Work place: Department of CSE, SRM University AP, Guntur, 522502, India
E-mail: saiyasheswini.kandimalla@srmap.edu.in
Website: https://orcid.org/0009-0001-7575-8364
Research Interests:
Biography
Kandimalla Sai Yasheswini holds a B.Tech in CSE (Big Data Analytic) from SRM University, India, and an M.S. in Computer Science from the University of Illinois Chicago. Her research focuses on machine learning, artificial intelligence, natural language processing, and the Internet of Things (IoT). She has expertise in large language models (LLMs), supervised and unsupervised machine learning techniques, deep learning frameworks, and IoT platforms. Sai has contributed to advancing innovative solutions in these areas through peer-reviewed publications. She is passionate about leveraging technology to solve complex problems and is committed to impactful research that bridges AI and real-world applications.
By Raja Manjula Tejodbhav Koduru Kandimalla Sai Yasheswini Tumala Meghana Kamineni Shasank Anirban Ghosh Anuj Deshpande Sibendu Samanta
DOI: https://doi.org/10.5815/ijwmt.2026.05.21, Pub. Date: 8 Oct. 2026
This research aims to understand the impact of the network area and shape on the performance metrics used in evaluating the random walk-based source location privacy (SLP) techniques developed for WSNs. In this context, the impact of circular and square network models for different network areas, node densities, and radio ranges of sensor nodes on the performance of three popular SLP techniques is investigated. The effectiveness is assessed using performance measures from the body of available literature. It has been found that square deployment performs better for sector-based SLP protocols when network area, node density, and node radio range are held constant. However, circular networks perform better for the same protocol when the radio range is varied beyond a certain threshold while holding all other parameters constant. The trend, however, changes under comparable network settings for the non-sector-based random walk protocols considered in the current work. The results reveal a strong link between deployment geometry and routing logic, demonstrating that sector-based SLP protocols are primarily geometry-sensitive, whereas non-sector-based protocols are more sensitive to radio-range variations. A radio-range threshold of approximately 100 m was identified beyond which privacy gains saturated and energy costs increased. These findings provide practical design guidelines for geometry-aware deployment of privacy-preserving WSNs. However, the conclusions are limited to simulation-based evaluation of three random walk-based SLP protocols in circular and square network topologies, and further validation in irregular and real-world deployments is required.
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