Snowflakes: Efficient schemes for source location privacy in Wireless Sensor Networks
Saginbekov S. Oryspayev D.
June 2025Elsevier B.V.
Computer Networks
2025#265
A Wireless Sensor Network (WSN) can be used in various applications such as asset monitoring, where the location privacy of the asset is of paramount importance. If such applications do not use a scheme that protects the nodes’ location privacy, an adversary may easily locate the monitored asset. In this paper, we propose two schemes that protect the location privacy of assets from a global adversary. Both schemes are adaptive, energy-efficient, and delay-efficient. The first scheme, called Snowflake, divides the nodes into two types. The first type of nodes transmit packets frequently while the second type of nodes transmit packets less frequently. This approach allows us to improve the performance of the scheme. The second scheme, called Snowflake-S, divides the network area into sectors to reduce the packet overhead further. The simulation results show that our schemes outperform an existing algorithm.
Global adversary , Source location privacy , Wireless Sensor Networks
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Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana, 010000, Kazakhstan
Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, United States
Department of Computer Science
Computational Science Initiative
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026