IoT sensor classification

Keywords: IoT, sensor

Background

IoT sensors play a crucial role in the working process of the machine and some of the most important features of IoT are that it helps in developing intelligent decision-making and makes the machines work automatically according to the given commands. This study will be promoting the use of Artificial Neural Networks (ANNs) as a powerful tool for IOT sensor classification. ANN has the capability to work on complex datasets as there will be many nonlinear relationships frequently present in the data. It is possible to predict the working status of the IoT sensors and it will automatically learn the features related to the sensor data, enabling effective classification. Experimental evaluations were conducted using real-world IoT sensor datasets, demonstrating the efficiency of the ANN model in accurately classifying sensor data with high accuracy and robustness. The results indicate that ANNs offer a promising approach for IoT sensor classification, facilitating efficient data processing, decision-making, and automation in IoT applications across various industries. Overall, after applying the different types of activation functions in the hidden layers, ANN architecture has obtained 99% accuracy.

Aim & Objectives

Aim
To Classify the IoT Sensor using DL Techniques
Objective
Develop an ANN model to classify IoT sensor data, enabling accurate recognition and utilization of sensor inputs.
Create an ANN system that analyzes sensor readings to identify different environmental conditions or events.
Evaluate the ANN's classification performance, contributing to efficient decision-making and automation in IoT applications.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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