Real-Time Low-Cost Personal Monitoring for Exposure to PM2.5 among Asthmatic Children: Opportunities and Challenges

This study aims to evaluate the accuracy and effectiveness of real-time personal monitoring of exposure to PM concentrations using low-cost sensors, in comparison to conventional data collection method based on fixed stations. PM2.5 data were measured every 5 min using a low-cost sensor attached to a bag carried by 47 asthmatic children living in the Seoul Metropolitan area between November 2019 and March 2020, along with the real-time GPS location, temperature, and humidity. The mobile sensor data were then matched with station-based hourly PM2.5 data using the time and location. Despite some uncertainty and inaccuracy of the sensor data, similar temporal patterns were found between the two sources of PM2.5 data on an aggregate level. However, average PM2.5 concentrations via personal monitoring tended to be lower than those from the fixed stations, particularly when the subjects were indoors, during nighttime, and located farther from the fixed station. On an individual level, a substantial discrepancy is observed between the two PM2.5 data sources while staying indoors. This study provides guidance to policymakers and researchers on improving the feasibility of personal monitoring via low-cost mobile sensors as an alternative or supplement to the conventional station-based monitoring.

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Semantic Traffic Sensor Data: The TRAFAIR Experience

Applied Sciences ◽

10.3390/app10175882 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5882

Author(s):

Federico Desimoni ◽

Sergio Ilarri ◽

Laura Po ◽

Federica Rollo ◽

Raquel Trillo-Lado

Keyword(s):

Air Quality ◽

Real Time ◽

Smart Cities ◽

Low Cost ◽

Semantic Annotation ◽

Open Data ◽

Transportation Systems ◽

Sensor Data ◽

Time Data ◽

Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

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IOTFlood: hardware and software platform using internet of things to monitor floods in real time

Ambiente e Agua - An Interdisciplinary Journal of Applied Science ◽

10.4136/ambi-agua.2675 ◽

2021 ◽

Vol 16 (4) ◽

pp. 1

Author(s):

Francisco Vital Da Silva Júnior ◽

Mônica Ximenes Carneiro Da Cunha ◽

Marcílio Ferreira De Souza Júnior

Keyword(s):

Internet Of Things ◽

Real Time ◽

Monitoring System ◽

Research Design ◽

Open Source Software ◽

Low Cost ◽

Sensor Data ◽

Software Platform ◽

Web Interface ◽

Iot Platform

Floods are responsible for a high number of human and material losses every year. Monitoring of river levels is usually performed with radar and pre-configured sensors. However, a major flood can occur quickly. This justifies the implementation of a real-time monitoring system. This work presents a hardware and software platform that uses Internet of Things (IoTFlood) to generate flood alerts to agencies responsible for monitoring by sending automatic messages about the situation of rivers. Research design involved laboratory and field scenarios, simulating floods using mockups, and later tested on the Mundaú River, state of Alagoas, Brazil, where flooding episodes have already occurred. As a result, a low-cost, modular and scalable IoT platform was achieved, where sensor data can be accessed through a web interface or smartphone, without the need for existing infrastructure at the site where the IOTFlood solution was installed using affordable hardware, open source software and free online services for the viewing of collected data.

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Real-Time Motion Capture for a Human Body using Accelerometers

Robotica ◽

10.1017/s0263574701003319 ◽

2001 ◽

Vol 19 (6) ◽

pp. 601-610 ◽

Cited By ~ 25

Author(s):

Jihong Lee ◽

Insoo Ha

Keyword(s):

Real Time ◽

Motion Capture ◽

Human Body ◽

Degrees Of Freedom ◽

Low Cost ◽

Human Motion ◽

The Body ◽

Sensor Data ◽

Time Motion ◽

Gravity Acceleration

In this paper we propose a set of techniques for a real-time motion capture of a human body. The proposed motion capture system is based on low cost accelerometers, and is capable of identifying the body configuration by extracting gravity-related terms from the sensor data. One sensor unit is composed of 3 accelerometers arranged orthogonally to each other, and is capable of identifying 2 rotating angles of joints with 2 degrees of freedom. A geometric fusion technique is applied to cope with the uncertainty of sensor data. A practical calibration technique is also proposed to handle errors in aligning the sensing axis to the coordination axis. In the case where motion acceleration is not negligible compared with gravity acceleration, a compensation technique to extract gravity acceleration from the sensor data is proposed. Experimental results not only for individual techniques but also for human motion capturing with graphics are included.

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Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

Sensors ◽

10.3390/s21093079 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3079

Author(s):

André Glória ◽

João Cardoso ◽

Pedro Sebastião

Keyword(s):

Machine Learning ◽

Random Forest ◽

Real Time ◽

New Technologies ◽

Irrigation System ◽

Low Cost ◽

Time Of Day ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Sensors And Actuators

Presently, saving natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. On this topic, the Internet of Things has been highlighted, with these solutions being characterized by offering robustness and simplicity, while being low cost. This paper presents the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, a mobile application that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. To adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration. Of the studied algorithms (Decision Trees, Random Forest, Neural Networks, and Support Vectors Machines) the one that obtained the best results was Random Forest, presenting an accuracy of 84.6%. Besides the ML solution, a method was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.

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Real time localization solution for land vehicle application using low-cost integrated sensors with GPS

Journal of Applied Research and Technology ◽

10.22201/icat.24486736e.2020.18.4.1196 ◽

2020 ◽

Vol 18 (4) ◽

pp. 214-228

Author(s):

Abdalla Eldesoky ◽

Ahmed M. Kamel ◽

M. Elhabiby ◽

Hadia Elhennawy

Keyword(s):

Data Fusion ◽

Real Time ◽

Navigation System ◽

Unscented Kalman Filter ◽

Low Cost ◽

Sensor Data ◽

Observation Angle ◽

Integrated Sensors ◽

Land Vehicle ◽

Position Errors

The technique proposed in this research demonstrates a real time nonlinear data fusion solution based on extremely low-cost and grade inertial sensors for land vehicle navigation. Here, the utilized nonlinear multi-sensor data fusion (MSDF) is based on the combination between extremely low-cost micro electrical mechanical systems (MEMS) inertial, heading, pressure, and speed sensors in addition to satellite-based navigation system. The integrated navigation system fuses position and velocity states from the Global Positioning System (GPS), the velocity measurements from an odometer, heading angle observation from a magnetometer and navigation states from an inertial navigation system (INS). The implemented system performance is assessed through the post-processing of collected raw measurements and real time experimental work. The system that runs the real-time experiments is established on three connected platforms, two of them are based on a 32-bit ARMTM core and the third one is based 16-bit AVR ATMEL microcontroller. This microcontroller is connected to an on-board diagnostics (OBD) shield to collect the vehicle speed measurements. The raw data obtained from the integrated sensors is saved and post processed in MATLAB®. In normal conditions, the estimated position errors are reduced through the usage of INS/GPS integration with heading observation angle from a magnetometer. In GPS-denied environments, the integrated system uses the observations from INS, magnetometer in addition to the velocity from odometer to ensure a continuous and accurate navigation solution. A complementary filter (CF) is implemented to estimate and improve the pitch and roll angles calculations. In addition to that, an unscented Kalman filter (UKF) is used cascaded with the designed CF to complete the designed sensors fusion algorithm. Experimental results show that the designed MSDF can achieve a good level of accuracy and a continuous localization solution of a land vehicle in different GPS availability cases and can be implemented on the available in the market processors to be run in real time.

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Static, Portable and Smart Detection of Water Quality in Panchayath Distribution Tank

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1023.0782s519 ◽

2019 ◽

Vol 8 (2S5) ◽

pp. 103-108

Keyword(s):

Real Time ◽

Low Cost ◽

Cost Effective ◽

Daily Basis ◽

Quality Parameters ◽

Sensor Data ◽

Sensor Technology ◽

Real Time System ◽

Quality Of Water ◽

Physical And Chemical

The significant crunch in the Current world is Water pollution. It has created an abundant influence on the Environment. With the intention of the non-toxic distribution of the water and its eminence should be monitored at real time. This paper suggested the smart detection with low cost real time system which is used to monitor the quality of water through IOT(internet of things). The system entail of different sensors which are used to measure the physical and chemical parameters of the water. The quality parameters are temperature, pH, turbidity, conductivity and Total dissolved solids of the water are measured. Commercially available products capable of monitoring such parameters are usually somewhat expensive and the data’s are collected by mobile van. Using Sensor technology provides a cost-effective and pre-eminent reliable as they can provide real time output. The measured values from the sensors can be observed by the core controller. The controller was programmed to monitor the distribution tank on a daily basis to hour basis monitoring. The TIVA C series is used as a core controller. The Controller is mounted on the side of the distribution tank. Finally, the sensor data from the controller is sent to Wi-Fi module through UART protocol. Wi-fi Module is connected to a public Wi-Fi system through which data is seen by the locals who are all connected to that Wi-Fi network.

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Real-time and Low-cost IoT based farming using raspberry Pi

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v17.i1.pp197-204 ◽

2020 ◽

Vol 17 (1) ◽

pp. 197

Author(s):

Md. Wahidur Rahman ◽

Md. Elias Hossain ◽

Rahabul Islam ◽

Md. Harun Ar Rashid ◽

Md. Nur A Alam ◽

...

Keyword(s):

Real Time ◽

Low Cost ◽

Ph Sensor ◽

Sensor Data ◽

Future Application ◽

Raspberry Pi ◽

Moisture Sensor ◽

Security Issues ◽

Temperature And Humidity ◽

Level Sensor

<span>This paper reflects on the implementation of IoT enabled Farming, especially for the people needed a smart way of agriculture. This research focuses on real-time observation with efficient use of cheapest security system. The features of this research including i) Sensor data monitoring using soil moisture sensor which is responsible for measuring moisture of the filed, water level sensor which is liable for detecting flooded water, pH sensor which is accountable for measuring pH of the soil and Temperature and humidity sensor which is responsible for tracking out the present temperature and humidity in the atmosphere ii) Live monitoring of sensor’s value using cloud and a Dashboard iii) Security issues of the farming using Laser shield and IP-Camera through Wi-Fi which is conducted by android application. This paper also assures the analysis of the experimented data through various sensor’s value and gives a momentous way for future application. Result and discussion ensures the contribution in the field of Internet of things</span>

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Multisensor IoT Platform for Optimising IAQ Levels in Buildings through a Smart Ventilation System

Sustainability ◽

10.3390/su11205777 ◽

2019 ◽

Vol 11 (20) ◽

pp. 5777 ◽

Cited By ~ 7

Author(s):

Giacomo Chiesa ◽

Silvia Cesari ◽

Miguel Garcia ◽

Mohammad Issa ◽

Shuyang Li

Keyword(s):

Air Quality ◽

Indoor Air Quality ◽

Real Time ◽

Indoor Air ◽

Low Cost ◽

Ventilation System ◽

Mobile App ◽

Sensor Data ◽

Pollutant Concentrations ◽

Indoor And Outdoor

Indoor Air Quality (IAQ) issues have a direct impact on the health and comfort of building occupants. In this paper, an experimental low-cost system has been developed to address IAQ issues by using a distributed internet of things platform to control and monitor the indoor environment in building spaces while adopting a data-driven approach. The system is based on several real-time sensor data to model the indoor air quality and accurately control the ventilation system through algorithms to maintain a comfortable level of IAQ by balancing indoor and outdoor pollutant concentrations using the Indoor Air Quality Index approach. This paper describes hardware and software details of the system as well as the algorithms, models, and control strategies of the proposed solution which can be integrated in detached ventilation systems. Furthermore, a mobile app has been developed to inform, in real time, different-expertise-user profiles showing indoor and outdoor IAQ conditions. The system is implemented in a small prototype box and early-validated with different test cases considering various pollutant concentrations, reaching a Technology Readiness Level (TRL) of 3–4.

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A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation

Sensors ◽

10.3390/s19061340 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1340 ◽

Cited By ~ 5

Author(s):

Xudong Wen ◽

Chunwu Liu ◽

Zhiping Huang ◽

Shaojing Su ◽

Xiaojun Guo ◽

...

Keyword(s):

Data Processing ◽

Real Time ◽

Low Cost ◽

Attitude Estimation ◽

Sensor Data ◽

Processing Algorithm ◽

Estimation Accuracy ◽

First Order ◽

Data Processing Algorithm ◽

Adjustment Strategy

There are many algorithms that can be used to fuse sensor data. The complementary filtering algorithm has low computational complexity and good real-time performance characteristics. It is very suitable for attitude estimation of small unmanned aerial vehicles (micro-UAVs) equipped with low-cost inertial measurement units (IMUs). However, its low attitude estimation accuracy severely limits its applications. Though, many methods have been proposed by researchers to improve attitude estimation accuracy of complementary filtering algorithms, there are few studies that aim to improve it from the data processing aspect. In this paper, a real-time first-order differential data processing algorithm is proposed for gyroscope data, and an adaptive adjustment strategy is designed for the parameters in the algorithm. Besides, the differential-nonlinear complementary filtering (D-NCF) algorithm is proposed by combine the first-order differential data processing algorithm with the basic nonlinear complementary filtering (NCF) algorithm. The experimental results show that the first-order differential data processing algorithm can effectively correct the gyroscope data, and the Root Mean Square Error (RMSE) of attitude estimation of the D-NCF algorithm is smaller than when the NCF algorithm is used. The RMSE of the roll angle decreases from 1.1653 to 0.5093, that of the pitch angle decreases from 2.9638 to 1.5542, and that of the yaw angle decreases from 0.9398 to 0.6827. In general, the attitude estimation accuracy of D-NCF algorithm is higher than that of the NCF algorithm.

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