Kuda Nageswara Rao

Work place: Department of Computer Science and Systems Engineering, Andhra University, Andhra Pradesh, India

E-mail: knrao2038@gmail.com

Website:

Research Interests: Software, Software Organization and Properties, Wireless Networks, Computer Networks

Biography

Dr. Kuda Nageswara Rao is working as a Professor at Andhra University, Visakhapatnam. He has 25 Years of Teaching, Research, and administrative experience. He received his Ph.D. in Computer Science and Engineering from JNTU Kakinada in 2010, M.Tech in Computer Science and Technology in 2001 from Andhra University, and B.E in Electronics and Communication Engineering from GITAM, Andhra University in 1998.  He has published more than 60 research papers in various international peer-reviewed journals. He has organized and attended many International and National conferences, presented papers, chaired sessions, and delivered invited talks. His area of research includes Wired and Wireless Networks, Internet Technologies, Cyber Security, Cloud Computing, IoT, and Software Engineering. Under his esteemed guidance, more than ten scholars were awarded Ph.D. degrees.

Author Articles
Reliable Data Delivery Using Fuzzy Reinforcement Learning in Wireless Sensor Networks

By Sateesh Gudla Kuda Nageswara Rao

DOI: https://doi.org/10.5815/ijcnis.2023.04.09, Pub. Date: 8 Aug. 2023

Wireless sensor networks (WSNs) has been envisioned as a potential paradigm in sensing technologies. Achieving energy efficiency in a wireless sensor network is challenging since sensor nodes have confined energy. Due to the multi-hop communication, sensor nodes spend much energy re-transmitting dropped packets. Packet loss may be minimized by finding efficient routing paths. In this research, a routing using fuzzy logic and reinforcement learning procedure is designed for WSNs to determine energy-efficient paths; to achieve reliable data delivery. Using the node’s characteristics, the reward is determined via fuzzy logic. For this paper, we employ reinforcement learning to improve the rewards, computed by considering the quality of the link, available free buffer of node, and residual energy. Further, simulation efforts have been made to illustrate the proposed mechanism’s efficacy in energy consumption, delivery delay of the packets, number of transmissions and lifespan.

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