K.E. Srinivasa Desikan

Scopus Publications

VMD-IMF Enhanced Hyper Graph Attention Module Based Reinforcement Learning for Portfolio Optimization
Venkata Siva Neeli, K. E. Srinivasa Desikan
Lecture Notes in Computer Science, 2026
Bridging the Trust Gap: Leveraging Explainable AI for Personalized E-Commerce Recommendations
Hari Sai Ganesh Sanjammagari, Gumma Sri Sougandhika, K. E. Srinivasa Desikan
Lecture Notes in Computer Science, 2026
SYNCAD: Synchronised Yields from Narrative Cross Modal Audio and Data
Aditya Vardhan Madivada, Madhav Kartheek Bhumireddi, K. E. Srinivasa Desikan
Lecture Notes in Computer Science, 2026
Bio-inspired Denoising of Multimedia Content Using Diffusion Models: A Comparative Study
Aditya Vardhan Madivada, Madhav Kartheek Bhumireddi, K. E. Srinivasa Desikan
Lecture Notes on Data Engineering and Communications Technologies, 2025
Hydro-Net: Integrating AI and Satellite Imagery for Precision Canal Building
Shivamani Gangarapu, G. Sai Surya Teja, K. E. Srinivasa Desikan
Lecture Notes on Data Engineering and Communications Technologies, 2025
PLAN SMART: A Tourism Plan with Efficient Minimal Cost
Jalapati Sravan Kumar, K. Yasasvi Jayavanth, K. E. Srinivasa Desikan
Lecture Notes on Data Engineering and Communications Technologies, 2025
CARL: Efficient Deep Reinforcement Learning via Contrastive Replay
Jalapati Sravan Kumar, Banavathu Gopi, K. E. Srinivasa Desikan
Lecture Notes on Data Engineering and Communications Technologies, 2025
MARLPPO: Multi-agent Drone Surveillance Using Deep Reinforcement Learning in Urban Aerial Mobility
Gopi Banavathu, Viswesh Nanapu, K. E. Srinivasa Desikan
Lecture Notes on Data Engineering and Communications Technologies, 2025
CAViaR Archery Optimization Algorithm based Cooperative Spectrum Sensing in Cognitive Radio Network
D. Raghunatha Rao, K. E. Srinivasa Desikan, J. Krishnaiah
Proceedings of the 2024 International Conference on Advancement in Renewable Energy and Intelligent Systems Areis 2024, 2024
Due to the advancement of wireless Communication Cooperative Spectrum Sensing (CSS) is a key aspect of Cognitive Radio Networks (CRNs). The CRN has been utilized as a viable model, which offers efficient spectrum utilization, the spectrum sensor is employed for controlling the functions of the Primary User (PU) and avoiding collisions with the Secondary User (SU). As a result, this work proposes the Conditional Autoregressive Value at Risk (CA ViaR)-Archery Algorithm (AA) named CA ViaR-AA for CSS. In the initial stage, the system model with the PU and SUs are developed. The test statistics for the SU are estimated, and every SU's signal is fused using signal elements like eigenstatistics, matching filters, wavelet transform, and signal energy. The proposed CA ViaR-AA is employed for weight detection to make the decision. Furthermore, performance computing metrics like the probability of false alarm, sensing time, and probability of detection are employed for validating the CA ViaR-AA that yields the optimal results of 0.005, 209.4 Sec, and 0.998.
Decoding the Interplay Between Latency, Reliability, Cost, and Energy While Provisioning Resources in Fog-Computing-Enabled IoT Networks
K E Srinivasa Desikan, Vijeth J Kotagi, C Siva Ram Murthy
IEEE Internet of Things Journal, 2023
By bringing the processing and storage capabilities of the cloud closer to the end devices, fog computing (FC) enhances the Quality of Service (QoS) for latency-critical Internet of Things (IoT) applications, such as autonomous driving, haptics, and augmented reality (AR). To facilitate the processing and storage of data packets, the fog nodes in the underlying FC-enabled IoT network (FC-IoTN) are to be provisioned with storage and processing resources. Existing resource provisioning solutions focus mainly on latency sensitivity and cost efficiency. They also operate under the assumption that these fog nodes are completely reliable and energy efficient. In reality, this is not true. The fog nodes are not 100% reliable. Neither are they energy efficient. In this study, we propose a novel resource provisioning framework for the fog nodes that considers reliability and energy efficiency, in addition to latency sensitivity and cost efficiency. We first give an analytical framework to model the failures and recoveries in a fog node and use this modeling to provision resources in the fog nodes such that the resultant resource provisioning is optimal in terms of cost and energy consumption. Further, to understand the effect of latency, reliability, cost, and energy on resource provisioning, we analyze and decode the interplay between these factors during resource provisioning in fog nodes. We finally show the efficacy of our approach over the scenario that does not consider reliability and energy efficiency while provisioning resources. Without affecting the latency sensitivity and reliability of the system, our framework achieves an enhancement of 35%, and 37% in terms of cost and energy consumption, respectively, over a nonoptimized framework.
A Novel Matching Theory-Based Data Offloading Framework for a Fog Network with Selfish and Rational Nodes
K. E. Srinivasa Desikan, Vijeth J. Kotagi, C. Siva Ram Murthy
IEEE Networking Letters, 2021
Topology Control in Fog Computing Enabled IoT Networks for Smart Cities
K.E. Srinivasa Desikan, Vijeth J. Kotagi, C. Siva Ram Murthy
Computer Networks, 2020
Smart at Right Price: A Cost Efficient Topology Construction for Fog Computing Enabled IoT Networks in Smart Cities
K E Srinivasa Desikan, Vijeth J Kotagi, C Siva Ram Murthy
IEEE International Symposium on Personal Indoor and Mobile Radio Communications PIMRC, 2018
A novel distributed latency-Aware data processing in fog computing-enabled iot networks
Thomas Moscibroda, Rogert Wattenhofer, Aaron Zollinger
Dipwn 2017 Proceedings of the 2017 ACM Workshop on Distributed Information Processing in Wireless Networks, 2017

K.E. Srinivasa Desikan

RESEARCH INTERESTS

Scopus Publications