On-line Data Acquisition Research Articles

Classical vs. neural network-based PCA approaches for lossy image compression: Similarities and differences

The paper describes three lossy data compression techniques based on the principal component analysis (PCA), which are compared using the image compression task. The presented approach uses both classical PCA method based on the eigen-decomposition of the image data covariance matrix and two different neural network structures. The first neural structure is a two-layer feed-forward network with supervised learning acting as the so-called autoencoder, and the second one is a single-layered network with an unsupervised learning method commonly known as the generalized Hebbian algorithm. For each compression method mentioned above, the influence of the number of image segments (frames) and the number of eigenvalues/eigenvectors on the compression ratio and the image quality are examined using three different gray-scale test images. The paper also addresses a vital implementation issue regarding selecting appropriate data types to represent the compressed data. The paper’s main conclusion is that the classical PCA method outperforms its neural counterparts, both in terms of the decompressed image quality and the time required to perform the compression procedure. The positive aspect of using neural networks as a tool for PCA-based lossy data compression is that they do not require calculating the correlation matrix explicitly and thus can be used in online data acquisition schemes.

Can’t stop won’t stop: problematic phone use, sleep quality, and mental health in U.S. Graduate students

Objective Prior work has suggested the impact of problematic phone use on the mental health of graduate students. This research explores the mediating effect of sleep quality on this link. Participants A total of 186 graduate students from the United States were collected using an online data acquisition platform. Graduate study areas represented a wide range of subjects. Methods Participants completed questionnaires about problematic phone use, sleep quality, and both anxiety and depressive symptoms. Phone use was further broken down into time spent on specific phone applications. Results Analyses revealed significant indirect effects from problematic phone use to both depression and anxiety through diminished sleep quality. Effects were stronger for individuals who spent higher amounts of time on social media applications. Conclusions Findings suggest that U.S. graduate students are particularly prone to the negative effects of problematic phone use. Implications for interventions in graduate programs are discussed.

A Parallel Sequential SBAS Processing Framework Based on Hadoop Distributed Computing

With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth’s crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring massive SAR image data, the need for an efficient and lightweight InSAR processing platform has become urgent in various research fields. However, traditional sequential algorithms are incapable of meeting the huge challenges of low efficiency and frequent human interaction in large-scale InSAR data processing. Therefore, this study proposes a distributed parallel sequential SBAS (P2SBAS) processing chain based on Hadoop by effectively parallelizing and improving the current sequential SBAS method. P2SBAS mainly consists of two components: (1) a distributed SAR data storage platform based on HDFS, which supports efficient inter-node data transfer and continuous online data acquisition, and (2) several parallel InSAR processing algorithms based on the MapReduce model, including image registration, filtering, phase unwrapping, sequential SBAS processing, and so on. By leveraging the capabilities associated with the distributed nature of the Hadoop platform, these algorithms are able to efficiently utilize the segmentation strategy and perform careful boundary processing. These parallelized InSAR algorithm modules can achieve their goals on different nodes in the Hadoop distributed environment, thereby maximizing computing resources and improving the overall performance while comprehensively considering performance and precision. In addition, P2SBAS provides better computing and storage capabilities for small- and medium-sized teams compared to popular InSAR processing approaches based on cloud computing or supercomputing platforms, and it can be easily deployed on clusters thanks to the integration of various existing computing components. Finally, to demonstrate and evaluate the efficiency and accuracy of P2SBAS, we conducted comparative experiments on a set of 32 TerraSAR images of Beijing, China. The results demonstrate that P2SBAS can fully utilize various computing nodes to improve InSAR processing and can be applied well in large-scale LuTan-1 InSAR applications in the future.

IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform

Multi-unmanned systems are primarily composed of unmanned vehicles, drones, and multi-legged robots, among other unmanned robotic devices. By integrating and coordinating the operation of these robotic devices, it is possible to achieve collaborative multitasking and autonomous operations in various environments. In the field of surveying and mapping, the traditional single-type unmanned device data collection mode is no longer sufficient to meet the data acquisition tasks in complex spatial scenarios (such as low-altitude, surface, indoor, underground, etc.). Faced with the data collection requirements in complex spaces, employing different types of robots for collaborative operations is an important means to improve operational efficiency. Additionally, the limited computational and storage capabilities of unmanned systems themselves pose significant challenges to multi-unmanned systems. Therefore, this paper designs an edge–end–cloud integrated multi-unmanned system payload management and computing platform (IMUC) that combines edge, end, and cloud computing. By utilizing the immense computational power and storage resources of the cloud, the platform enables cloud-based online task management and data acquisition visualization for multi-unmanned systems. The platform addresses the high complexity of task execution in various scenarios by considering factors such as space, time, and task completion. It performs data collection tasks at the end terminal, optimizes processing at the edge, and finally transmits the data to the cloud for visualization. The platform seamlessly integrates edge computing, terminal devices, and cloud resources, achieving efficient resource utilization and distributed execution of computing tasks. Test results demonstrate that the platform can successfully complete the entire process of payload management and computation for multi-unmanned systems in complex scenarios. The platform exhibits low response time and produces normal routing results, greatly enhancing operational efficiency in the field. These test results validate the practicality and reliability of the platform, providing a new approach for efficient operations of multi-unmanned systems in surveying and mapping requirements, combining cloud computing with the construction of smart cities.

Spiral drawing deficits in children with prenatal alcohol exposure.

Empirical investigations reveal that, in comparison to their typically developing peers, children with histories of prenatal alcohol exposure experience deficits in writing but not drawing skills,both of which require fine motor control. This study examines drawing skills in this clinical group by assessing simple free-form spiral drawings with indices of spectral features and structural organization. Children with (n = 15) and without (n = 24) prenatal alcohol exposure used their dominant and nondominant hands to draw a series of spirals using a wireless pen stylus that either provided concurrent visual feedback in the form of a black ink trace or left no visible ink trace of each drawing. Spirals were drawn on a sheet of paper placed on a digitizing table, which facilitated online data acquisition. The data were assessed by power spectral density function analysis and sample entropy analysis. In comparison to their typically developing peers, children with prenatal alcohol exposure produced spirals with a lower mean frequency and less spectral variability. Spirals in the prenatally exposed group were also lower in complexity and structural organization than in the control group. These results occurred independently of hand dominance or the availability of visual feedback. The drawing skills of children with prenatal alcohol exposure have inherent signal characteristics that differ significantly from those produced by typically developing peers. Simple tasks requiring fine motor control may be useful in identifying individuals with fetal alcohol spectrum disorders.

Experimental performance analysis for reverse osmosis pilot plant subjected to different brackish salinity spectrum

The main obstacles to reverse osmosis desalination processes are high energy intensity and long-term continuity. Temperature and pressure have the greatest and most significant effects on energy use. The main objectives of the current study are to determine the pressure and temperature-dependent optimal operating parameters for a membrane desalination unit. To determine the ideal operating settings for a membrane unit, the impact of changing pressure and temperature on its performance was investigated. These two elements are closely connected to the energy consumption per cubic meter under various operating situations. The present work is experimentally carried out in a research laboratory for capacity building and future research studies in the desalination field established in the National Center of Water Research – Egypt. This laboratory is Egypt’s first multi-functional Desalination Research Station for seawater, brackish water, and related water treatment areas. The plant is equipped with online instrumentation and Data Acquisition System and 13 sensors for most physical parameters which economically affect membrane performance and desalination processes. These parameters, particularly pressure and temperature, are measured, evaluated and analyzed. According to the findings in this study, feed salinity and feed pressure both have significant impacts.At 13 bar pressure, the maximum salt rejection was 98.8%. When feed pressure is increased from 5 to 13 bars, there is a 73.3% decrease in permeate salinity. Additionally, applying a 13 bar feed pressure to water with a salinity of 1000 ppm results in the best water quality of 12 ppm. The relationship between feed pressure, brine salinity, and membrane water recovery appeared to be approximately linear and positive. More crucially, it was discovered that feed pressure, salinity, and water recovery are all constants for water permeability. A prototype for the maximum pressure (ranges from 15.6 to 10.8) and temperature (ranges from 21 to 35) at which the optimal recovery of the laboratory occurred was developed. Moreover, the developed prototype includes the corresponding permeate TDS and a specific energy for each optimal point.

Philosophy of the Future in the Context of Scientific and Pedagogical Workers Training and Artificial Intelligence Application

The article is aimed to introduce a conceptual and categorical framework of the future, to analyse the philosophical fundamentals of scientific and pedagogical workers training and artificial intelligence application, and to reveal applicable scenarios of establishment of efficient training in future in the context of application of artificial intelligence. Methodology: To conduct the research thoroughly we used mixed methodology. A scientific literature review was used in order to establish the theoretical basics of the investigation, to clarify the relevant ideas, and to develop the research methodology. During the investigation the most recent 41 papers devoted to the philosophy of future-focused education and application of artificial intelligence were selected to describe the problem accurately. To collect data the questionnaires, surveys and online data acquisition were applied. The research included three stages and lasted during September, 2021 – May, 2023. The research involved 119 scientific and pedagogical workers from institutions of higher education including instructors, post-graduate students and undergraduates. Results: The research interpreted the category artificial intelligence from the philosophical perspective, described the applications of artificial intelligence in the process of training of scientific and pedagogical workers at the institutions of higher education, and elaborated the model of artificial intelligence application while training scientific and pedagogical workers. Also, four future-focused scenarios to implement the model were outlined. Scientific Novelty: The study initially represents a unique perspective of philosophical interpretation of the future. Also, it brings original ideas to design the model of artificial intelligence application while training scientific and pedagogical workers that has not been done before. Conclusion: The model can be used for the improvement of scientific and pedagogical workers training at the institutions of higher education and prepare them to future transformation within the educational environment.

Status of the data acquisition, trigger, and slow control systems of the Mu2e experiment at Fermilab

The Mu2e experiment at the Fermilab will search for a coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10,000 over existing limits. The Mu2e Trigger and Data Acquisition System (TDAQ) uses otsdaq framework as the online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates several framework components — an artdaq-based DAQ, an art-based event processing, and an EPICS-based detector control system (DCS), and provides a uniform multi-user interface to its components through a web browser. Data streams from the Mu2e tracker and calorimeter are handled by the artdaq-based DAQ and processed by a one-level software trigger implemented within the art framework. Events accepted by the trigger have their data combined, post-trigger, with the separately read out data from the Mu2e Cosmic Ray Veto system. Foundation of the Mu2e DCS, EPICS – an Experimental Physics and Industrial Control System – is an open-source platform for monitoring, controlling, alarming, and archiving. A prototype of the TDAQ and the DCS systems has been built and tested over the last three years at Fermilab’s Feynman Computing Center, and now the production system installation is underway. This work presents their status and focus on the installation plans and procedures for racks, workstations, network switches, gateway computers, DAQ hardware, slow controls implementation, and testing.

Development of inferential sensor and real-time optimizer for a vacuum distillation unit by recurrent neural network modeling of time series data

In lube base oil production, a vacuum distillation unit (VDU) is a crucial unit operation which must be controlled tightly to meet final product specifications. To address this requirement, model-based optimal control can be employed, which requires an accurate model relating to the operation and quality variables involved. However, the mathematical modeling of VDU is hampered by the lack of process knowledge and the limited online data acquisition capability for key quality variables. As a recourse, data-based modeling can be attempted to develop inferential sensors and controllers but one is faced with the problem of temporal credit assignment due to the complex characteristics of the VDU process dynamics, e.g., varying time-lags among the variables. To overcome this difficulty, the use of a stacked RNN structure with sequence length is proposed for the prediction of kinematic viscosity. In addition, regression models for the product's distillation temperatures are constructed by testing a large number of regression model structures. Then, these quality predictors are applied in developing a real-time optimizer which adjusts key operating variables in order to satisfy the product specifications despite various disturbances. Accuracies of the soft sensor and the optimization model are evaluated, first offline and then, online through a commercial plant test performed at SK Ulsan Refinery Complex in Korea. The overall approach and framework are general and can be used for the development of inferential sensors and controllers in other similar situations.

A Low-Cost Online Data Acquisition and Processing System for Industrial Pollutants’ Monitoring

Recently, the prevention and control of industrial pollution has attracted extensive attention. Many studies based on emission data have achieved great success, but there are relatively few studies on the emission data acquisition. To this end, we propose a data acquisition and processing system (DAPS) for online monitoring of industrial pollutants. The system receives real-time emission data through the RS485, automatically generates periodic data, and saves it in the SQL Server database. Real-time Ethernet (RTE) is used to transmit control signals between the system and programmable logic controllers (PLCs). Emission data is transmitted to local industrial control systems and remote monitoring centers (RMCs) via a wireless network. We tested the acquisition function and communication function of the system through experiments and evaluated the overall performance of the system by observing the memory and CPU usage. The system is characterized by simple implementation and versatility and can be applied to continuous monitoring of various industrial pollutants.

FLASHIda enables intelligent data acquisition for top–down proteomics to boost proteoform identification counts

The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.

Power Systems’ Connectivity and Resiliency: Modeling, Simulation and Analysis

Over the last decade, online data acquisition and processing of power systems’ parameters led to significant improvements in power systems’ operability, control and reliability [...]

Computationally expedient Photovoltaic power Forecasting: A LSTM ensemble method augmented with adaptive weighting and data segmentation technique

Photovoltaics (PVs) hold the promise of sustainable electricity production. However, PV output is significantly influenced by variations in terrestrial solar radiation and other weather factors, causing problems in unit commitment, economic power dispatch and reliable electricity distribution from grid-connected, hybrid and off-grid PV plants. Thus, accurate and consistent PV output prediction is crucial for utilities companies and researchers. Current initiatives enlist a plethora of forecasting techniques, including: statistical models, physical models, artificial neural networks and deep learning algorithms. Nevertheless, most of these approaches suffer from computational costs, complex models and uncertainties. Hence, this research employed an ensemble-based Long Short-Term Memory (LSTM) algorithm comprising 10 component LSTM models. The method compares the effects of different data segmentations (three-months to one-day) and is based on varying time-horizons (14-days to 5-mins) in order to compare the effects of seasonal and periodic variations on time-series data and PV output forecast. The comparison is then used to minimise uncertainty by implementing grid search technique. Subsequently, the model’s performance was evaluated using MAPE analysis for two cases involving erratic weather conditions and PV output in two specific days of the year 2020. From the evaluation, the best prediction was found to be for the two-week dataset with MAPE of 6.02. This approach combined with online data acquisition from the Yulara Solar System power plant in central Australia leads to a more practical, computationally economical and robust PV power generation forecasting technique.

Swallowing assessment in patients with dysphagia: Validity and reliability of a pocket-sized ultrasound system.

BackgroundThe use of ultrasound as an adjunct to clinical swallowing evaluation provides quantitative physiological and morphological data. As a low‐risk procedure, ultrasound imaging can be performed outside of a medical setting. This is particularly important for patients living in rural areas with restricted access to a hospital. Technical advances have produced pocket‐sized ultrasound technology that is more affordable, and therefore within the fiscal reach of most allied health services.AimsTo explore the validity and reliability of pocket‐sized ultrasound technology in dysphagia assessment.Methods & ProceduresData were acquired from 43 patients with dysphagia using the Clarius ultrasound device. Ultrasound and videofluoroscopic measures of hyoid and laryngeal displacement during liquid and puree swallowing were collected concurrently to quantify correlation and agreement between identical measures derived from the two instruments. Reliability of ultrasound was assessed for measures of hyoid and laryngeal displacement, tongue thickness, and size of the submental muscles in eight patients. Reliability was evaluated for the entire process of data acquisition including scanning and online measurement using an iPad in a clinical setting and for offline measurement on a computer screen to explore environmental influences on reliability.Outcomes & ResultsResults revealed poor correlation between the measures of interest across instruments. Reliability of the entire process of data acquisition in a clinical setting was insufficient while reliability was more promising for offline measurements.Conclusions & ImplicationsThe clinical use of pocket‐sized ultrasound devices, such as the Clarius system, for swallowing evaluation is not indicated at this time. Enhanced validity and reliability of the entire process of data acquisition are needed prior to clinical translation of such technology.WHAT THIS PAPER ADDSWhat is already known on the subject The use of ultrasound allows for radiation‐free, non‐invasive swallowing assessment. Some data suggest that ultrasound is valid and reliable in the evaluation of swallowing using standard‐sized equipment. Insufficient validity and reliability have been reported for pocket‐sized ultrasound technology in the assessment of healthy swallowing. What this paper adds to existing knowledge This research is the first to provide validity and reliability data of the pocket‐sized Clarius technology in the evaluation of swallowing in patients with dysphagia. Insufficient validity and reliability of online data acquisition in a clinical environment were found. Reliability for offline measurement was more promising. What are the potential or actual clinical implications of this work? The clinical use of pocket‐sized ultrasound devices, such as the Clarius system, for swallowing assessment is not indicated at this time.

Mu2e DAQ and slow control systems

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10,000 over existing limits. The Mu2e Trigger and Data Acquisition System (TDAQ) uses otsdaq as the online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multiuser, web-based, interface accessible through a web browser. The data stream from the detector subsystems is read by a software filter algorithm that selects events which are combined with the data flux coming from a Cosmic Ray Veto System. The Detector Control System (DCS) has been developed using the Experimental Physics and Industrial Control System (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS System has been integrated into otsdaq. A prototype of the TDAQ and the DCS systems has been built at Fermilab’s Feynman Computing Center. In this paper, we report on the progress of the integration of this prototype in the online otsdaq software.

Data acquisition and slow control interface for the Mu2e experiment

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10000 over existing limits. The Mu2e Trigger and Data Acquisition System (TDAQ) uses otsdaq as the online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multi-user, web-based, interface accessible through a web browser. The data stream from the detector subsystems is read by a software filter algorithm that selects events which are combined with the data flux coming from a cosmic ray veto system. The Detector Control System (DCS) has been developed using the Experimental Physics and Industrial Control System (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS system has been integrated into otsdaq. A prototype of the TDAQ and the DCS systems has been built at Fermilab’s Feynman Computing Center. In this paper, we report on the progress of the integration of this prototype in the online otsdaq software.

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.

National Tibetan Plateau Data Center: Promoting Earth System Science on the Third Pole

AbstractThe Tibetan Plateau, known as the world’s “Third Pole” due to its high altitude, is experiencing rapid, intense climate change, similar to and even far more than that occurring in the Arctic and Antarctic. Scientific data sharing is very important to address the challenges of better understanding the unprecedented changes in the Third Pole and their impacts on the global environment and humans. The National Tibetan Plateau Data Center (TPDC, http://data.tpdc.ac.cn) is one of the first 20 national data centers endorsed by the Ministry of Science and Technology of China in 2019 and features the most complete scientific data for the Tibetan Plateau and surrounding regions, hosting more than 3,500 datasets in diverse disciplines. Fifty datasets featuring high-mountain observations, land surface parameters, near-surface atmospheric forcing, cryospheric variables, and high-profile article-associated data over the Tibetan Plateau, frequently being used to quantify the hydrological cycle and water security, early warning assessments of glacier avalanche disasters, and other geoscience studies on the Tibetan Plateau, are highlighted in this manuscript. The TPDC provides a cloud-based platform with integrated online data acquisition, quality control, analysis, and visualization capability to maximize the efficiency of data sharing. The TPDC shifts from the traditional centralized architecture to a decentralized deployment to effectively connect Third Pole–related data from other domestic and international data sources. As an embryo of data sharing and management over extreme environment in the upcoming “big data” era, the TPDC is dedicated to filling the gaps in data collection, discovery, and consumption in the Third Pole, facilitating scientific activities, particularly those featuring extensive interdisciplinary data use.

Prototype Data Acquisition and Slow Control Systems for the Mu2e Experiment

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10 000 over existing limits. Such a charged lepton flavor-violating reaction probes new physics at a scale unavailable with direct searches at either present or planned high-energy colliders. The Mu2e Trigger and Data Acquisition (TDAQ) system exploits otsdaq as its online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multi-user, web-based, interface accessible through a web browser. The read out controllers (ROCs) stream out zero-suppressed data continuously from the detector subsystems to the data transfer controllers (DTCs). The data stream is then read over the peripheral component interconnect express (PCIe) bus to a software filter algorithm that selects events which are combined with the data flux coming from a cosmic-ray veto (CRV) system. The detector control system (DCS) has been developed using the experimental physics and industrial control system (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS has been integrated into otsdaq. A prototype of the TDAQ system and the DCS has been built at Fermilab's Feynman Computing Center. In this article, we report on the progress of the integration of this prototype in the online otsdaq software.

Online monitoring of water pollution level based on dissolved oxygen (DO) and biochemical oxygen demand (BOD) using wireless sensor system

In the case of water pollution, including the sea, rivers, lakes, and reservoirs, usually when measuring the parameter of Dissolved Oxygen (DO) and Biochemical Oxygen Demand (BOD) values exceeds the pollution level. Online and continuous monitoring is essential to monitor water conditions so that if there is pollution, it can be immediate handling. Wireless sensor network technology becomes part of the Internet of Things makes it possible for online data acquisition from several node stations that are spread in monitoring locations. This paper purpose the online BOD dan DO data acquisition continuously builds with combine the wireless sensor system and database replication concept. From implementing a database replication method for online monitoring of water pollution level, database replication continuously ensures consistency and prevents redundant data between node sensor station and server database, and provides reliable information on the www website.