The P2 algorithm for dynamic calculation of quantiles and histograms without storing observations
Communications of the ACM
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Approximate medians and other quantiles in one pass and with limited memory
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Space-efficient online computation of quantile summaries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
COUGAR: the network is the database
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
SPINS: security protocols for sensor networks
Wireless Networks
LEAP: efficient security mechanisms for large-scale distributed sensor networks
Proceedings of the 10th ACM conference on Computer and communications security
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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
Synopsis diffusion for robust aggregation in sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Power-conserving computation of order-statistics over sensor networks
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
SDAP: a secure hop-by-Hop data aggregation protocol for sensor networks
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Attack-resilient hierarchical data aggregation in sensor networks
Proceedings of the fourth ACM workshop on Security of ad hoc and sensor networks
RANBAR: RANSAC-based resilient aggregation in sensor networks
Proceedings of the fourth ACM workshop on Security of ad hoc and sensor networks
Secure hierarchical in-network aggregation in sensor networks
Proceedings of the 13th ACM conference on Computer and communications security
A note on efficient aggregate queries in sensor networks
Theoretical Computer Science
An efficient integrity-preserving scheme for hierarchical sensor aggregation
WiSec '08 Proceedings of the first ACM conference on Wireless network security
Secure median computation in wireless sensor networks
Ad Hoc Networks
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Wireless Sensor Networks (WSNs) have proven to be useful in many applications, such as military surveillance and environment monitoring. To meet the severe energy constraints in WSNs, some researchers have proposed to use the in-network data aggregation technique (i.e., combining partial results at intermediate nodes during message routing), which significantly reduces the communication overhead. Given the lack of hardware support for tamper resistance and the unattended nature of sensor nodes, sensor network protocols need to be designed with security in mind. Recently, researchers proposed algorithms for securely computing a few aggregates, such as Sum (the sum of the sensed values), Count (number of nodes) and Average. However, to the best of our knowledge, there is no prior work which securely computes the Median, although the Median is considered to be an important aggregate. The contribution of this paper is twofold. We first propose a protocol to compute an approximate Median and verify if it has been falsified by an adversary. Then, we design an attack-resilient algorithm to compute the Median even in the presence of a few compromised nodes. We evaluate the performance and cost of our approach via both analysis and simulation. Our results show that our approach is scalable and efficient.