RANBAR: RANSAC-based resilient aggregation in sensor networks

Levente Buttyán, Péter Schaffer, István Vajda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Citations (Scopus)

Abstract

We present a novel outlier elimination technique designed for sensor networks. This technique is called RANBAR and it is based on the RANSAC (RANdom SAmple Consensus) paradigm, which is well-known in computer vision and in automated cartography. The RANSAC paradigm gives us a hint on how to instantiate a model if there are a lot of compromised data elements.However,the paradigm does not specify an algorithm and it uses a guess for the number of compromised elements, which is not known in general in real life environments. We developed the RANBAR algorithm following this paradigm and we eliminated the need for the guess. Our RANBAR algorithm is therefore capable to handle a high percent of outlier measurement data by leaning on only one preassumption,namely that the sample is i.i.d. in the unattacked case. We implemented the algorithm in a simulation environment and we used it to filter out outlier elements from a sample before an aggregation procedure. The aggregation function that we used was the average. We show that the algorithm guarantees a small distortion on the output of the aggregator even if almost half of the sample is compromised. Compared to other resilient aggregation algorithms, like the trimmed average and the median, our RANBAR algorithm results in smaller distortion, especially for high attack strengths.

Original languageEnglish
Title of host publicationProceedings of the Fourth ACM Workshop on Security of ad hoc and Sensor Networks, SASN 2006. A workshop held in conjuction with the 13th ACM Conference on Computer and Communications Security, CCS'06
Pages83-90
Number of pages8
DOIs
Publication statusPublished - Dec 1 2006
Event4th ACM Workshop on Security of ad hoc and Sensor Networks, SASN 2006. A workshop held in conjuction with the 13th ACM Conference on Computer and Communications Security, CCS'06 - Alexandria, VA, United States
Duration: Oct 30 2006Oct 30 2006

Publication series

NameProceedings of the Fourth ACM Workshop on Security of ad hoc and Sensor Networks, SASN 2006. A workshop held in conjuction with the 13th ACM Conference on Computer and Communications Security, CCS'06

Other

Other4th ACM Workshop on Security of ad hoc and Sensor Networks, SASN 2006. A workshop held in conjuction with the 13th ACM Conference on Computer and Communications Security, CCS'06
CountryUnited States
CityAlexandria, VA
Period10/30/0610/30/06

Keywords

  • Outlier elimination
  • Random sample consensus
  • Resilient aggregation
  • Sensor networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Buttyán, L., Schaffer, P., & Vajda, I. (2006). RANBAR: RANSAC-based resilient aggregation in sensor networks. In Proceedings of the Fourth ACM Workshop on Security of ad hoc and Sensor Networks, SASN 2006. A workshop held in conjuction with the 13th ACM Conference on Computer and Communications Security, CCS'06 (pp. 83-90). (Proceedings of the Fourth ACM Workshop on Security of ad hoc and Sensor Networks, SASN 2006. A workshop held in conjuction with the 13th ACM Conference on Computer and Communications Security, CCS'06). https://doi.org/10.1145/1180345.1180356