition on gold nanorods (AuNRs), which were formed by reducing silver ions to Ag nanoshells on the surface of the AuNRs by different reductants containing hydroxyl or amino groups. The degree of inhibition of silver deposition was demonstrated to be strongly associated with the various components of white spirits, such as organic acids, aldehydes, ketones, and esters, which resulted in color differences and a consequent colorimetric response for different types of white spirits. The color differences could be recognized using principal component analysis (PCA), hierarchical cluster analysis (HCA), and linear discriminant analysis (LDA). The colorimetric sensor array was constructed by designing AuNRs with four different morphologies by utilizing different reducing agents to not only successfully distinguish various types of white spirits, including twelve types of strong-aroma-type spirits with similar flavor characteristics and different aroma-type spirits, but also to determine the concentration of the main ingredients affecting the quality of white spirits with a satisfactory linear range. Notably, the discriminating ability of the colorimetric sensor arrays was equal or superior than that of the untargeted metabolomic approach based on liquid chromatography-mass spectrometry. This sensing strategy, with facile preparation, fast response, excellent discrimination and high-throughput signal output, provides a promising application platform for real-time quality monitoring of white spirits and other alcoholic beverages.

An Electrochemophysiological Microarray for Real-Time Monitoring and Quantification of Multiple Ions in the Brain of a Freely Moving Rat.

Herein, we present an electrochemophysiological microarray (ECPM) for real-time mapping and simultaneous quantification of chemical signals for multiple ions in the deep brain of a freely moving rat, in which microelectrode arrays were developed for direct determination of multiple ions using open-circuit potentiometry. Specific recognition ionophores were synthesized and optimized for determination of K+ , Ca2+ , Na+ and pH. A reference electrode was also developed to avoid interferences in the brain. The microarrays were successfully applied in real-time monitoring and quantification of ions in a live brain. The extra current-free potentiometry allowed mapping and biosensing of chemical signals, together with recording of electrical signals in the whole brain without cross-talk, for the first time. Furthermore, the ECPM provided a platform for real-time monitoring of the dynamic changes of multiple ions in the deep brain of freely moving rat during a seizure.

Understanding Human Activity and Urban Mobility Patterns From Massive Cellphone Data: Platform Design and Applications

Large-scale cellphone data provide an emerging source for acquiring urban movements and patterns. Existing researches on cellphone data based travel behavior detection are mostly concentrated on algorithms exploration and evaluation. The framework and layers of the monitoring platform from data collection to application are not very clear, and existing platform are mostly concentrated on limited functions, some important travel characteristics and influencing factors have not been clearly explored. Therefore, this paper tries to propose a real-time urban mobility monitoring and traffic management platform using cellular data. First, the systems and essential modules of the platform including global system architecture, functional service system, core technology system, data system, and security & privacy protection system are proposed. Second, a field case study in Guiyang China, covering over 3 million people daily and a total 3 years 600TB cellphone data, is carried out to evaluate the methods. 6 visualization platforms 36 sub-functions including jobs-housing distribution, real-time passenger mobility, regional contacting, hotspot passenger attraction, traffic congestion and distribution etc. are constructed and analyzed. This paper aims to provide practical experience and reference for urban mobility monitoring and traffic management system construction using cellular data.

Label-Free SERS Strategy for In Situ Monitoring and Real-Time Imaging of Aβ Aggregation Process in Live Neurons and Brain Tissues.

The aggregation of Aβ has been reported to closely correlate with Alzheimer's disease (AD). However, clear monitoring of the entire aggregation process of Aβ from monomer to fibril has been scarcely reported until now. Herein, we developed a label-free ratiometric surface enhanced Raman spectroscopic (SERS) platform for real-time monitoring of the entire process of Aβ aggregation in neurons and brain tissues. Different gold nanoparticles, generated in situ with Aβ monomer and fibril as templates separately, were served as effective SERS substrates to achieve a high sensitivity with a limit of detection (LOD) down to 70 ± 4 pM and 3.0 ± 0.5 pM for Aβ40 monomer and fibrils, respectively. Besides, the introduction of ratiometric determination of Aβ monomer and fibril (I1244/I1268) realized real-time monitoring of the entire aggregation process of Aβ monomer with high accuracy and selectivity against other proteins and amino acids. The significant analytical performance of the developed platform, together with good biocompatibility, long-term stability, and remarkable spatial resolution, enabled the present SERS platform imaging and real-time monitoring and imaging of Aβ aggregation influenced by different metal ions (Cu2+, Zn2+, and Fe3+) in neurons and brain tissues at the single cell level. Our results suggested that Cu2+ and Zn2+ ion of low concentration (10 μM) promoted fibril formation, while Fe3+ and Zn2+ of high concentration (100 μM) showed inhibition of fibrosis.

Flexible and Reusable Ag Coated TiO2 Nanotube Arrays for Highly Sensitive SERS Detection of Formaldehyde.

Quantitative analysis of formaldehyde (HCHO, FA), especially at low levels, in various environmental media is of great importance for assessing related environmental and human health risks. A highly efficient and convenient FA detection method based on surface-enhanced Raman spectroscopy (SERS) technology has been developed. This SERS-based method employs a reusable and soft silver-coated TiO2 nanotube array (TNA) material, such as an SERS substrate, which can be used as both a sensing platform and a degradation platform. The Ag-coated TNA exhibits superior detection sensitivity with high reproducibility and stability compared with other SERS substrates. The detection of FA is achieved using the well-known redox reaction of FA with 4-amino-3-hydrazino-5-mercapto-1,2,4-triazole (AHMT) at room temperature. The limit of detection (LOD) for FA is 1.21 × 10−7 M. In addition, the stable catalytic performance of the array allows the degradation and cleaning of the AHMT-FA products adsorbed on the array surface under ultraviolet irradiation, making this material recyclable. This SERS platform displays a real-time monitoring platform that combines the detection and degradation of FA.

Dynamic calculation of ship exhaust emissions based on real-time AIS data

The activity-based methodology is becoming an increasing way to calculate exhaust emissions from ships in a port. Existing studies make great effort to build and analyze ship emission inventory in a variety of ports by applying this method to historical ship trajectory data. This kind of static emission inventory however, cannot meet the needs of real-time ship emission monitoring. This article proposes a method of dynamic calculation of ship exhaust emissions based on real-time ship trajectory data. Firstly, real-time ship AIS messages are partitioned into continuous data blocks and go through a series of pre-processing operations, including trajectory extraction, association and interpolation. Ship activity parameters are then determined by database querying and regression analysis based on ship attributes. Subsequently, an improved activity-based methodology is employed to estimate exhaust emissions from ships in a distributed way. Based on the grid model, regional ship exhaust emissions can be statistically and dynamically calculated by the spatial allocation of all ship emissions. In a case study, a real-time monitoring platform for ship exhaust emissions in Shenzhen port is developed to demonstrate the effectiveness of the proposed method.

The AquiFR hydrometeorological modelling platform as a tool for improving groundwater resource monitoring over France: evaluation over a 60-year period

Abstract. The new AquiFR hydrometeorological modelling platform was developed to provide short-to-long-term forecasts for groundwater resource management in France. This study aims to describe and assess this new tool over a long period of 60 years. This platform gathers in a single numerical tool several hydrogeological models covering much of the French metropolitan area. A total of 11 aquifer systems are simulated through spatially distributed models using either the MARTHE (Modélisation d'Aquifères avec un maillage Rectangulaire, Transport et HydrodynamiquE; Modelling Aquifers with Rectangular cells, Transport and Hydrodynamics) groundwater modelling software programme or the EauDyssée hydrogeological platform. A total of 23 karstic systems are simulated by a lumped reservoir approach using the EROS (Ensemble de Rivières Organisés en Sous-bassins; set of rivers organized in sub-basins) software programme. AquiFR computes the groundwater level, the groundwater–surface-water exchanges and the river flows. A simulation covering a 60-year period from 1958 to 2018 is achieved in order to evaluate the performance of this platform. The 8 km resolution SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) meteorological analysis provides the atmospheric variables needed by the SURFEX (SURFace EXternalisée) land surface model in order to compute surface runoff and groundwater recharge used by the hydrogeological models. The assessment is based on more than 600 piezometers and more than 300 gauging stations corresponding to simulated rivers and outlets of karstic systems. For the simulated piezometric heads, 42 % and 60 % of the absolute biases are lower than 2 and 4 m respectively. The standardized piezometric level index (SPLI) was computed to assess the ability of AquiFR to identify extreme events such as groundwater floods or droughts in the long-term simulation over a set of piezometers used for groundwater resource management. A total of 56 % of the Nash–Sutcliffe efficiency (NSE; Ef) coefficient calculations between the observed and simulated SPLI time series are greater than 0.5. The quality of the results makes it possible to consider using the platform for real-time monitoring and seasonal forecasts of groundwater resources as well as for climate change impact assessments.

IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit.

New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification. IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (PbtO2), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application. Sustained increases in ICP and concordant decreases in PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r20.633-0.781; p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%. This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers. This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.

MiCLAD as a platform for real-time monitoring and machine learning in laser metal deposition

The MiCLAD machine designed at the VUB, Belgium, allows for closed-loop controlled laser metal deposition including various in-situ optical based measurement systems. These integrated sensors collect information on deposition geometry and temperature during the building process. Hence, each cubic millimeter of material that is either added or removed is mapped to its digital twin with a millisecond temporal resolution in the machines database. This paper introduces the platform and its capabilities by focusing on the procedure of obtaining the necessary training data for the future application of machine learning algorithms, with the goal of controlling the geometry and temperature history during additive manufacturing.

Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning

Due to the global warming, which mechanically increases the risk of starting fires. The number of forest fires is increasing and will increase more and more. To better support the fire soldiers on the ground, we present in this work a system for early detection of forest fires. This system is more precise compared to traditional surveillance approaches such as lookout towers and satellite surveillance. The proposed system is based on collecting environmental wireless sensor network data from the forest and predicting the occurrence of a forest fire using artificial intelligence, more particularly Deep Learning (DL) models. The combination of such a system based on the concept of the Internet of Things (IoT) is made up of a Low Power Wide Area Network (LPWAN), fixed or mobile sensors and a good suitable model of deep learning. That several models derived from deep learning were evaluated and compared enabled us to show the feasibility of an autonomous and real-time environmental monitoring platform for dynamic risk factors of forest fires.

Real-Time Monitoring of Bacteria Clearance From Blood in a Murine Model.

Bloodstream infections, especially those that are antibiotic resistant, pose a significant challenge to health care leading to increased hospitalization time and patient mortality. There are different facets to this problem that make these diseases difficult to treat, such as the difficulty to detect bacteria in the blood and the poorly understood mechanism of bacterial invasion into and out of the circulatory system. However, little progress has been made in developing techniques to study bacteria dynamics in the bloodstream. Here, we present a new approach using an in vivo flow cytometry platform for real-time, noninvasive, label-free, and quantitative monitoring of the lifespan of green fluorescent protein-expressing Staphylococcus aureus and Pseudomonas aeruginosa in a murine model. We report a relatively fast average rate of clearance for S. aureus (k = 0.37 ± 0.09 min-1 , half-life ~1.9 min) and a slower rate for P. aeruginosa (k = 0.07 ± 0.02 min-1 , half-life ~9.6 min). We also observed what appears to be two stages of clearance for S. aureus, while P. aeruginosa appeared only to have a single stage of clearance. Our results demonstrate that an advanced research tool can be used for studying the dynamics of bacteria cells directly in the bloodstream, providing insight into the progression of infectious diseases in circulation. © 2019 International Society for Advancement of Cytometry.

Benzo[d]imidazo[2,1-b]thiazole-based fluorescent sensor for Zn2+ ion detection

In this study, we have synthesized and evaluated three benzo[d]imidazo[2,1-b]thiazole-based sensors (BIT-1, BIT-2 and BIT-3). Among them, BIT-3 namely 2-(benzo[d]imidazo[2,1-b]thiazol-2-yl)-5-methoxyphenol was introduced as a selective fluorescence chemosensor for Zn2+ ion detection. The presence of zinc ions enhanced fluorescence property of BIT-3 at 404 nm due to formation of a 1:1 complex between the chemosensor and the Zn2+ ion. Control experiments showed that the analogous ions do not have fluorescence enhancement effect and some of them even quench the fluorescence dramatically. The data obtained from UV–vis absorption analysis, fluorescence measurements and 1H NMR spectroscopy approved the interaction between BIT-3 and Zn2+ ion. The sensor probe BIT-3 exhibits a selective fluorescence enhancing property via a chelation-enhanced fluorescence (CHEF) upon addition of Zn2+. Thus, a selective fluorescent chemosensor BIT-3 establishes an important sensing platform for real-time monitoring of Zn2+ ion in aqueous environment.

IoT and Big Data Technologies for Monitoring and Processing Real-Time Healthcare Data

Recent advances in pervasive technologies, such as wireless, ad hoc networks, and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as the Internet of Things (IoT). The IoT is seen as an enabler to the development of intelligent and context-aware services and applications. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data technologies and data analytics have been recently proposed for timely analyzing information (i.e., data, events) streams. The main aim is to make users' life more comfortable according to their locations, current requirements, and ongoing activities. In this article, combining IoT techniques and Big data technologies into a holistic platform for continuous and real-time health-care data monitoring and processing is introduced. Real-testing experiments have been conducted and results are reported to show the usefulness of this platform in a real-case scenario.

Tracking Airborne Molecules from Afar: Three-Dimensional Metal–Organic Framework-Surface-Enhanced Raman Scattering Platform for Stand-Off and Real-Time Atmospheric Monitoring

Stand-off Raman spectroscopy combines the advantages of both Raman spectroscopy and remote detection to retrieve molecular vibrational fingerprints of chemicals at inaccessible sites. However, it is currently restricted to the detection of pure solids and liquids and not widely applicable for dispersed molecules in air. Herein, we realize real-time stand-off SERS spectroscopy for remote and multiplex detection of atmospheric airborne species by integrating a long-range optic system with a 3D analyte-sorbing metal-organic framework (MOF)-integrated SERS platform. Formed via the self-assembly of Ag@MOF core-shell nanoparticles, our 3D plasmonic architecture exhibits micrometer thick SERS hotspot to allow active sorption and rapid detection of aerosols, gas, and volatile organic compounds down to parts-per-billion levels, notably at a distance up to 10 m apart. The platform is highly sensitive to changes in atmospheric content, as demonstrated in the temporal monitoring of gaseous CO2 in several cycles. Importantly, we demonstrate the remote and multiplex quantification of polycyclic aromatic hydrocarbon mixtures in real time under outdoor daylight. By overcoming core challenges in current remote Raman spectroscopy, our strategy creates an opportunity in the long-distance and sensitive monitoring of air/gaseous environment at the molecular level, which is especially important in environmental conservation, disaster prevention, and homeland defense.

"Basicles": Microbial Growth and Production Monitoring in Giant Lipid Vesicles.

We present an optimized protocol to encapsulate bacteria inside giant unilamellar lipid vesicles combined with a microfluidic platform for real-time monitoring of microbial growth and production. The microfluidic device allows us to immobilize the lipid vesicles and record bacterial growth and production using automated microscopy. Moreover, the lipid vesicles retain hydrophilic molecules and therefore can be used to accumulate products of microbial biosynthesis, which we demonstrate here for a riboflavin-producing bacterial strain. We show that stimulation as well as inhibition of bacterial production can be performed through the liposomal membrane simply by passive diffusion of inducing or antibiotic compounds, respectively. The possibility to introduce as well as accumulate compounds in liposomal cultivation compartments represents great advantage over the current state of the art systems, emulsion droplets, and gel beads. Additionally, the encapsulation of bacteria and monitoring of individual lipid vesicles have been accomplished on a single microfluidic device. The presented system paves the way toward highly parallel microbial cultivation and monitoring as required in biotechnology, basic research, or drug discovery.

Design of real-time monitoring and intelligent management cloud platform for entire process of construction waste

In order to achieve real-time detection and intelligent control of generation links, transportation processing links, resource utilization and application of recycled products about construction waste, the real-time monitoring and intelligent control cloud platform is studied. This platform can dock urban external construction departments, urban management departments and other external e-government systems to achieve the aims of entire process detection and intelligent resource utilization. This paper studies the design and implementation of real-time monitoring and intelligent control cloud platform for construction waste. This platform is divided into three parts: the data acquisition layer, the data transmission layer, the big data processing layer and application service layer. The article focuses on the functions and technical implementation of each layer: data access design including big data monitoring technology in the whole process of construction waste control, storage design of cloud computing collaborative service technology in the whole process of construction waste control, real-time 3D visualization software and hardware support based on space-time cloud platform.

Low-Cost, Open Source IoT-Based SCADA System Design Using Thinger.IO and ESP32 Thing

Supervisory Control and Data Acquisition (SCADA) is a technology for monitoring and controlling distributed processes. SCADA provides real-time data exchange between a control/monitoring centre and field devices connected to the distributed processes. A SCADA system performs these functions using its four basic elements: Field Instrumentation Devices (FIDs) such as sensors and actuators which are connected to the distributed process plants being managed, Remote Terminal Units (RTUs) such as single board computers for receiving, processing and sending the remote data from the field instrumentation devices, Master Terminal Units (MTUs) for handling data processing and human machine interactions, and lastly SCADA Communication Channels for connecting the RTUs to the MTUs, and for parsing the acquired data. Generally, there are two classes of SCADA hardware and software; Proprietary (Commercial) and Open Source. In this paper, we present the design and implementation of a low-cost, Open Source SCADA system by using Thinger.IO local server IoT platform as the MTU and ESP32 Thing micro-controller as the RTU. SCADA architectures have evolved over the years from monolithic (stand-alone) through distributed and networked architectures to the latest Internet of Things (IoT) architecture. The SCADA system proposed in this work is based on the Internet of Things SCADA architecture which incorporates web services with the conventional (traditional) SCADA for a more robust supervisory control and monitoring. It comprises of analog Current and Voltage Sensors, the low-power ESP32 Thing micro-controller, a Raspberry Pi micro-controller, and a local Wi-Fi Router. In its implementation, the current and voltage sensors acquire the desired data from the process plant, the ESP32 micro-controller receives, processes and sends the acquired sensor data via a Wi-Fi network to the Thinger.IO local server IoT platform for data storage, real-time monitoring and remote control. The Thinger.IO server is locally hosted by the Raspberry Pi micro-controller, while the Wi-Fi network which forms the SCADA communication channel is created using the Wi-Fi Router. In order to test the proposed SCADA system solution, the designed hardware was set up to remotely monitor the Photovoltaic (PV) voltage, current, and power, as well as the storage battery voltage of a 260 W, 12 V Solar PV System. Some of the created Human Machine Interfaces (HMIs) on Thinger.IO Server where an operator can remotely monitor the data in the cloud, as well as initiate supervisory control activities if the acquired data are not in the expected range, using both a computer connected to the network, and Thinger.IO Mobile Apps are presented in the paper.