Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN

Saritha; Sarasvathi, V.

doi:10.2478/cait-2023-0046

Abstract

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.

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Reliability Analysis of an IoT-Based Air Pollution Monitoring System Using Machine Learning Algorithm-BDBN

Abstract

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