Prasanna Kumar G.

Work place: Dept. of Computer Science & Engineering, SJCE Mysore, 570006, India



Research Interests: Online social communication, Mobile Learning, Network Security, Network Architecture


Prasanna Kumar G. received his Bachelor of Engineering in Computer Science and Engineering from Coorg Institute of Technology, Ponnampet, affiliated to Visvesvaraya Technological University, Belgaum, Karnataka, and obtained his Master’s Degree in the area of Networking and Internet Engineering from Sri Jayachamarajendra College of Engineering, Mysore, autonomous under Visvesvaraya Technological University, Belgaum, Karnataka. He is pursuing his Ph.D. under the guidance of Dr. Shankaraiah in the area of Ubiquitous Networks. His research interests include Computer Network, Mobile Communication and Machine Learning. At present he is working as Assistant Professor in the Department of Information science and Engineering at NIE Institute of Technology, Mysuru, Karnataka, India.

Author Articles
A Novel Approach by Integrating Dynamic Network Selection and Security Measures to improve Seamless Connectivity in Ubiquitous Networks

By Prasanna Kumar G. Shankaraiah N. Rajashekar M B Sudeep J Shruthi B S Darshini Y Manasa K B

DOI:, Pub. Date: 8 Feb. 2024

Researchers have developed an innovative approach to ensure seamless connectivity in ubiquitous networks with limited or irregular network coverage. The proposed method leverages advanced network technologies and protocols to seamlessly establish and maintain network connections across various environments. It integrates multiple wireless communication technologies and dynamic network selection algorithms, overcoming issues like poor reliability, limited scalability, and security problems. Compared to existing solutions, the method exhibits improved connection handover efficiency, network throughput, and end-to-end delay. Considering user mobility, network availability, and quality of service needs, it makes informed decisions about the most suitable network connections. The proposed method is expected to significantly impact the development of future ubiquitous networking solutions.

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An Efficient IoT-based Ubiquitous Networking Service for Smart Cities Using Machine Learning Based Regression Algorithm

By Prasanna Kumar G. Shankaraiah N.

DOI:, Pub. Date: 8 Jun. 2023

In recent days, the smart city project has been emerging concept all over the world. In this process, the proper communication between the sensors and the smart devices, and identification of optimal path between sensors and mutation sensors in large geographical area is very difficult. The main objective has been considered to overcome the drawbacks as mentioned above. The proposed algorithm is efficient to provide integrated communication of IoT-based ubiquitous networking (UBN) devices to improve in large geographically distributed area. The data storage capacity and accuracy of sensors and smart devices are enhanced using the proposed algorithm. The communication latency and data pre-processing of IoT-based UBN nodes deployed in smart cities are reduced. The proposed algorithm also analyses the performance of IoT-based UBN nodes by considering geographical testbeds that represent a smart city scenario. The analysis and comparison are carried out by considering the heuristic parameters. The proposed algorithm will also optimize the communication latency and data pre-processing time by analyzing various sensitivity levels by considering the heuristic parameters in different probability of nodes in smart cities. The proposed IoT-based UBN computing devices improve the objective function due to proper integrated communication between the sensors using a machine learning based regression algorithm. The proposed algorithm also identifies the probability sensitivity of optimal path between smart devices in a smart city thereby enhancing the connectivity of mutated sensor nodes. The proposed algorithm also enhances the probability of smart device connectivity to improve the accuracy, flexibility and large geographical coverage using machine learning based regression algorithm.

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