iTPS: an improved location discovery scheme for sensor networks with long-range beacons

Thaeler, Andrew; Deng, Min; Cheng, Xiuzhen

doi:10.1016/j.jpdc.2004.09.002

Cited by 39 publications

(18 citation statements)

References 11 publications

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“…Although a number of localization protocols (e.g., [4,[7][8][9]) have been proposed [3], security has mostly been overlooked. As pinpointed in the study by [10,11], these protocols typically employ the minimum mean square error estimate (MMSEE) to gauge sensor positions by somehow solving mathematical optimization problems with certain constraints, while potential attacks from malicious adversaries are not accounted.…”

Section: Related Workmentioning

confidence: 99%

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Secure localization with attack detection in wireless sensor networks

Zhu

Xiang

Zhou

et al. 2011

Int. J. Inf. Secur.

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Rapid technological advances have enabled the development of low-cost sensor networks for various monitoring tasks, where it is important to estimate the positions of a number of regular sensor nodes whose locations cannot be known apriori. We address the problem of localizing the regular nodes with range-based location references obtained from certain anchor nodes referred to as beacons, particularly in an adverse environment where some of the beacons may be compromised. We propose an innovative modular solution featuring two lightweight modules that are for dedicated functionalities, respectively, but can also be closely integrated. First, we harness simple geometric triangular rules and an efficient voting technique to enable the attack detection module, which identifies and filters out malicious location references. We then develop a secure localization module that computes and clusters certain reference points, and the position of the concerned regular node is estimated with the centroid of the most valuable reference points identified. Extensive simulations show that our attack detection module can detect compromised beacons effectively, and the secure localization module can subsequently provide a dependable localization service in terms of bounded estimation error. The integrated system turns out to be tolerant of malicious attacks even in highly challenging scenarios.

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Section: Related Workmentioning

confidence: 99%

“…1). A localization scheme (e.g., [4,[7][8][9]) is to allow the nodes to estimate their physical locations, which essentially relies on the anchor positions that are obtained and announced by the beacons. It usually works as follows.…”

Section: Sensors: Beacons and Regular Nodesmentioning

confidence: 99%

Secure localization with attack detection in wireless sensor networks

Zhu

Xiang

Zhou

et al. 2011

Int. J. Inf. Secur.

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“…All the above techniques rely on measured signal strengths to infer the location of the signal source. The localization task in wireless network is accomplished using Time Difference-of-Arrival (TDOA) methods [Savvides et al 2001;Cheng et al 2004;Thaeler et al 2005] that measure differences in signal arrival times at different sensors, and Angle-of-Arrival (AoA) methods [Niculescu and Nath 2003] that measure the signal incident angles at different sensors. Unlike the signal strength-based methods, the TDOA methods require accurate time synchronization among the sensors, whereas AoA requires directional antenna for its operation.…”

Section: :5mentioning

confidence: 99%

Identification of low-level point radioactive sources using a sensor network

Chin

Rao

Yau

et al. 2010

ACM Trans. Sen. Netw.

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Identification of a low-level point radioactive source amidst background radiation is achieved by a network of radiation sensors using a two-step approach. Based on measurements from three or more sensors, a geometric difference triangulation method or an N-sensor localization method is used to estimate the location and strength of the source. Then a sequential probability ratio test based on current measurements and estimated parameters is employed to finally decide: (1) the presence of a source with the estimated parameters, or (2) the absence of the source, or (3) the Author's address: J.-C. Chin: email: jcchin@cs.purdue.edu. c 2010 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by a contractor or affiliate of the U.S. Government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 ( insufficiency of measurements to make a decision. This method achieves specified levels of false alarm and missed detection probabilities, while ensuring a close-to-minimal number of measurements for reaching a decision. This method minimizes the ghost-source problem of current estimation methods, and achieves a lower false alarm rate compared with current detection methods. This method is tested and demonstrated using: (1) simulations, and (2) a test-bed that utilizes the scaling properties of point radioactive sources to emulate high intensity ones that cannot be easily and safely handled in laboratory experiments.

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“…We assume that sensors can obtain their own geometric coordinates (S x , S y ) using GPS or other techniques, such as those proposed in [1,6,12]. We further assume that a robust broadcasting protocol is in place such that information can be properly disseminated.…”

Section: Overview Of Location-centric Storagementioning

confidence: 99%

Location-centric storage and query in wireless sensor networks

Xing

Cheng

et al. 2009

Wireless Netw

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Location-centric storage (LCS) is envisioned as a promising scheme for robust and user-friendly ondemand data storage in networking environments such as the roadway sensor networks [Xing K et al. J Parallel Distributed Comput 67:336-345, 2007; Xing K et al. in: IEEE wireless communication and networking conference (WCNC), 2005].In this paper, we analyze the performance of LCS in terms of storage and query overheads. This study indicates that LCS utilizes network resource efficiently and achieves good scalability. In particular, the storage overhead of sensors is independent of the network size, and is evenly distributed across the network. We also propose two algorithms for data retrieval in LCS-enabled one-dimensional and two-dimensional sensor networks. Our algorithms guarantee that acquiring the stored data of any event only takes a small number of communication hops to query a small number of sensors.

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iTPS: an improved location discovery scheme for sensor networks with long-range beacons

Cited by 39 publications

References 11 publications

Secure localization with attack detection in wireless sensor networks

Secure localization with attack detection in wireless sensor networks

Identification of low-level point radioactive sources using a sensor network

Location-centric storage and query in wireless sensor networks

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