Work place: Department of CSE, SRM University AP, Guntur, 522502, India
E-mail: meghana.rangasai@srmap.edu.in
Website: https://orcid.org/0009-0000-0975-5977
Research Interests:
Biography
Tumala Meghana has completed her master’s degree in computer science at George Mason University, USA (May 2025) and holds a Bachelor of Technology in Computer Science with a specialization in AIML from SRM University AP (May 2023). She is currently working as an AI Engineer Intern at Hexaware Technologies, where she contributes to building enterprise-grade AI solutions. Her areas of interest include cloud computing, AI/ML, NLP, software architecture, and system reliability, with a strong focus on designing secure and scalable systems. More recently, she has been actively working on and exploring Generative AI, Agentic AI, and RAG techniques. In the past, she has also worked on developing privacy-preserving solutions for Wireless Sensor Networks (WSN).
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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