IRO Journals

Journal of IoT in Social, Mobile, Analytics, and Cloud

Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3

Design of Deep Learning Algorithm for IoT Application by Image based Recognition
Volume-3 | Issue-3

Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3

Health Record Management System – A Web-based Application
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A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3

IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2

Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation
Volume-3 | Issue-3

Review of Internet of Wearable Things and Healthcare based Computational Devices
Volume-3 | Issue-3

Stock Index Prediction with Financial News Sentiments and Technical Indicators
Volume-4 | Issue-3

Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs
Volume-3 | Issue-3

Suspicious Human Activity Detection System
Volume-2 | Issue-4

ROBOT ASSISTED SENSING, CONTROL AND MANUFACTURE IN AUTOMOBILE INDUSTRY
Volume-1 | Issue-3

EFFICIENT RESOURCE ALLOCATION AND QOS ENHANCEMENTS OF IOT WITH FOG NETWORK
Volume-1 | Issue-2

Live Streaming Architectures for Video Data - A Review
Volume-2 | Issue-4

IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2

Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3

A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3

IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS
Volume-2 | Issue-1

Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3

Hybrid Intrusion Detection System for Internet of Things (IoT)
Volume-2 | Issue-4

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Volume - 2 | Issue - 1 | march 2020

IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS
Pages: 13-25
Published
16 March, 2020
Abstract

Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. In order to overcome these issues an IoT based air and sound pollution monitoring system is designed. To design this monitoring system, machine learning algorithms K-NN and Naive Bayes are used. K-Nearest Neighbour and Naive Bayes are machine learning algorithms used to predict the status of pollution present in the environment. In this system, analog to digital converter, global service mobile communication, temperature sensor, humidity sensor, carbon monoxide and sound sensors are interfaced with raspberry pi using serial cable. The sensor data is uploaded in thinkspeak (IoT) and webpage. This data is compared with the trained data to check accuracy. To calculate the accuracy of both algorithms, Python code is developed using python software tool.

Keywords

IoT Temperature Humidity Carbon Monoxide Sound Raspberry Pi

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