scholarly journals An open source Real Time IOT based environmental sensor monitoring system

10.29007/q4cf ◽  
2018 ◽  
Author(s):  
Ronak Vithlani ◽  
Siddharth Fultariya ◽  
Mahesh Jivani ◽  
Haresh Pandya
Keyword(s):  
Embedded System ◽  
Open Source ◽  
Real Time ◽  
Monitoring System ◽  
Information Structure ◽  
Low Cost ◽  
Environmental Parameters ◽  
Test Bed ◽  
Network Construction ◽  
Embedded Sensing

In this paper, we have described an operative prototype for Internet of Things (IoT) used for consistent monitoring various environmental sensors by means of low cost open source embedded system. The explanation about the unified network construction and the interconnecting devices for the consistent measurement of environmental parameters by various sensors and broadcast of data through internet is being presented. The framework of the monitoring system is based on a combination of embedded sensing units, information structure for data collection, and intellectual and context responsiveness. The projected system does not involve a devoted server computer with respect to analogous systems and offers a light weight communication protocol to monitor environment data using sensors. Outcomes are inspiring as the consistency of sensing information broadcast through the projected unified network construction is very much reliable. The prototype was experienced to create real-time graphical information rather than a test bed set-up.

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

Smart and Low Cost Real Time Water Quality Monitoring System Using IoT

2019 ◽  
Author(s):  
Jeba Anandh S ◽  
Anandharaj M ◽  
Aswinrajan J ◽  
Karankumar G ◽  
Karthik P
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring

Intelligent Environmental Monitoring System Based on Multi-Sensor Data Technology

2020 ◽  
Vol 11 (4) ◽  
pp. 57-71
Author(s):  
Qiuxia Liu
Keyword(s):  
Data Fusion ◽  
Environmental Monitoring ◽  
Real Time ◽  
Monitoring System ◽  
Intelligent System ◽  
Evidence Synthesis ◽  
Environmental Parameters ◽  
Monitoring Data ◽  
Sensor Data ◽  
Environmental Monitoring System

Using multi-sensor data fusion technology, ARM technology, ZigBee technology, GPRS, and other technologies, an intelligent environmental monitoring system is studied and developed. The SCM STC12C5A60S2 is used to collect the main environmental parameters in real time intelligently. The collected data is transmitted to the central controller LPC2138 through the ZigBee module ATZGB-780S5, and then the collected data is transmitted to the management computer through the GPRS communication module SIM300; thus, the real-time processing and intelligent monitoring of the environmental parameters are realized. The structure of the system is optimized; the suitable fusion model of environmental monitoring parameters is established; the hardware and the software of the intelligent system are completed. Each sensor is set up synchronously at the end of environmental parameter acquisition. The method of different value detection is used to filter out different values. The authors obtain the reliability of the sensor through the application of the analytic hierarchy process. In the analysis and processing of parameters, they proposed a new data fusion algorithm by using the reliability, probability association algorithm, and evidence synthesis algorithm. Through this algorithm, the accuracy of environmental monitoring data and the accuracy of judging monitoring data are greatly improved.

scholarly journals A study on the development of a light scattering particulate matter sensor and monitoring system

2020 ◽  
Vol 17 (3) ◽  
pp. 867-890
Author(s):  
Jun-Hee Choi ◽  
Hyun-Sug Cho
Keyword(s):  
Big Data ◽  
Particulate Matter ◽  
Light Scattering ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Gravimetric Method ◽  
Correction Method ◽  
Measurement Methods ◽  
Linear Correction

The gravimetric method, which is mainly used among particulate matter (PM) measurement methods, includes the disadvantages that it cannot measure PM in real time and it requires expensive equipment. To overcome these disadvantages, we have developed a light scattering type PM sensor that can be manufactured at low cost and can measure PM in real time. We have built a big data system that can systematically store and analyze the data collected through the developed sensor, as well as an environment where PM states can be monitored mobile in real time using such data. In addition, additional studies were conducted to analyze and correct the collected big data to overcome the problem of low accuracy, which is a disadvantage of the light scattering type PM sensor. We used a linear correction method and proceeded to adopt the most suitable value based on error and accuracy.

scholarly journals An IoT based Low-cost Weather Monitoring System for Smart Farming

2021 ◽  
Author(s):  
L.P.S.S.K. Dayananda ◽  
A. Narmilan ◽  
P. Pirapuraj
Keyword(s):  
Liquid Crystal ◽  
Mobile Phone ◽  
Real Time ◽  
Monitoring System ◽  
Liquid Crystal Display ◽  
Low Cost ◽  
Weather Conditions ◽  
Weather Data ◽  
The Internet ◽  
Arduino Uno

Background: Weather monitoring is an important aspect of crop cultivation for reducing economic loss while increasing productivity. Weather is the combination of current meteorological components, such as temperature, wind direction and speed, amount and kind of precipitation, sunshine hours and so on. The weather defines a time span ranging from a few hours to several days. The periodic or continuous surveillance or the analysis of the status of the atmosphere and the climate, including parameters such as temperature, moisture, wind velocity and barometric pressure, is known as weather monitoring. Because of the increased usage of the internet, weather monitoring has been upgraded to smart weather monitoring. The Internet of Things (IoT) is one of the new technology that can help with many precision farming operations. Smart weather monitoring is one of the precision agriculture technologies that use sensors to monitor correct weather. The main objective of the research is to design a smart weather monitoring and real-time alert system to overcome the issue of monitoring weather conditions in agricultural farms in order for farmers to make better decisions. Methods: Different sensors were used in this study to detect temperature and humidity, pressure, rain, light intensity, CO2 level, wind speed and direction in an agricultural farm and real time clock sensor was used to measured real time weather data. The major component of this system was an Arduino Uno microcontroller and the system ran according to a program written in the Arduino Uno software. Result: This is a low-cost smart weather monitoring system. This system’s output unit were a liquid crystal display and a GSM900A module. The weather data was displayed on a liquid crystal display and the GSM900A module was used to send the data to a mobile phone. This smart weather station was used to monitor real-time weather conditions while sending weather information to the farmer’s mobile phone, allowing him to make better decisions to increase yield.

scholarly journals Arduino based platform for process control learning

2020 ◽  
Vol 6 (5) ◽  
pp. 0585-0593
Author(s):  
Bruna Couto Molinar Henrique ◽  
Leonardo Couto Molinar Henrique ◽  
Humberto Molinar Henrique
Keyword(s):  
Open Source ◽  
Real Time ◽  
Low Cost ◽  
Control Unit ◽  
Absolute Error ◽  
Internal Model Control ◽  
Pid Controllers ◽  
Experimental Unit ◽  
Source Codes ◽  
Model Control

This work deals with implementation of an experimental flowrate control unit using free and low-cost hardware and software. The open-source software Processing was used to develop the source codes and user graphical interface and the open-source electronic prototyping platform Arduino was used to acquire data from an experimental unit. Work presents descriptions of the experimental setup, the real-time PID controllers used and theoretical/conceptual issues of Arduino. PID controllers based on internal model control, minimization of the integral of time-weighted absolute error, Ziegler-Nichols, and others were tuned for setpoint and load changes and real-time runs were carried out in order to make real-time use of  control theory learned in academy. Results showed the developed platform proved to be suitable for use in experimental setups allowing users compare their ideas and expectations with the experimental evidence in a real and low-cost fashion. In addition, the instrumentation is simple to configure with acceptable level noise and particularly useful for control/automation learning with educational purposes.

Computer Intelligence in Healthcare

2013 ◽  
pp. 716-715 ◽  
Author(s):  
Pramit Ghosh ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Dipak Kumar Basu
Keyword(s):  
Embedded System ◽  
Room Temperature ◽  
Cooling System ◽  
Low Cost ◽  
Environmental Parameters ◽  
Training Data ◽  
Data Sets ◽  
Air Conditioner ◽  
Time Data ◽  
Full Speed

Low cost solutions for the development of intelligent bio-medical devices that not only assist people to live in a better way but also assist physicians for better diagnosis are presented in this chapter. Two such devices are discussed here, which are helpful for prevention and diagnosis of diseases. Statistical analysis reveals that cold and fever are the main culprits for the loss of man-hours throughout the world, and early pathological investigation can reduce the vulnerability of disease and the sick period. To reduce this cold and fever problem a household cooling system controller, which is adaptive and intelligent in nature, is designed. It is able to control the speed of a household cooling fan or an air conditioner based on the real time data, namely room temperature, humidity, and time for which system is active, which are collected from environment. To control the speed in an adaptive and intelligent manner, an associative memory neural network (Kramer) has been used. This embedded system is able to learn from training set; i.e., the user can teach the system about his/her feelings through training data sets. When the system starts up, it allows the fan to run freely at full speed, and after certain interval, it takes the environmental parameters like room temperature, humidity, and time as inputs. After that, the system takes the decision and controls the speed of the fan.

scholarly journals Model Sistem Monitoring pH dan Kekeruhan pada Akuarium Air Tawar berbasis Internet of Things

METANA ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 49-56
Author(s):  
Dista Yoel Tadeus ◽  
Khasnan Azazi ◽  
Didik Ariwibowo
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Aquatic Vegetation ◽  
Ph Value ◽  
Low Cost ◽  
Environmental Parameters ◽  
Ornamental Fish ◽  
Automation System ◽  
Water Turbidity ◽  
Air Filter

Ikan hias dan vegetasi air memiliki rentang toleransi terhadap nilai parameter lingkungan. Parameter tersebut hendaknya senantiasa diawasi demi kelangsungan hidupnya. Internet of Things (IoT) telah dimanfaatkan sebagai sistem monitoring dan otomasi parameter lingkungan ikan dan vegetasi air namun sistem ini membutuhkan biaya yang tinggi. Tujuan penelitian ini adalah mengembangkan suatu model sistem monitoring berbasis IoT berbiaya rendah untuk memberikan informasi parameter pH dan kekeruhan air setiap saat kepada pemilik ikan hias. Sistem ini dibangun menggunakan komponen opensource dan sensor berbiaya rendah. Data monitoring digunakan untuk mengaktifkan aktuator berupa filter air. Filter akan aktif apabila tingkat kekeruhan air sudah melebihi batas kekeruhan yang ditentukan. Pengujian kekeruhan air aquarium menunjukkan saat kekeruhan mencapai 3000 ntu pukul 14.12 pompa aktif dan filter bekerja sampai kekeruhan berada pada nilai 498 ntu pada pukul 17.00 dan pompa mati secara otomatis. Nilai pH dan kekeruhan air berhasil ditampilkan dengan baik di aplikasi Blynk pada ponsel. Hasil pengujian menyimpulkan bahwa sistem monitoring yang dikembangkan telah berhasil diimplementasikan dengan baik.  Ornamental fish and aquatic vegetation have a tolerance range of environmental parameter values. These parameters should always be monitored for survival. Internet of Things (IoT) has been utilized as a monitoring and automation system for environmental parameters of fish and aquatic vegetation, but this system requires high costs. The purpose of this study is to develop a low-cost IoT-based monitoring system model to provide information on pH parameters and water turbidity at any time to ornamental fish owners. This system is built using opensource components and low-cost sensors. Monitoring data is used to activate the actuator in the form of a water filter. The filter will active if the turbidity level of water has exceeded the specified turbidity limit. The aquarium water turbidity test showed that when the turbidity reached 3000 ntu at 14.12 the pump was active and the filter worked until the turbidity was at 498 ntu at 17.00 and the pump automatically shut down. The pH value and the turbidity of the water were successfully displayed in the Blynk application on the cellphone. The test results concluded that the monitoring system developed was successfully implemented. 

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