A novel, low power biosensor for real time monitoring of creatinine and urea in peritoneal dialysis

The real-time monitoring of a data is crucial in ensuring the accuracy of the acquired data. It determines whether the device is properly working or in fault. This paper proposed the design and implementation of a ZigBee-based wireless automatic meter reading system. It focuses on the development of a device that is capable of monitoring meter reader remotely. It sends the data hourly or daily using Zigbee as the transmitting medium. The proposed device uses software; XCTU, Arduino Programming Language, Multisim and hardware; Microcontroller, Pulse generating circuit, Zigbee antenna, 16X2 LCD Display to actually demonstrate the result. This device has a good potential in wireless meter reading due to its low-cost, low power consuming, and low data rate. The input is the pulse generated by the pulsating circuit and the output will be shown onto the LCD display and in the XCTU software proving that it transmitted wirelessly. The results successfully shows the data is received as what it is transmitted.

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LoRa-LBO: An Experimental Analysis of LoRa Link Budget Optimization in Custom Build IoT Test Bed for Agriculture 4.0

Agronomy ◽

10.3390/agronomy11050820 ◽

2021 ◽

Vol 11 (5) ◽

pp. 820

Author(s):

Mahendra Swain ◽

Dominik Zimon ◽

Rajesh Singh ◽

Mohammad Farukh Hashmi ◽

Mamoon Rashid ◽

...

Keyword(s):

Low Power ◽

Real Time ◽

Long Range ◽

Sensor Node ◽

Agricultural Field ◽

Real Time Monitoring ◽

Handheld Device ◽

Localization Algorithms ◽

Cloud Server ◽

Link Budget

The Internet of Things (IoT) is transforming all applications into real-time monitoring systems. Due to the advancement in sensor technology and communication protocols, the implementation of the IoT is occurring rapidly. In agriculture, the IoT is encouraging implementation of real-time monitoring of crop fields from any remote location. However, there are several agricultural challenges regarding low power use and long-range transmission for effective implementation of the IoT. These challenges are overcome by integrating a long-range (LoRa) communication modem with customized, low-power hardware for transmitting agricultural field data to a cloud server. In this study, we implemented a custom-based sensor node, gateway, and handheld device for real-time transmission of agricultural data to a cloud server. Moreover, we calibrated certain LoRa field parameters, such as link budget, spreading factor, and receiver sensitivity, to extract the correlation of these parameters on a custom-built LoRa testbed in MATLAB. An energy harvesting mechanism is also presented in this article for analyzing the lifetime of the sensor node. Furthermore, this article addresses the significance and distinct kinds of localization algorithms. Based on the MATLAB simulation, we conclude that hybrid range-based localization algorithms are more reliable and scalable for deployment in the agricultural field. Finally, a real-time experiment was conducted to analyze the performance of custom sensor nodes, gateway, and handheld devices.

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Implementation of Low-Power Wireless Sensor Network Protocol Stack for Real-Time Monitoring of Warehouses

Applied Engineering in Agriculture ◽

10.13031/aea.31.10920 ◽

2015 ◽

pp. 697-706

Keyword(s):

Wireless Sensor Network ◽

Low Power ◽

Real Time ◽

Sensor Network ◽

Network Protocol ◽

Wireless Sensor ◽

Protocol Stack ◽

Real Time Monitoring

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Design of a Remote Real-Time Monitoring System for Multiple Physiological Parameters Based on Smartphone

Journal of Healthcare Engineering ◽

10.1155/2019/5674673 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Noman Q. Al-Naggar ◽

Husam Mohammed Al-Hammadi ◽

Adel Mohammed Al-Fusail ◽

Zakarya Ali AL-Shaebi

Keyword(s):

Power Consumption ◽

Low Power ◽

Real Time ◽

Monitoring System ◽

Mobile Network ◽

Economic Benefits ◽

Physiological Parameters ◽

Low Power Consumption ◽

Real Time Monitoring ◽

Accuracy Measurement

Background. Utilization of the widely used wearable sensor and smartphone technology for remote monitoring represents a healthcare breakthrough. This study aims to design a remote real-time monitoring system for multiple physiological parameters (electrocardiogram, heart rate, respiratory rate, blood oxygen saturation, and temperature) based on smartphones, considering high performance, autoalarm generation, warning transmission, and security through more than one method. Methods. Data on monitoring parameters were acquired by the integrated circuits of wearable sensors and collected by an Arduino Mega 250 R3. The collected data were transmitted via a Wi-Fi interface to a smartphone. A patient application was developed to analyze, process, and display the data in numerical and graphical forms. The abnormality threshold values of parameters were identified and analyzed to generate an autoalarm in the system and transmitted with data to a doctor application via a third-generation (3G) mobile network and Wi-Fi. The performance of the proposed system was verified and evaluated. The proposed system was designed to meet main (sensing, processing, displaying, real-time transmission, autoalarm generation, and threshold value identification) and auxiliary requirements (compatibility, comfort, low power consumption and cost, small size, and suitability for ambulatory applications). Results. System performance is reliable, with a sufficient average accuracy measurement (99.26%). The system demonstrates an average time delay of 14 s in transmitting data to a doctor application via Wi-Fi compared with an average time of 68 s via a 3G mobile network. The proposed system achieves low power consumption against time (4 h 21 m 30 s) and the main and auxiliary requirements for remotely monitoring multiple parameters simultaneously with secure data. Conclusions. The proposed system can offer economic benefits for remotely monitoring patients living alone or in rural areas, thereby improving medical services, if manufactured in large quantities.

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Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2016/7129376 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 7

Author(s):

Mehmet Şimşir ◽

Raif Bayır ◽

Yılmaz Uyaroğlu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Low Power ◽

Real Time ◽

Economic Losses ◽

Real Time Monitoring ◽

System Operation ◽

Compact Structure ◽

Neural Network Approach ◽

Data Set

Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.

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