Ramesh Vatambeti

Work place: School of Computer Science and Engineering, VIT-AP University, Vijayawada-522237, India

E-mail: v2ramesh634@gmail.com

Website: https://orcid.org/0000-0002-2611-4925

Research Interests: Internet of Things, Computer Networks

Biography

Dr. Ramesh Vatambeti received his B. Tech from Sri Venkateswara University, Tirupati in Computer Science and Engineering and M. Tech in IT and PhD in CSE from Sathyabama University, Chennai. He has around 18 years of teaching experience from reputed Engineering Institutions. He works as Professor in the School of Computer Science and Engineering at VIT-AP University, Amaravati, India. He has published 3 books, 5 book chapters and more than 70 papers in refereed journals and conference proceedings. He is the reviewer for several refereed International Journals and acted as Session Chair and technical committee member for several International Conferences held in India and abroad. He is guiding 6 scholars for the award of Ph.D. His research interests include Computer Networks, Mobile Ad-Hoc and Sensor Networks and Internet of Things.

Author Articles
Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN

By Ramesh Vatambeti Vijay Kumar Damera Karthikeyan H. Manohar M. Sharon Roji Priya C. M. S. Mekala

DOI: https://doi.org/10.5815/ijcnis.2023.06.01, Pub. Date: 8 Dec. 2023

Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.

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Energy Management and Network Traffic Avoidance Using GAODM and E-AODV Protocols in Mobile Ad-Hoc Network

By Ramesh Vatambeti Nrusingha Charan Pradhan E. Sandhya Surendra Reddy Vinta V. Anbarasu K. Venkateswara Rao

DOI: https://doi.org/10.5815/ijcnis.2023.03.06, Pub. Date: 8 Jun. 2023

Because of the mobility of its nodes, MANET plays a significant role in mobile communication. As a result, network infrastructure is frequently changed, resulting in data loss and communication overheads. Despite this, the large packet size causes network congestion or traffic. The difficult task is efficient routing through a dynamic network. For node generation and energy management, the proposed approach in this paper employs GAODM (Geography-based Ad-hoc On Demand disjoint multipath) and E-AODM (Energy Ad-hoc On Demand Vector routing). The proposed GAODM routing protocol reduces congestion using Spider Monkey (SM) Optimization. The E- AODM protocol assesses the energy management solution based on parameters such as delay, energy consumption, routing overhead, and node energy. By choosing the best path through the network, the proposed protocol's effectiveness is increased. The proposed protocol reduces routing overload, delay, and congestion. The simulated results show that increasing the number of packets transmitted in the network using the proposed GAODM and E-AODM routing protocols over the existing protocols on NS 2 reduces node energy and, as a result, overload and delay.

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