IOT BASED AIR QUALITY MONITORING USING DENSENET IN URBAN AREAS
Journal Title: International Journal of Data Science and Artificial Intelligence - Year 2024, Vol 2, Issue 04
Abstract
– Internet of Things is being used more and more in the control and monitoring of air quality. Real-time data regarding air pollutants and other environmental parameters can be gathered by deploying IoT devices with sensors and connectivity capabilities. Rapid urbanization and industry cause increasingly serious problems with air quality. A significant challenge in the current air quality monitoring system is its limited spatial coverage and accuracy. In this paper, a novel air quality monitoring using IoT is proposed to monitor the quality of the air efficiently in real time. Sensors are placed in the various traffic system to collect environmental data and processed it in Real Time Data Analytics Module (RTDM). DenseNet is used to predict the quality of air and classified into three classes namely pure, impure, and normal. The efficacy of the proposed technique has been evaluated using assessment actions such as accuracy, time efficiency, precision, F1 score, RMSE, MAPE, and MAE. By the comparison analysis, the proposed technique’s accuracy rate is 10.08%, 17.64%, and 34.34% higher than the existing Ide Air, SMOTEDNN, and ETAPM-AIT techniques respectively.
Authors and Affiliations
M. Devaki, Jeyaraman Sathiamoorthy and M. Usha
REAL TIME REMOTE MONITORING VIA HORSE HEAD OPTIMIZATION DEEP LEARNING NETWORK
Over the past few decades, IoT has become indispensable in many industries. More people can now get healthcare and their general health can be improved thanks to recent developments in the healthcare sector. Predictive a...
IOT BASED AIR QUALITY MONITORING USING DENSENET IN URBAN AREAS
– Internet of Things is being used more and more in the control and monitoring of air quality. Real-time data regarding air pollutants and other environmental parameters can be gathered by deploying IoT devices with sens...
SAFE-ACID: A NOVEL SECURITY ASSESSMENT FRAMEWORK FOR IOT INTRUSION DETECTION VIA DEEP LEARNING
Internet of Things (IoT) intrusion detection is crucial for ensuring the security of interconnected devices in our digital world. With diverse devices communicating in complex networks, IoT environments face vulnerabilit...
CHICKEN SWARM OPTIMIZATION BASED ENSEMBLED LEARNING CLASSIFIER FOR BLACK HOLE ATTACK IN WIRELESS SENSOR NETWORK
Wireless Sensor Networks (WSNs) are an inevitable technology prevalently used in various critical and remote monitoring applications. The security of WSNs is compromised by various attacks in wireless mediums. Even thoug...
DYNAMIC LOAD BALANCING IN CLOUD COMPUTING USING HYBRID KOOKABURRA-PELICAN OPTIMIZATION ALGORITHMS
Cloud Computing (CC) technology facilitates virtualized computer resources to users via service providers. Load balancing assumes a critical role in distributing dynamic workloads across cloud systems, ensuring equitable...