A general approach to d-dimensional geometric queries
STOC '85 Proceedings of the seventeenth annual ACM symposium on Theory of computing
Discrepancy and &egr; approximations for bounded VC-dimension
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
On linear-time deterministic algorithms for optimization problems in fixed dimension
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Denial of Service in Sensor Networks
Computer
On Range Searching with Semialgebraic Sets
MFCS '92 Proceedings of the 17th International Symposium on Mathematical Foundations of Computer Science
Fast and Robust Smallest Enclosing Balls
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Network failure detection and graph connectivity
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Convex Optimization
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
Topological hole detection in wireless sensor networks and its applications
DIALM-POMC '05 Proceedings of the 2005 joint workshop on Foundations of mobile computing
Ef.cient Continuous Mapping in Sensor Networks Using Isolines
MOBIQUITOUS '05 Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
Proceedings of the 3rd international conference on Embedded networked sensor systems
Deterministic boundary recognition and topology extraction for large sensor networks
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Contour map matching for event detection in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Detecting cuts in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Boundary recognition in sensor networks by topological methods
Proceedings of the 12th annual international conference on Mobile computing and networking
Approximate isocontours and spatial summaries for sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Beyond average: toward sophisticated sensing with queries
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Contour approximation in sensor networks
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
An effective coreset compression algorithm for large scale sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
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We propose a scalably efficient scheme for detecting large-scale physically correlated events in sensor networks. Specifically, we show that in a network of n sensors arbitrarily distributed in the plane, a sample of O(1/&epsis; log 1/&epsis;) sensor nodes (mice) is sufficient to catch any, and only those, events that affect Ω (&epsis;n) nodes (elephants), for any 0 geometry of the event has a bounded Vapnik-Chervonenkis (VC) dimension. In fact, the scheme is provably able to estimate the size of an event within the approximation error of ±&epsis;n/4, which can be improved further at the expense of more mice. The detection algorithm itself requires knowledge of the event geometry (e.g., circle, ellipse, or rectangle) for the sake of computational efficiency, but the combinatorial bound on the sample size (set of mice) depends only on the VC, dimension of the event class and not the precise shape geometry. While nearly optimal in theory, due to implicit constant factors, these “scale-free” bounds still prove too large in practice if applied blindly. We therefore propose heuristic improvements and perform empirical parameter tuning to counter the pessimism inherent in these theoretical estimates. Using a variety of data distributions and event geometries, we show through simulations that the final scheme is eminently scalable and practical, say, for n ≥ 1000. The overall simplicity and generality of our technique suggests that it is well suited for a wide class of sensornet applications, including monitoring of physical environments, network anomalies, network security, or any abstract binary event that affects a significant number of nodes in the network.