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On the interaction between localization and location verification for wireless sensor networks
Affiliation:1. Dept. of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32603, USA;2. Juniper Networks, Inc. Sunnyvale, CA 94089, USA;3. Dept. of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32603, USA;1. IBM Hursley Labs, UK;2. Ozyegin University, Turkey;3. IBM Research, USA;4. City University, London, UK;1. Department of Computer Science Engineering, Sona College of Technology, Salem, 636005, Tamilnadu, India;2. School of Information Technology and Engineering, VIT University, Vellore, 632014, Tamilnadu, India;3. Department of Electrical and Communication Engineering, The PNG University of Technology, Lae, Papua New Guinea;4. Department of Information Technology, VNR Vignana Jyothi Institute of Engineering & Technology, India;5. Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia
Abstract:Secure wireless sensor networks (WSNs) must be able to associate a set of reported data with a valid location. Many algorithms exist for the localization service that determines a WSN node’s location, and current research is developing for location verification, where the network must determine whether or not a node’s claimed location is valid (or invalid). However, the interaction of these two services creates another challenge, since there is no method to distinguish between benign errors, e.g., errors that are inherent to the localization technique, and malicious errors, e.g., errors due to a node’s deceptive location report. In this paper, we study the problem of inherent localization errors and their impact on the location verification service. We propose a localization and location verification (LLV) server model, and define categories of LLV schemes for discrete and continuous resolution. We then designate two metrics to measure the impact of inherent localization errors—the probability of verification (for the discrete location verification schemes) and the CDF of the deviation distance (for the continuous location verification schemes)—to analyze the performance of each LLV category. Numerical results show that a proper tuning mechanism is needed to tolerate even small inherited estimation errors, otherwise the location verification can result in the rejection of almost all nodes. In addition, we propose several location verification feedback (LV-FEED) algorithms to improve the localization accuracy. Analysis of these algorithms shows that a significant improvement in localization accuracy can be accomplished in a few iterations of executing the location verification feedback schemes.
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