Secure Aggregation for Wireless Networks
SAINT-W '03 Proceedings of the 2003 Symposium on Applications and the Internet Workshops (SAINT'03 Workshops)
SIA: secure information aggregation in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Resilient aggregation in sensor networks
Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks
Medians and beyond: new aggregation techniques for sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Quantifying eavesdropping vulnerability in sensor networks
DMSN '05 Proceedings of the 2nd international workshop on Data management for sensor networks
Resilient Aggregation with Attack Detection in Sensor Networks
PERCOMW '06 Proceedings of the 4th annual IEEE international conference on Pervasive Computing and Communications Workshops
Robust statistical methods for securing wireless localization in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
A collaborative approach to in-place sensor calibration
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Articulated motion segmentation using RANSAC with priors
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Cora: correlation-based resilient aggregation in sensor networks
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
A secure and resilient WSN roadside architecture for intelligent transport systems
WiSec '08 Proceedings of the first ACM conference on Wireless network security
Relaxed authenticity for data aggregation in wireless sensor networks
Proceedings of the 4th international conference on Security and privacy in communication netowrks
Securely computing an approximate median in wireless sensor networks
Proceedings of the 4th international conference on Security and privacy in communication netowrks
FAIR: fuzzy-based aggregation providing in-network resilience for real-time wireless sensor networks
Proceedings of the second ACM conference on Wireless network security
Uncertainty-aware Wireless Sensor Networks
International Journal of Mobile Communications
Efficient and provably secure aggregation of encrypted data in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
CORA: Correlation-based resilient aggregation in sensor networks
Ad Hoc Networks
Secure median computation in wireless sensor networks
Ad Hoc Networks
Design of large-scale agricultural wireless sensor networks: email from the vineyard
International Journal of Sensor Networks
Location privacy and resilience in wireless sensor networks querying
Computer Communications
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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.