Secure Location Verification Using Simultaneous Multilateration

Chiang, Jerry T.; Haas, J. H. De; Choi, Jaepil; Hu, Yih-Chun

doi:10.1109/twc.2011.120911.101147

Cited by 34 publications

(37 citation statements)

References 6 publications

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“…Even though the secure localization protects the location estimation process from several attacks, the location estimation can still go wrong in some situations, e.g., compromised sensors report a false location intentionally. Therefore, many researchers have worked on the location verification in WSNs [10,11,12,13,14,15,16,17,18,19,20,21]. Location verification schemes can be categorized as in-region and on-spot verification [15].…”

Section: Related Workmentioning

confidence: 99%

“…For example, if the location of a sensor is suspicious, a sensor that is called the verifier collects the necessary information and performs location verification as shown in Figure 1b. This location verification problem can be formulated in many different mathematical ways by reflecting the characteristics of the algorithms [10,11,12,13,14,15,16,17,18,19,20,21]. …”

Section: Introductionmentioning

confidence: 99%

“…On-spot verification is to verify whether the true location of a sensor is the same as its claimed location, and in-region verification is to verify whether a sensor is within a certain region. In each category, several location verification schemes have been proposed to check the reported location of sensors with special requirements [10,11,12,13,14,15,16,17,18,19,20,21]. The special requirements, such as special hardware, the active participation of neighbors, a dedicated verifier and a trusted third party, impose a burden on the sensors in terms of hardware cost, communication overhead and computation overhead.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Location Verification Scheme for Static Wireless Sensor Networks

Kim

Song

2017

Sensors

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Abstract:In wireless sensor networks (WSNs), the accuracy of location information is vital to support many interesting applications. Unfortunately, sensors have difficulty in estimating their location when malicious sensors attack the location estimation process. Even though secure localization schemes have been proposed to protect location estimation process from attacks, they are not enough to eliminate the wrong location estimations in some situations. The location verification can be the solution to the situations or be the second-line defense. The problem of most of the location verifications is the explicit involvement of many sensors in the verification process and requirements, such as special hardware, a dedicated verifier and the trusted third party, which causes more communication and computation overhead. In this paper, we propose an efficient location verification scheme for static WSN called mutually-shared region-based location verification (MSRLV), which reduces those overheads by utilizing the implicit involvement of sensors and eliminating several requirements. In order to achieve this, we use the mutually-shared region between location claimant and verifier for the location verification. The analysis shows that MSRLV reduces communication overhead by 77% and computation overhead by 92% on average, when compared with the other location verification schemes, in a single sensor verification. In addition, simulation results for the verification of the whole network show that MSRLV can detect the malicious sensors by over 90% when sensors in the network have five or more neighbors.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Location Verification Scheme for Static Wireless Sensor Networks

Kim

Song

2017

Sensors

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“…Likewise, [10] proposes a method to check if the target lies within a claimed region and whether the claimed location exceeds a reasonable bound. Distance bounding protocols (e.g., [15]) have also been proposed to verify that a target is located within a geometric region from the anchors. This is achieved by rapidly exchanging of messages based on random nonces to bound the distances between the target and the anchors.…”

Section: A Related Workmentioning

confidence: 99%

Geo-Spatial Location Spoofing Detection for Internet of Things

Koh

Nevat

Leong

et al. 2016

IEEE Internet Things J.

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We develop a new location spoofing detection algorithm for geo-spatial tagging and location-based services in the Internet of Things (IoT), called Enhanced Location Spoofing Detection using Audibility (ELSA) which can be implemented at the backend server without modifying existing legacy IoT systems. ELSA is based on a statistical decision theory framework and uses two-way time-of-arrival (TW-TOA) information between the user's device and the anchors. In addition to the TW-TOA information, ELSA exploits the implicit available audibility information to improve detection rates of location spoofing attacks. Given TW-TOA and audibility information, we derive the decision rule for the verification of the device's location, based on the generalized likelihood ratio test. We develop a practical threat model for delay measurements spoofing scenarios, and investigate in detail the performance of ELSA in terms of detection and false alarm rates. Our extensive simulation results on both synthetic and real-world datasets demonstrate the superior performance of ELSA compared to conventional non-audibility-aware approaches.

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“…In the case of GPS positioning, there are certain measures of uncertainty that can be derived from the configuration of satellites above the horizon. Through multilateration (Kennedy 2002, Chiang et al 2012, an individual's three-dimensional location can be determined when a minimum of four satellites are in view of the receiver. As the number of satellites present in the calculation increases, along with the geometry for Geometric Dilution of Precision (GDOP), the accuracy in GPS positioning improves.…”

Section: Introductionmentioning

confidence: 99%

Assessing the effectiveness of different visualizations for judgments of positional uncertainty

McKenzie

Hegarty

Barrett

et al. 2015

International Journal of Geographical Information Science

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Many techniques have been proposed for visualizing uncertainty in geospatial data. Previous empirical research on the effectiveness of visualizations of geospatial uncertainty has focused primarily on user intuitions rather than objective measures of performance when reasoning under uncertainty. Framed in the context of Google's blue dot, we examined the effectiveness of four alternative visualizations for representing positional uncertainty when reasoning about self-location data. Our task presents a mobile mapping scenario in which GPS satellite location readings produce location estimates with varying levels of uncertainty. Given a known location and two smartphone estimates of that known location, participants were asked to judge which smartphone produces the better location reading, taking uncertainty into account. We produced visualizations that vary by glyph type (uniform blue circle with border vs. Gaussian fade) and visibility of a centroid dot (visible vs. not visible) to produce the four visualization formats. Participants viewing the uniform blue circle are most likely to respond in accordance with the actual probability density of points sampled from bivariate normal distributions and additionally respond most rapidly. Participants reported a number of simple heuristics on which they based their judgments, and consistency with these heuristics was highly predictive of their judgments.

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Secure Location Verification Using Simultaneous Multilateration

Cited by 34 publications

References 6 publications

An Efficient Location Verification Scheme for Static Wireless Sensor Networks

An Efficient Location Verification Scheme for Static Wireless Sensor Networks

Geo-Spatial Location Spoofing Detection for Internet of Things

Assessing the effectiveness of different visualizations for judgments of positional uncertainty

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