1Department of Biomedical Engineering, KPR Institute of Engineering and Technology, Arasur, Tamil Nadu, India.
2Department of Computer Science, College of Science & Arts Tanumah, King Khalid University, Abha, Saudi Arabia.
3Department of ECE, Signal Processing Lab, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, Tamil Nadu, India.
4Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
5Department of Electromechanical Engineering, Faculty of Manufacturing, Institute of Technology, Hawassa University, Hawassa, Ethiopia.
6Department of Chemical Engineering, Lovely Professional University, Punjab, India
Corresponding author email: email@example.com
Article Publishing History
Accepted After Revision: 24/07/2021
In this paper, we aim here at integrating disparate sources of information, particularly the deep neural network. The Deep Neural Network structure can intelligently track the traffic conditions in order to fulfil this task by using open data sources, whilst using sensors run by end users to provide helpful resources. We also recommended a multi-faceted architecture which permits the coexistence of different data collection methods. We have constructed a generic data structure in a relation database that gives heterogeneous data a specific interpretability. In order to make full use of the IoT potential for customers to adapt to their behaviour, data can also be offered to several suppliers. Any adjustment in our system or structures depending on the same idea would therefore encourage co-operation and convergence. It should be reminded that confidential information is generated by some users and not to be shared.
Improved Monitoring, Environment, Internet of Things, Smart Cities