Spatio-temporal correlation: theory and applications for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
Gathering correlated data in sensor networks
Proceedings of the 2004 joint workshop on Foundations of mobile computing
Resilient aggregation in sensor networks
Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks
Efficient gathering of correlated data in sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
A Scalable Correlation Aware Aggregation Strategy for Wireless Sensor Networks
WICON '05 Proceedings of the First International Conference on Wireless Internet
Proceedings of the 14th ACM international conference on Information and knowledge management
Resilient Aggregation with Attack Detection in Sensor Networks
PERCOMW '06 Proceedings of the 4th annual IEEE international conference on Pervasive Computing and Communications Workshops
RANBAR: RANSAC-based resilient aggregation in sensor networks
Proceedings of the fourth ACM workshop on Security of ad hoc and sensor networks
Spatial correlation-based collaborative medium access control in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
CORA: Correlation-based resilient aggregation in sensor networks
Ad Hoc Networks
Wireless Personal Communications: An International Journal
Distributed faulty sensor detection
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Hi-index | 0.02 |
In this paper we consider the problem of resilient data aggregation, namely, when aggregation has to be performed on a compromised sample. We present a statistical framework that is designed to mitigate the effects of an attacker who is able to alter the values of the measured parameters of the environment around some of the sensor nodes. Our proposed framework takes advantage of the naturally existing correlation between the sample elements, which is very rarely considered in other sensor network related papers. The algorithms presented are to be applied without assumption on the sensor network's sampling distribution or on the behaviour of the attacker. The effectiveness of the algorithms is formally evaluated.