A Low-cost IoT System for Environmental Pollution Monitoring in Developing Countries

Flores-Cortez, Omar Otoniel; Cortez, Ronny Adalberto; Rosa, Veronica Idalia

doi:10.23919/mixdes.2019.8787056

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…It keeps track of the differences between two successive time series [10], regardless of whether or not their recommendations are taken into consideration [30][31][32][33][34][35][36][37][38]. Specifically, it demonstrates that the standard and ultimate differences between a prediction and actual gathered data are the same rolling average [11] and the relative disparities in the equations for the MAE computation are represented in Equation (11).…”

Section: Linear Regression Modelmentioning

confidence: 99%

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Kshirsagar

Manoharan²,

Shitharth

et al. 2022

Electronics

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Due to air pollution, pollutants that harm humans and other species, as well as the environment and natural resources, can be detected in the atmosphere. In real-world applications, the following impurities that are caused due to smog, nicotine, bacteria, yeast, biogas, and carbon dioxide occur uninterruptedly and give rise to unavoidable pollutants. Weather, transportation, and the combustion of fossil fuels are all factors that contribute to air pollution. Uncontrolled fire in parts of grasslands and unmanaged construction projects are two factors that contribute to air pollution. The challenge of assessing contaminated air is critical. Machine learning algorithms are used to forecast the surroundings if any pollution level exceeds the corresponding limit. As a result, in the proposed method air pollution levels are predicted using a machine learning technique where a computer-aided procedure is employed in the process of developing technological aspects to estimate harmful element levels with 99.99% accuracy. Some of the models used to enhance forecasts are Mean Square Error (MSE), Coefficient of Determination Error (CDE), and R Square Error (RSE).

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Section: Linear Regression Modelmentioning

confidence: 99%

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Kshirsagar

Manoharan²,

Shitharth

et al. 2022

Electronics

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“…The development of internet of things (IoT) devices has grown widely and rapidly. There are also various communication media used for the IoT system, including wired ethernet [1][2][3], Wi-Fi [4][5][6][7][8], and cellular communication [9][10][11]. These communication media are used in the client-server network model.…”

Section: Introductionmentioning

confidence: 99%

Securing sensor data transmission with ethernet elliptic curve cryptography secure socket layer on STM32F103 device

Seniman

Siregar

Pelle

et al. 2021

IJEECS

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Currently there is no method, feature, or ability in securing data transmission in microcontroller systems and applications with client-server scheme communication, while major modern computer systems using secure socket layer (SSL) for establishing secure communication. However, ESP espressif based microcontroller has supported SSL communication to secure data transmission, but only works on the Wi-Fi network. A single-board computer based embedded system has fully supported SSL communication, but it costs a very high price. On the other hand, STM32F103 microcontrollers with a very affordable price even cheaper than the Arduino board has the opportunity to build secure data communication using SSL protocol based on MbedTLS library. In addition to wiznet W5100/W5500 ethernet shield, an STM32F103 SSL client device has been successfully built in this study. The SSL client device supports ECDHE ECDHA AES128 CBC SHA256 SSL cipher suite. The Apache web server must also be configured to support this cipher suite by generating OpenSSL ECC (elliptic curve cryptography) certificate. The system was tested with the LM35 analog temperature sensor, and as a result, the STM32F103 SSL client has successfully secured the data transmission to the Apache SSL web server. The communication time was 3 seconds for the first connection and 42 ms for the next data transmission.

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A Power Efficient IoT Edge Computing Solution for Cooking Oil Recycling

Gomes

Melo

Rodrigues

et al. 2020

Trends and Innovations in Information Systems and Technologies

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A Low-cost IoT System for Environmental Pollution Monitoring in Developing Countries

Cited by 8 publications

References 6 publications

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Securing sensor data transmission with ethernet elliptic curve cryptography secure socket layer on STM32F103 device

A Power Efficient IoT Edge Computing Solution for Cooking Oil Recycling

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