Data Acquisition Nodes Research Articles

Data Acquisition for Estimating Energy-Efficient Solar-Powered Sensor Node Performance for Usage in Industrial IoT

In the era of rapid technological growth, we are facing increased energy consumption. The question of using renewable energy sources is also essential for the sustainability of wireless sensor networks and the Industrial Internet of Things, especially in scenarios where there is a need to deploy an extensive number of sensor nodes and smart devices in industrial environments. Because of that, this paper targets the problem of monitoring the operations of solar-powered wireless sensor nodes applicable for a variety of Industrial IoT environments, considering their required locations in outdoor scenarios and the efficient solar power harvesting effects. This paper proposes a distributed wireless sensor network system architecture based on open-source hardware and open-source software technologies to achieve that. The proposed architecture is designed for acquiring solar radiation data and other ambient parameters (solar panel and ambient temperature, light intensity, etc.). These data are collected primarily to define estimation techniques using nonlinear regression for predicting solar panel voltage outputs that can be used to achieve energy-efficient operations of solar-powered sensor nodes in outdoor Industrial IoT systems. Additionally, data can be used to analyze and monitor the influence of multiple ambient data on the efficiency of solar panels and, thus, powering sensor nodes. The architecture proposal considers the variety of required data and the transmission and storage of harvested data for further processing. The proposed architecture is implemented in the small-scale variants for evaluation and testing. The platform is further evaluated with the prototype sensor node for collecting solar panel voltage generation data with open-source hardware and low-cost components for designing such data acquisition nodes. The sensor node is evaluated in different scenarios with solar and artificial light conditions for the feasibility of the proposed architecture and justification of its usage. As a result of this research, the platform and the method for implementing estimation techniques for sensor nodes in various sensor and IoT networks, which helps to achieve edge intelligence, is established.

A Study Regarding Power Consumption of An IoT Node For Image Retrieval and its Optimization

Abstract This paper analyses the power consumption of an IoT node that captures images and transmits them to a cloud storage. Various implementation versions of the IoT node are proposed, focusing on the utilization of common components to achieve cost-effectiveness and minimize the maintenance requirements. The realised node variants were initially tested in laboratory conditions and then commissioned and deployed outdoors, being located in an orchard where they remained operational during 7 months under different meteorological conditions. The proposed solution utilizes an ESP32 microcontroller and a camera with a resolution of 1.3 Megapixels as a data acquisition node whose energy is assured by a Li-Po battery, charged through a solar panel. Wi-Fi communication was employed to transmit the images to the server. The power consumption of the node was evaluated for different variants of software optimization using the HTTP or the MQTT transmission modes. The instantaneous current consumption of the nodes was measured in laboratory to identify the power consumption for each phase of the program execution. The experiments revealed that the MQTT transmission mode consumed considerably less power than the HTTP mode. Additional experiments have been performed in which the variation of the battery voltage, the solar panel voltage and the number of daily emissions in the examined period, as well as correlations between these quantities have been analyzed.

Impact of pricing leadership on blockchain data acquisition efforts in a circular supply chain

Motivated by the industrial observation of the increasing application of blockchain in the supply chain and its improvement in terms of consumer demand, this study examines the impact of investment decisions of blockchain data acquisition nodes upstream and downstream of the supply chain on firms’ product recycling and pricing, market demand, and profits. The influences of different pricing leadership (i.e. supplier-led vs. retailer-led) in a supply chain are also thoroughly investigated. First, the presence of a strong retailer dramatically reduces suppliers’ incentive to invest in blockchain. However, the supplier is still likely to invest more in a retailer-led supply chain than that in a supply chain led by itself under certain conditions; that is, the cost of blockchain is relatively small, or consumers’ sensitivity to blockchain or product recycling benefits is rather large. Second, the total number of blockchain data acquisition nodes, market demand, and supply chain profit in a retailer-led supply chain are higher than in a supplier-led supply chain. Finally, interestingly, the supplier will be better off in a retailer-led supply chain when the return effect of product recycling is relatively high, while the cost-effectiveness of blockchain investment is relatively low.

An IoT-based interoperable architecture for wireless biomonitoring of patients with sensor patches

The alliance between the Internet of Things (IoT) and healthcare has the potential to improve healthcare assistance at different stages of care through distributed vital sign sensing, paving the way for domiciliary hospitalization. In this work, we propose an innovative design for an IoT-based interoperable healthcare system to wirelessly monitor and classify patient status. To support our research, we identify gaps, and discuss standards, protocols and technologies based on works that use relevant IoT applications in healthcare. The proposed architecture is centered on several low-energy unobtrusive sensors attached to the patients’ bodies, as well as their beds, which encompass data acquisition nodes linked to a smart gateway that aggregates data. The smart gateway is integrated with an existing hospital information system through the exchange of Electronic Health Records (EHR), making relevant patient data easily available to health professionals on systems which are familiar to them. A use case scenario is presented in order to fulfill functional and non-functional requirements and provide a better understanding of connection and communication between the distinct entities of the proposed architecture, which is based on Bluetooth Low Energy (BLE) technology at the data acquisition level, the Message Queuing Telemetry Transport (MQTT) protocol at the internal level, and on the Fast Healthcare Interoperability Resources (FHIR) standard at the higher level.

Green Building Design Based on 5G Network and Internet of Things System

An information-based and intelligent comprehensive energy consumption management system should not only collect, manage, and analyze energy consumption data but also monitor the environmental data inside the building, to manage and display the real energy consumption and internal environmental quality more effectively and reliably. In order to solve this problem, the design and construction of the front-end hardware of the energy consumption management system are proposed, mainly including the design of data acquisition node and data centralization end. The design of data acquisition node includes the selection of DSP controller, wireless communication module, and data acquisition module and the design of main circuit and peripheral circuits. The design of data centralization end includes the selection of its high-performance processor and the transplantation of Linux operating system. This paper only introduces the design and research of lighting control algorithm and applies fuzzy control algorithm to intelligent lighting. The fuzzy control system is introduced, and a fuzzy controller based on intelligent lighting is created. The fuzzy control rule table and fuzzy control query table of the intelligent lighting system are obtained through the fuzzy control algorithm. According to the basic rules of light intensity control, when the indoor light intensity is greater than the set value of light intensity, the LED lamps are turned off, so when e < 0 , the LED lamp is off and the PWM output value is 0. Therefore, when designing the fuzzy controller of intelligent lighting system, the basic domain of illumination intensity error E is 0500 , excluding the case that the indoor illumination intensity is greater than the set value of illumination intensity.

Development of Cloud Computing Platform Based on Neural Network

Aiming at the problems of small data storage, small platform throughput, and high energy consumption in the development of existing cloud computing platforms, this paper develops a cloud computing platform based on neural networks. In the design of the platform, firstly, the functions of the cloud computing platform are determined. Based on the function design, the hardware and software of the cloud computing platform are designed. In the hardware design, the topology of cloud computing platform, data acquisition module, single-chip microcomputer, node deployment, and other types of functional hardware are designed. In the software design, the neural network is mainly used to remove the redundancy of data stored in a cloud computing platform, design the data processing flow of cloud computing platform, and complete the development of cloud computing platform based on neural network. The experimental results show that the cloud computing platform based on the neural network designed in this paper runs faster, the throughput of platform data has been significantly improved, and the operating energy consumption of the platform is low.

Solar Driven Self-learning Duty Cycle Control Algorithm for Maintenance-free Environmental Data Acquisition Nodes

Environmental wireless sensor network (EWSN) nodes are ordinarily implemented as embedded devices which use low-power and low-cost microcontrollers whose duty cycle is controlled by a machine learning method. This article updates a previously designed Q-learning (QL) algorithm to reduce the number of failed operational duty cycles in solar-powered EWSN nodes. The innovation is based on modification of the QL learning process and was tested and verified on an EWSN node model using a 5-year dataset of solar irradiance values. By designing a new learning algorithm, the number of failures in operational cycles was significantly reduced, specifically by a factor of ten compared to the reference algorithm.

Research on internal network data security monitoring method based on NB-IoT

In order to overcome the problems of poor data classification accuracy and effectiveness of traditional data monitoring methods, this paper designs a data security monitoring method based on narrow-band Internet of things. Firstly, the model of network data acquisition and sensor node’s optimal configuration is established to collect intranet data. Based on the analysis of data characteristics, dynamic intranet data analysis indexes are designed from three aspects: establishing security incident quantity index, establishing address entropy index and data diversion. According to the above-mentioned narrow-band data aggregation rate, the security index of the Internet of things is calculated to realize the security of monitoring data. The experimental results show that: whether the network attack exists or not, the accuracy rate of the method is always higher than 90%, the classification time is less than 4 s, and the energy consumption of monitoring process is always less than 150 J, which fully proves that the method achieves the design expectation.

Optimization of Water Microbial Concentration Monitoring System Based on Internet of Things

The Internet of Things is an emerging information industry. Applying the information collection, transmission, and processing technologies in the Internet of Things technology to environmental monitoring, environmental emergency, and other environmental protection supervision fields will greatly improve the speed and accuracy of environmental supervision and facilitate the scientific development of environmental protection. Through the Internet of Things, people can obtain a large amount of reliable real-time information, and it is not easy to be affected by time, place, and environment, while the wireless sensor network has the advantages of easy installation and low cost, so environmental monitoring through the Internet of Things is the future development trend. In this paper, in view of the current situation of water scarcity and serious water pollution in China, combined with the development trend and advantages of the Internet of Things (IoT), and based on the inadequacy of the existing microbial sensor data collection equipment, we propose a design scheme of microbial concentration monitoring system for waters based on IoT. The system is based on Zig Bee wireless sensor network to build a common data acquisition platform and design special hardware to carry out high-precision microbial sensor data acquisition in water and through the PC to complete the real-time measurement data storage, waveform display, and data processing. In this paper, the schematic diagram and PCB board design of the system hardware module NUC120 main control board, CC2530 RF board, Wi-Fi wireless communication module, and high-precision ADC acquisition module are completed and fabricated. Then, the four modules are combined to realize the development of the data aggregation node and data acquisition node of the dedicated Zig Bee wireless network hardware device.

Design and implementation of real‐time monitoring system for atmospheric particles based on a cloud platform

In order to reduce the cost of data acquisition and arrange the data acquisition nodes flexibly to meet the requirements of mobile pollution sources and unfixed data collection points, a real-time monitoring system for atmospheric particles based on a cloud platform is proposed here. The movable data acquisition nodes for atmospheric particles are designed to collect the particle concentration data, which are uploaded to the cloud platform through the general packet radio service (GPRS) network. The android application gets the atmospheric particle concentration data from the cloud platform in real time. Data acquisition nodes can be flexibly added to the system. It greatly improves the sharing of atmospheric particle concentration data. The test results show that the system has stable performance and low error rate.

Aircraft Maintenance Training Simulation System Based on “CAN Bus + Ethernet” Architecture

Aiming at the problem of centralized data collection scheme, a distributed data acquisition and control scheme based on “CAN bus + Ethernet” is proposed. According to the functional structure design, the cockpit control equipment is defined as independent data acquisition and control nodes on the communication network. The architecture and related key technologies improve the reliability, maintainability and testability of the system. The application of this system to carry out training shortens the training period of aviation ground handling personnel and saves a large amount of maintenance and support funds.

Cloud connection oriented real‐time monitoring system for atmospheric particles

To meet the requirements of data acquisition from mobile pollution sources and unfixed data acquisition points in atmospheric particle monitoring system, this study designs a real-time monitoring system for atmospheric particles such as PM2.5 based on a single-chip microcomputer, an ET-iLink open cloud platform and an Android operating system. The movable data acquisition nodes for atmospheric particles are designed. The laser scattering method is used to obtain the data of atmospheric particle concentration, which are uploaded to the cloud platform through the GPRS network. The Android application obtains the real time data of atmospheric particle concentration from the cloud platform. Thus the real-time remote monitoring for atmospheric particles is realised. The test results show that the system has the following advantages: stable performance, high scalability and low error rate.

Design of a new wireless data acquisition system for civil structural monitoring

Traditional civil structure wired monitoring systems suffer complicated wiring, high cost, poor flexibility, and available wireless sensors network (WSN)-based wireless monitoring systems are inconvenient to access the internet. In this paper, based on the 6LoWPAN means: IPv6 low-power wireless personal area network WSN, a new wireless data acquisition system is proposed for structural monitoring applications, which consists of data acquisition nodes, a border node and a remote monitoring centre. The hardware design of the wireless data acquisition system is based on the CC2538 wireless transceiver, and inertial sensors and temperature and humidity sensors are integrated. Based on the Contiki-3.0 embedded operating system, the embedded application software of 6LoWPAN data acquisition nodes and border router nodes are designed. The remote monitoring centre of structural monitoring system is developed using the graphical user interface development environment (GUIDE) tool of the MATLAB software. Experimental testing results show that the system is stable and reliable and can realise interconnection between 6LoWPAN network and traditional IPv4 network. Moreover, the network performance is satisfactory. Therefore, the proposed wireless structural monitoring system has a good practical engineering application value.

Design of a new wireless data acquisition system for civil structural monitoring

Traditional civil structure wired monitoring systems suffer complicated wiring, high cost, poor flexibility, and available wireless sensors network (WSN)-based wireless monitoring systems are inconvenient to access the internet. In this paper, based on the 6LoWPAN means: IPv6 low-power wireless personal area network WSN, a new wireless data acquisition system is proposed for structural monitoring applications, which consists of data acquisition nodes, a border node and a remote monitoring centre. The hardware design of the wireless data acquisition system is based on the CC2538 wireless transceiver, and inertial sensors and temperature and humidity sensors are integrated. Based on the Contiki-3.0 embedded operating system, the embedded application software of 6LoWPAN data acquisition nodes and border router nodes are designed. The remote monitoring centre of structural monitoring system is developed using the graphical user interface development environment (GUIDE) tool of the MATLAB software. Experimental testing results show that the system is stable and reliable and can realise interconnection between 6LoWPAN network and traditional IPv4 network. Moreover, the network performance is satisfactory. Therefore, the proposed wireless structural monitoring system has a good practical engineering application value.

Design of the transducer for multi-direction wind energy harvesting based on PZT

This paper presents key design ideas and performance of the transducer for multi-direction wind energy harvesting. Lead zirconate titanate (PZT) and cantilever beam are introduced to set up the piezoelectric vibrator unit for vibration-to-electric energy conversion. The conversion circuit and the storage unit are designed to output and store electric energy. In addition, wireless data transmission system based on microprocessor is proposed to realize information acquisition for data acquisition nodes. The experimental results show that the transducer achieves good practical property, and it can fill a 25 mAh lithium polymer battery with the continuous 40 min work in the 11 m s−1 wind speed environment. The transducer can work without the wind direction restrictions. It can be widely used in data monitoring, wireless data transmission and other fields with simple structure, high energy utilization rate, low cost and environmental-friendly.

Design of an Intelligent Bridge Structure Health Monitoring System

This paper designs an intelligent bridge structure health monitoring system (BSHMS) based on ZigBee wireless sensor network. Based on 3G wireless transmission technology and ZigBee technology, it serves as a wireless monitoring platform with amulti-level network architecture composed of data acquisition node, centre node(gateway) and remote server. In this paper, system hardware circuit and software system redesigned, CC2530is applied in the Contiki operating system. Then, various modules of the entire system are tested and debugged online. The test shows that the system can run stably and capture various data accurately before transmitting it to the rear server, thus meeting the design requirements.

Wireless Sensor Networks Composed of Standard Microcomputers and Smartphones for Applications in Structural Health Monitoring.

Wireless sensor networks have attracted great attention for applications in structural health monitoring due to their ease of use, flexibility of deployment, and cost-effectiveness. This paper presents a software framework for WiFi-based wireless sensor networks composed of low-cost mass market single-board computers. A number of specific system-level software components were developed to enable robust data acquisition, data processing, sensor network communication, and timing with a focus on structural health monitoring (SHM) applications. The framework was validated on Raspberry Pi computers, and its performance was studied in detail. The paper presents several characteristics of the measurement quality such as sampling accuracy and time synchronization and discusses the specific limitations of the system. The implementation includes a complementary smartphone application that is utilized for data acquisition, visualization, and analysis. A prototypical implementation further demonstrates the feasibility of integrating smartphones as data acquisition nodes into the network, utilizing their internal sensors. The measurement system was employed in several monitoring campaigns, three of which are documented in detail. The suitability of the system is evaluated based on comparisons of target quantities with reference measurements. The results indicate that the presented system can robustly achieve a measurement performance commensurate with that required in many typical SHM tasks such as modal identification. As such, it represents a cost-effective alternative to more traditional monitoring solutions.

A Robust Data Interpolation Based on a Back Propagation Artificial Neural Network Operator for Incomplete Acquisition in Wireless Sensor Networks

The data space collected by a wireless sensor network (WSN) is the basis of data mining and data visualization. In the process of monitoring physical quantities with large time and space correlations, incomplete acquisition strategy with data interpolation can be adopted to reduce the deployment cost. To improve the performance of data interpolation in such a scenario, we proposed a robust data interpolation based on a back propagation artificial neural network operator. In this paper, a neural network learning operator is proposed based on the strong fault tolerance of artificial neural networks. The learning operator is trained by using the historical data of the data acquisition nodes of WSN and is transferred to estimate the value of physical quantities at the locations where sensors are not deployed. The experimental results show that our proposed method yields smaller interpolation error than the traditional inverse-distance-weighted interpolation (IDWI) method.

Design of Water Quality Monitoring System for Aquaculture Based on ZigBee

The aquaculture industry is developing on a large-scale. Now most aquaculture households use the artificial way of water quality diagnosis, decision-making and adjustment, the data cannot accurately reflect the water quality status. The data acquisition node used distributed self-organizing network structure, it accomplished the detection of water temperature, pH value, dissolved oxygen, conductivity and other water quality parameters. ZigBee wireless communication module was used to the data exchange between the data acquisition node and router node, router node and coordinator node medium. The synchronization of the whole network node to sleep and wake up was realized using the time synchronization algorithm, it greatly reduced the system power consumption. The system was tested in Sanya university natural lake, it can realize the monitoring function of aquaculture water quality parameters, all-weather real-time transmission of information and automatic regulating or remote manual adjustment according to the preset dissolved oxygen content.

Real Time In-situ Detection System Research for Methane in Offshore Shallow Gas based on Thin Film Interface

In order to get ridded of the non real-time detection methods of artificial site sampled and laboratory instrument analyzed in the field of methane detection in the offshore shallow gas, real-time in-situ detection system for methane in offshore shallow gas was designed by the film interface.The methane in the offshore shallow gas through the gas-liquid separation membrane of polymer permeation into the system internal detection probe, analog infrared micro gas sensor sensed the methane concentration and the corresponded output value, data acquisition and communication node fitted into standard gas concentration.Based on the experimental data compared with the traditional detection method, and further analyzed the causes of error produced by the case experiment. The application results show that the system can achieve a single borehole layout, long-term on-line in-situ on-line detection, and improve the detection efficiency and the timeliness of the detection data.