Difference between revisions of "Kiyavash2007"
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|author=N. Kiyavash and F. Koushanfar | |author=N. Kiyavash and F. Koushanfar | ||
|abstract=Sensor networks are highly susceptible to errors and malicious attacks. A host of nefarious attacks are targeted at preventing nodes from discovering their correct positions. In this work, we present a novel framework for position estimation in presence of malicious attacks on distance measurements of sensor networks. Additionally, we propose a practical randomized algorithm in the framework, which efficiently detects and rejects the corrupted measurements. The algorithm searches for an agreeable solution starting from randomly sampled minimal subsets of data; it subsequently enhances its estimate by augmenting consistent data points to the best random sample. The performance of the proposed algorithm is evaluated and compared to state-of-the-art robust positioning algorithms, both for independent and colluding attackers. While our method performs the same or better compared with the other algorithms on independent attacks, it is significantly more robust against collusion attacks, in terms of both the position estimation error and attack diagnosis and isolation. Moreover, the algorithm has a shorter runtime due to its randomized nature. | |abstract=Sensor networks are highly susceptible to errors and malicious attacks. A host of nefarious attacks are targeted at preventing nodes from discovering their correct positions. In this work, we present a novel framework for position estimation in presence of malicious attacks on distance measurements of sensor networks. Additionally, we propose a practical randomized algorithm in the framework, which efficiently detects and rejects the corrupted measurements. The algorithm searches for an agreeable solution starting from randomly sampled minimal subsets of data; it subsequently enhances its estimate by augmenting consistent data points to the best random sample. The performance of the proposed algorithm is evaluated and compared to state-of-the-art robust positioning algorithms, both for independent and colluding attackers. While our method performs the same or better compared with the other algorithms on independent attacks, it is significantly more robust against collusion attacks, in terms of both the position estimation error and attack diagnosis and isolation. Moreover, the algorithm has a shorter runtime due to its randomized nature. | ||
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|year=2007 | |||
|booktitle=IEEE Mobile Ad-hoc and Sensor Systems (MASS) | |booktitle=IEEE Mobile Ad-hoc and Sensor Systems (MASS) | ||
|title=Anti-Collusion Position Estimation in Wireless Sensor Networks | |title=Anti-Collusion Position Estimation in Wireless Sensor Networks | ||
|entry=inproceedings | |entry=inproceedings | ||
}} | }} | ||
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| author | N. Kiyavash and F. Koushanfar |
| booktitle | IEEE Mobile Ad-hoc and Sensor Systems (MASS) |
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| title | Anti-Collusion Position Estimation in Wireless Sensor Networks |
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| year | 2007 |
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Email:
farinaz@ucsd.edu
farinaz@ucsd.edu
Address:
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Lab Location: EBU1-2514
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093