Shashi Shekhar Kumar1 and Upendra Pratap Singh2*
1Department of Information Technology, IIIT Allahabad, Prayagraj, India
2Department of Computer Science and Engineering, SOA University, Bhubaneshwar, India
*Corresponding Author: Upendra Pratap Singh, Department of Computer Science and Engineering, SOA University, Bhubaneshwar, India.
Received: May 16, 2022; Published: July 18, 2022
The IoT has emerged as one of the fastest-growing areas of research. Community of IoT is exploring this area of study to enhance innovative building features for reliable comfort in every prospect. One of the intelligent building features is occupancy detection, which can detect the Occupancy of a particular room or premises in real-time. Different IoT sensors implemented in an intelligent building can generate enormous data. For simulation purposes, we have used python along with apache spark framework, which will analyze the data generated and consequently, we can realize the other features like humidity, Co2 level of the room. The key contribution of this paper is to enhance the accuracy of the machine learning model for better occupancy prediction. With machine learning, the approach building feature can adjust automatically.
Keywords: Smart Building; Big Data; Occupancy Detection; Cyber-Physical System; Internet of Things
Citation: Shashi Shekhar Kumar and Upendra Pratap Singh. “A Machine Learning Approach for Occupancy Detection in Smart Building". Acta Scientific Computer Sciences 4.8 (2022): 78-81.
Copyright: © 2022 Shashi Shekhar Kumar and Upendra Pratap Singh. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.