Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN
Transmission of information is an essential component in an IoT device for sending, receiving, and collecting data. The Smart devices in IoT architecture are designed as physical devices linked with computing resources that can connect and communicate with another smart device through any medium and...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-11-01
|
| Series: | Cybernetics and Information Technologies |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/cait-2023-0046 |
| _version_ | 1797405097126264832 |
|---|---|
| author | Saritha Sarasvathi V. |
| author_facet | Saritha Sarasvathi V. |
| author_sort | Saritha |
| collection | DOAJ |
| description | Transmission of information is an essential component in an IoT device for sending, receiving, and collecting data. The Smart devices in IoT architecture are designed as physical devices linked with computing resources that can connect and communicate with another smart device through any medium and protocol. Communication among various smart devices is a challenging task to exchange information and to guarantee the information reaches the destination entirely in real-time in the same order as sent without any data loss. Thus, this article proposes the novel Bat-based Deep Belief Neural framework (BDBN) method for the air pollution monitoring scheme. The reliability of the proposed system has been tested under the error condition in the transport layer and is validated with the conventional methods in terms of Accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson correlation coefficient (r), Coefficient of determination (R2) and Error rate. |
| first_indexed | 2024-03-09T03:04:45Z |
| format | Article |
| id | doaj.art-2fb7bbd5361e4ef6860ba6fbb87d4956 |
| institution | Directory Open Access Journal |
| issn | 1314-4081 |
| language | English |
| last_indexed | 2024-03-09T03:04:45Z |
| publishDate | 2023-11-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Cybernetics and Information Technologies |
| spelling | doaj.art-2fb7bbd5361e4ef6860ba6fbb87d49562023-12-04T08:03:39ZengSciendoCybernetics and Information Technologies1314-40812023-11-0123423325010.2478/cait-2023-0046Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBNSaritha0Sarasvathi V.11Department of Computer Science and Engineering, PESIT-Bangalore South Campus, Bangalore and Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India1Department of Computer Science and Engineering, PESIT-Bangalore South Campus, Bangalore and Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, IndiaTransmission of information is an essential component in an IoT device for sending, receiving, and collecting data. The Smart devices in IoT architecture are designed as physical devices linked with computing resources that can connect and communicate with another smart device through any medium and protocol. Communication among various smart devices is a challenging task to exchange information and to guarantee the information reaches the destination entirely in real-time in the same order as sent without any data loss. Thus, this article proposes the novel Bat-based Deep Belief Neural framework (BDBN) method for the air pollution monitoring scheme. The reliability of the proposed system has been tested under the error condition in the transport layer and is validated with the conventional methods in terms of Accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson correlation coefficient (r), Coefficient of determination (R2) and Error rate.https://doi.org/10.2478/cait-2023-0046air quality monitoringmetaheuristic optimizationmachine learninginternet of thingspollution monitoring schemecloud storage |
| spellingShingle | Saritha Sarasvathi V. Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN Cybernetics and Information Technologies air quality monitoring metaheuristic optimization machine learning internet of things pollution monitoring scheme cloud storage |
| title | Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN |
| title_full | Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN |
| title_fullStr | Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN |
| title_full_unstemmed | Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN |
| title_short | Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN |
| title_sort | reliability analysis of an iot based air pollution monitoring system using machine learning algorithm bdbn |
| topic | air quality monitoring metaheuristic optimization machine learning internet of things pollution monitoring scheme cloud storage |
| url | https://doi.org/10.2478/cait-2023-0046 |
| work_keys_str_mv | AT saritha reliabilityanalysisofaniotbasedairpollutionmonitoringsystemusingmachinelearningalgorithmbdbn AT sarasvathiv reliabilityanalysisofaniotbasedairpollutionmonitoringsystemusingmachinelearningalgorithmbdbn |