A multi-illusive voids approach for increasing base station anonymity

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MIVA Accepted.pdf (1.24 MB)

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https://link.springer.com/article/10.1007/s10586-022-03811-z

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https://doi.org/10.1007/s10586-022-03811-z
 http://hdl.handle.net/11603/26448

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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Author/Creator ORCID

https://orcid.org/0000-0003-3865-9217

Date

2022-11-12

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journal articles
postprints
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Citation of Original Publication

Boulaiche, M., Younis, M. A multi-illusive voids approach for increasing base station anonymity. Cluster Comput (2022). https://doi.org/10.1007/s10586-022-03811-z

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This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10586-022-03811-z
Access to this item will begin on 11/12/2023

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Abstract

In wireless sensor networks (WSNs), the base station plays a major role in processing and transmitting collected data to the command centers. Given its critical role, it is considered as the most important part in the network and hence becomes a target of an adversary attack. Although many solutions have been proposed to prevent the base station (BS) from exposing itself, the traffic pattern in the network degrades the BS location anonymity and makes it vulnerable. In this paper, we propose a Multi-Illusive Voids approach for increasing the Base Station Anonymity (MIVA). MIVA exploits the features of geographic routing to both confuse the adversary about the routing topology and to have some control over the adversary’s Belief. Specifically, MIVA forms a fake void around the BS in order to avert the adversary’s attention away from the BS vicinity and the multiple other fake voids throughout the network to confuse the adversary about a potential location for the BS. MIVA is validated through simulation and is shown to outperform prominent competing anti-traffic analysis techniques.