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Secure Localization in Wireless Sensor Networks

dc.contributor.advisorHogrefe, Dieter Prof. Dr.
dc.contributor.authorBochem, Arne
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.titleSecure Localization in Wireless Sensor Networksde
dc.contributor.refereeHogrefe, Dieter Prof. Dr.
dc.description.abstractengWith the growing popularity of the Internet of Things, Wireless Sensor Networks also only grow more and more common in various different forms. However, sensor data is often only useful in connection with information about where it comes from. For this reason, localization schemes that allow sensor nodes to localize their positions are a very active field of research. As schemes are refined, localization results grow increasingly more accurate, but it also becomes more and more important to make localization approaches more robust against malfunctioning or malicious nodes in the network, as well as network scale attacks. This thesis presents two approaches, Unchained and Rechained, to monetarily disincentivize the creation of Sybil identities in decentralized networks, mitigating a common class of network level attacks against localization schemes. Furthermore, Robustness Enhanced Sensor Assisted Monte Carlo Localization (RESA-MCL) is introduced, evaluated and compared against previous comparable schemes. Evaluation is performed in simulations without attacks and under three different attack models that are introduced for the application field of Wireless Sensor Networks. RESA-MCL outperforms other approaches both without and with attacks and performs well in both low and high anchor density scenarios (e.g. a localization error of 0.5 is reached at an anchor density of ~0.33), reaching a localization error up to 48% lower than that of a recent comparable approach at a similar anchor density. It is shown to be much more robust than other approaches under attacks while computational complexity is barely
dc.contributor.coRefereeFu, Xiaoming Prof. Dr.
dc.subject.engWireless Sensor Networksde
dc.subject.engMonte Carlo Localizationde
dc.subject.engInternet of Thingsde
dc.affiliation.instituteFakultät für Mathematik und Informatikde
dc.subject.gokfullInformatik (PPN619939052)de

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